This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate ...This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.展开更多
The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication e...The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.展开更多
Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity...Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity and mortality rates than the past two decades. Lack of spatiotemporal-based technology and variability assessment tools in Malawi’s Cholera monitoring and management, limit our understanding of the disease’s epidemiology. The present work developed a spatiotemporal variability model for Cholera disease at district level and its relationship to socioeconomic and climatic factors based on cumulative confirmed Cholera cases in Malawi from March 2022 to July 2023 using Z-score statistic and multiscale geographically weighted regression (MGWR) in a Geographical Information System (GIS). We found out that socioeconomic factors such as access to safe drinking water, population density and poverty level, and climatic factors including temperature and rainfall strongly influenced Cholera prevalence in a complex and multifaceted manner. The model shows that Lilongwe, Mangochi, Blantyre and Balaka districts were highly vulnerable to Cholera disease followed by lakeshore districts of Salima, Nkhotakota, Nkhata-Bay and Karonga than other districts. We recommend strategic measures such as Water, Sanitation, and Hygiene (WASH) interventions, community awareness on proper water storage, Cholera case management, vaccination campaigns and spatial-based surveillance systems in the most affected districts. This research has shown that MGWR, as a surveillance system, has the potential of providing insights on the disease’s spatial patterns for public health authorities to identify high-risk districts and implement early response interventions to reduce the spread of the disease.展开更多
About 23%of the surface area and 44%of the volume of all the lakes are occupied by saline lakes in the world.Importantly,agricultural diversion,illegal encroachment,pollution,and invasive species could cause these lak...About 23%of the surface area and 44%of the volume of all the lakes are occupied by saline lakes in the world.Importantly,agricultural diversion,illegal encroachment,pollution,and invasive species could cause these lakes to dry up completely or partially by 2025.Illegal saltpan encroachment is causing Sambhar,India’s largest saline lake,to shrink by 4.23%every decade.This study aims to characterize the soil parameters where halophytes are growing.A literature survey was conducted for halophytes and soil characteristics.The study area was divided into four zones for stratified random sampling.Soil sampling was conducted in February 2021.The soil indicators for halophyte selected were pH,electrical conductivity,moisture,salinity,organic carbon,and organic matter.The obtained results were interpolated in the geospatial platform for soil characteristic mapping.It is found that no research is conducted on halophytes of the lake.Studies on soil are also inconsistent and only six common parameters could be identified.Results show that the pH ranged 9.37-7.66,electrical conductivity was 16.1-0.38,moisture 23.37%-1.2%,organic carbon 3.29%-0.15%,organic matter 5.6%-0.2%,and salinity 8.86%-0.72%.Though these results show improved condition as compared to last few years,in long term,the lake is desiccating.During the UN Decade of Ecosystem Restoration(2021-2030),if these causes are not addressed,the ecosystem may completely dry up.展开更多
Check dams are widely used on the Loess Plateau in China to control soil and water losses,develop agricultural land,and improve watershed ecology.Detailed information on the number and spatial distribution of check da...Check dams are widely used on the Loess Plateau in China to control soil and water losses,develop agricultural land,and improve watershed ecology.Detailed information on the number and spatial distribution of check dams is critical for quantitatively evaluating hydrological and ecological effects and planning the construction of new dams.Thus,this study developed a check dam detection framework for broad areas from high-resolution remote sensing images using an ensemble approach of deep learning and geospatial analysis.First,we made a sample dataset of check dams using GaoFen-2(GF-2)and Google Earth images.Next,we evaluated five popular deep-learning-based object detectors,including Faster R-CNN,You Only Look Once(version 3)(YOLOv3),Cascade R-CNN,YOLOX,and VarifocalNet(VFNet),to identify the best one for check dam detection.Finally,we analyzed the location characteristics of the check dams and used geographical constraints to optimize the detection results.Precision,recall,average precision at intersection over union(IoU)threshold of 0.50(AP_(50)),IoU threshold of 0.75(AP_(75)),and average value for 10 IoU thresholds ranging from 0.50-0.95 with a 0.05 step(AP_(50-95)),and inference time were used to evaluate model performance.All the five deep learning networks could identify check dams quickly and accurately,with AP_(50-95),AP_(50),and AP_(75)values higher than 60.0%,90.0%,and 70.0%,respectively,except for YOLOv3.The VFNet had the best performance,followed by YOLOX.The proposed framework was tested in the Yanhe River Basin and yielded promising results,with a recall rate of 87.0%for 521 check dams.Furthermore,the geographic analysis deleted about 50%of the false detection boxes,increasing the identification accuracy of check dams from 78.6%to 87.6%.Simultaneously,this framework recognized 568 recently constructed check dams and small check dams not recorded in the known check dam survey datasets.The extraction results will support efficient watershed management and guide future studies on soil erosion in the Loess Plateau.展开更多
Growing attention has been directed to the use of satellite imagery and open geospatial data to understand large-scale sustainable development outcomes.Health and education are critical domains of the Unites Nations’...Growing attention has been directed to the use of satellite imagery and open geospatial data to understand large-scale sustainable development outcomes.Health and education are critical domains of the Unites Nations’Sus-tainable Development Goals(SDGs),yet existing research on the accessibility of corresponding services focused mainly on detailed but small-scale studies.This means that such studies lack accessibility metrics for large-scale quantitative evaluations.To address this deficiency,we evaluated the accessibility of health and education ser-vices in China's Mainland in 2021 using point-of-interest data,OpenStreetMap road data,land cover data,and WorldPop spatial demographic data.The accessibility metrics used were the least time costs of reaching hospital and school services and population coverage with a time cost of less than 1 h.On the basis of the road network and land cover information,the overall average time costs of reaching hospital and school were 20 and 22 min,respectively.In terms of population coverage,94.7%and 92.5%of the population in China has a time cost of less than 1 h in obtaining hospital and school services,respectively.Counties with low accessibility to hospitals and schools were highly coupled with poor areas and ecological function regions,with the time cost incurred in these areas being more than twice that experienced in non-poor and non-ecological areas.Furthermore,the cumulative time cost incurred by the bottom 20%of counties(by GDP)from access to hospital and school services reached approximately 80%of the national total.Low-GDP counties were compelled to suffer disproportionately increased time costs to acquire health and education services compared with high-GDP counties.The accessibil-ity metrics proposed in this study are highly related to SDGs 3 and 4,and they can serve as auxiliary data that can be used to enhance the evaluation of SDG outcomes.The analysis of the uneven distribution of health and education services in China can help identify areas with backward public services and may contribute to targeted and efficient policy interventions.展开更多
In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management to...In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management tool to identify and model the vulnerabilities of Malawi’s districts in prioritizing health services as far as coronavirus prevalence and other infectious diseases are concerned. The aim of this study was to model coronavirus vulnerability in all districts in Malawi using Geographic Information System (GIS) to monitor the disease’s cumulative prevalence over the severely affected period between 2020 and 2021. To achieve this, four parameters associated with coronavirus prevalence, including population density, percentage of older people, temperature, and humidity, were prepared in a GIS environment and used in the modelling process. A multiscale geographically weighted regression (MGWR) model was used to model and determine the vulnerability of coronavirus in Malawi. In the MGWR modelling, the Fixed Spatial Kernel was used following a Gaussian distribution model type. The Results indicated that population density and older people (age greater than 60 years) have a more significant impact on coronavirus prevalence in Malawi. The modelling further shows that Malawi, between April 2020 and May 2021, Lilongwe, Blantyre and Thyolo were more vulnerable to coronavirus than other districts. This research has shown that spatial variability of Covid-19 cases using MGWR has the potential of providing useful insights to policymakers for targeted interventions that could otherwise not be possible to detect using non-geovisualization techniques.展开更多
Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, res...Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, resulting in reduced harvests and sometimes losses for farmers. Better availability of information such as weather patterns, suitable crops, nutrient requirements based on soil types and conditions would greatly alleviate these challenges. While geospatial information is being developed and improved continuously by researchers, its accessibility and use by the counties has not been established and cannot be identified as contributing to better crop production outcomes. The aim of this study, therefore, was to assess the awareness and status of geospatial data availability and use for crop production, and the level of the relevant capacities, both human and infrastructural, in selected Counties of Kenya. A survey was conducted in the four counties of Vihiga, Kilifi, Wajir and Nyeri and key informant interviews were conducted with both management and technical County Agricultural Officers, as well as sub-county agricultural extension officers. From the results of the survey, out of the four counties, only one has adequate infrastructure in terms of hard-ware, software and connectivity to conduct useful geospatial data acquisition and processing. While most indicated awareness of the existence of geospatial data, limited resources, low skills and knowledge have restricted any meaningful sourcing of and access to data, with only 38% moderately or highly skilled in acquisition, 48% in processing and 57% in interpretation and use of geospatial data. The study concludes that moderate skills and capacities available within the counties have considerable potential to make use the available geospatial data to inform farmers accordingly and improve their farming outcomes.展开更多
According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food con...According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food consumed there;their farming activities are therefore critical to the economies of their countries and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions;but even when they are on registered land, the fear of losing such land that they should default on loan payments often prevents them from applying for farm credit;and even if they apply, they still get disadvantaged by low credit scores (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and good seeds among others. This depresses their yields, and in turn, has negative implications for the food security in their communities, and in the world, hence making it difficult for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used and the results showed that over 40% of the farmers could not qualify for credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing credit, thus enabling such a farmer to be better evaluated for credit on the basis of the health of their crop, rather than on a traditional form of collateral.展开更多
The balanced development of the elementary education sector has been a long goal pursued by the education departments of various places, and is also an outcome expected by the people. Based on a study of the equilibri...The balanced development of the elementary education sector has been a long goal pursued by the education departments of various places, and is also an outcome expected by the people. Based on a study of the equilibrium of the spatial distribution of the capacity saturation models of all the primary schools in Changsha’s (China) five core districts, the results show that the overall geographical distribution of the primary schools in Changsha is relatively balanced, based on the natural characteristics of Changsha, such as human geography, and the moderate gradient between the central urban area and the primary schools in the suburbs and outer suburbs. Then the Theil index model was introduced, and the results of the model analysis show that the differences between elementary schools in Changsha urban area are relatively small, and the main differences originate from between districts rather than within districts, and subtle differences among regions mainly reflect in the teacher strength.展开更多
Although big data are widely used in various fields,its application is still rare in the study of mining subsidence prediction(MSP)caused by underground mining.Traditional research in MSP has the problem of oversimpli...Although big data are widely used in various fields,its application is still rare in the study of mining subsidence prediction(MSP)caused by underground mining.Traditional research in MSP has the problem of oversimplifying geological mining conditions,ignoring the fluctuation of rock layers with space.In the context of geospatial big data,a data-intensive FLAC3D(Fast Lagrangian Analysis of a Continua in 3 Dimensions)model is proposed in this paper based on borehole logs.In the modeling process,we developed a method to handle geospatial big data and were able to make full use of borehole logs.The effectiveness of the proposed method was verified by comparing the results of the traditional method,proposed method,and field observation.The findings show that the proposed method has obvious advantages over the traditional prediction results.The relative error of the maximum surface subsidence predicted by the proposed method decreased by 93.7%and the standard deviation of the prediction results(which was 70 points)decreased by 39.4%,on average.The data-intensive modeling method is of great significance for improving the accuracy of mining subsidence predictions.展开更多
Based on the developing tendency of present China’s basic GIS,this paper discusses the designing idea for scales of 1∶10 000,1∶50 000, 1∶250 000 and 1∶1 000 000 pyramid_like multi_layer and multi_resolution of th...Based on the developing tendency of present China’s basic GIS,this paper discusses the designing idea for scales of 1∶10 000,1∶50 000, 1∶250 000 and 1∶1 000 000 pyramid_like multi_layer and multi_resolution of the basic GIS.A technical line for the construction of basic GIS of the whole country and various provinces for sustainable development is put forward.And some important theoretical GIS issues touched by the technical process are discussed.展开更多
Inserting Groundwater quality variability and sources potentially contributing to aquifer recharge was evaluated in metropolitan Karachi. Selected sampling sites were characterized by large waste dumping sites, indust...Inserting Groundwater quality variability and sources potentially contributing to aquifer recharge was evaluated in metropolitan Karachi. Selected sampling sites were characterized by large waste dumping sites, industrial zones, and the presence of open streams receiving heavy loads of industrial and domestic wastes. Levels of pH, electrical conductivity (EC), fluoride (F-), chloride (Cl-), bromide (Br-), nitrate-N (NO-3-N), sulfate (SO2-4), sodium (Na+), potassium (K+), calcium (Ca2+), magnesium (Mg2+), and ammonium (NH+4) were determined and compared with the WHO permissible limits. Concentrations of the measured ions were in the order of Cl- > Na+ > SO2-4 > Mg2+ > Ca2+ > NO-3-N > K+ > F- > Br-. EC values were above the WHO guidelines, representing the presence of high ionic concentration in the groundwater. The health risk index (HRI) for indicated that inhabitants of Karachi are at risk of high exposure. Ingestion of high concentrations of NO-3-N in water can cause methemoglobinemia and birth defects. Results of multivariate statistical analysis, principal component analysis (PCA), cluster analysis (CA), and geographic information system (GIS) map analysis revealed that human activities are leading to adverse effects on the existing groundwater quality in Karachi.展开更多
Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an al...Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus.Limited studies have,however,reported on COVID-19 transmission pattern analysis,and using geography features for prediction of potential outbreak sites.Predicting the next most probable outbreak site is crucial,particularly for optimizing the planning of medical personnel and supply resources.To tackle the challenge,this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude,when would the outbreak likely to happen and the duration of the outbreak.The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia.The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19.Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases,next outbreak location,and the time interval between start dates of two similar sites.Such findings provided valuable insights for policymakers to perform Active Case Detection.展开更多
Geospatial technology is increasingly being used for various applications in environmental management as the need for sustainable development becomes more evident in today’s rapidly-developing world. As a decision to...Geospatial technology is increasingly being used for various applications in environmental management as the need for sustainable development becomes more evident in today’s rapidly-developing world. As a decision tool, Geographic Information system (GIS) and Global positioning System (GPS) can support major decisions dealing with natural phenomena distributed in space and time. Such is the case for land use/cover known to impact ecosystems health in very direct ways. Our study examined one such application in managing land use of some sub-watersheds in the eastern Shore of Maryland, USA. We conducted a 20-year historical land use/cover evaluation using Landsat-TM remotely sensed images and GIS analysis and water monitoring data acquired during the period by Maryland Department of Natural Resources, including sewage discharge of some municipalities in the area. The results not only showed general trends in land use patterns, but also detailed dynamics of land use-land cover classes, impact on water quality, as well as other useful information for guiding both terrestrial and aquatic ecosystems management decisions of the sub-watersheds. The use of this technology for evaluating trends in land use/cover on a decade-by-decade basis is recommended as standard practice for managing ecosystem health on a sustainable basis.展开更多
Development of irrigation infrastructure and its efficient management is the primary concern for sustainable food production. The assessment of irrigation infrastructure creation, its utilization, diagnostic evaluatio...Development of irrigation infrastructure and its efficient management is the primary concern for sustainable food production. The assessment of irrigation infrastructure creation, its utilization, diagnostic evaluation of the various performance indices (monitoring) are important to measure the efficiency. Benchmarking of Irrigation Systems (BIS) is for the diagnostic analysis of irrigation performance indicators comprising of Irrigation Infrastructure System (IIS), Agricultural System (AS), Water Delivery Dynamics (WDD). Since, the performance of an irrigation command varies with space and time, utilization of spatial information technologies viz. Remote Sensing (RS), Geographical Information Systems (GIS), Global Positioning Systems (GPS) useful to provide spatial information on several indices in the process of benchmarking (BM). Information requirements for BIS at different stages, utilization of spatial information technologies to derive irrigation performance indicators was discussed with suitable examples and demonstrated in this study. The studies carried out indicates that the geospatial approach for BIS enabled the improvements in data collection methods, diagnostic analysis, spatio-temporal visualisation of BM indicators at disaggregated canal level which would be useful for decision support during the corrective management measures. The conjunctive use of multi-date (medium resolution) satellite data, high spatial resolution data, field data on water deliveries was found to be an alternative to the conventional non-spatial approaches for BIS and thereby better water resources planning and management.展开更多
Climatic zone is one of the most important units for horticultural planning. Survival and failure of particular land use or farming system in a given region heavily relies on careful assessment of agroclimatic resourc...Climatic zone is one of the most important units for horticultural planning. Survival and failure of particular land use or farming system in a given region heavily relies on careful assessment of agroclimatic resources. Large variety of crops is being grown in Meghalaya traditionally, based on the needs and cultural practices of people without any consideration to the suitability of the climate. This study attempts to make adjustments with the climate based upon scientific knowledge to make the best use of the natural resources so as to recommend more area under horticultural crops. This has been made by identifying existing land use and appropriate land use strategies mainly for horticultural crops for each agro-climatic region. Modern Geospatial technology such as satellite Remote Sensing (RS) and Geographical Information System (GIS) and GPS have been used effectively to provide newer dimensions in identifying suitable sites for various horticultural crops and also for appropriate monitoring and management of land resources in an integrated manner with reference to agro-climatic condition.展开更多
River flooding has become a widely distributed and devastating natural disaster that has caused significant damages both economically and socially in Nigeria. Although, the Nigeria Meteorological Agency (NiMet) are al...River flooding has become a widely distributed and devastating natural disaster that has caused significant damages both economically and socially in Nigeria. Although, the Nigeria Meteorological Agency (NiMet) are always on time to provide flood warnings prior to flood disasters, spatial information pertaining to the areal extent vulnerable to the hazard are not usually made available. This study focuses on the application of geospatial techniques and quantitative research method for the assessment and analysis of flood risk and vulnerability assessment along the River Benue Basin of Kogi State. Digital Elevation Model (DEM) and Landsat Thematic Mapper (TM) images for 2010, 2014 and 2018 were used. These images were processed, digitized, interpolated, classified, overlaid and buffered using the ArcMap 10.3.1 GIS software to generate classified land use/cover maps, topographic map and flood physical vulnerability maps. Household questionnaires which contained structured questions were distributed and obtained to analyse the social-economic vulnerability of the study area using descriptive analysis. The study revealed that areas within 6-kilometre buffer from the River Benue are most vulnerable to flood hazard with the vulnerability decreasing towards the southern part. The two urban areas in the study area (Abejukolo and Oguma) are vulnerable to flood threat. Abejukolo is vulnerable due to high water footprint in the town and its surroundings. The factors that increase flood vulnerability of the inhabitants are low elevation, high illiteracy rate, high numbers of dependent population, poverty, low quality housing, poor land use and planning, inadequacy or absence of risk communication tools. The study recommends the development of guidelines for land use, continuous vulnerability mapping of the study area especially in the flood risk probability months (April to October), construction of dams to contain release water from the upstream, collaboration of NEMA with the local Government and traditional rulers for effective risk communication and relocation of communities lying on the river Benue basin in other to reduce the population vulnerability to flood impacts.展开更多
On the basis of the authors’ experiences of setting up an NGDC Web site,this paper attempts to present some significant aspects about the security of NGDC based on ASP.They include data storing,database maintenance,n...On the basis of the authors’ experiences of setting up an NGDC Web site,this paper attempts to present some significant aspects about the security of NGDC based on ASP.They include data storing,database maintenance,new technical support and so on.Firstly,this paper discusses how to provide the security of data which is saved in the hosts of NGDC.The security model of "Networks_DB Server_DB_DB Object" is also presented.In Windows NT Server,Internet Information Server (i.e.,IIS) is in charge of transferring message and the management of Web sites.ASP is also based on IIS.The advantages of virtual directory technique provided by IIS are emphasized. An NGDC Web site,at the Research Center of GIS in Wuhan Technical University of Surveying and Mapping is also mentioned in this paper.Because it is only an analoge used for case study,the transmission of digital spatial products is not included in the functions in this NGDC Web site.However,the management of spatial metadata is more important and some functions of metadata query are implemented in it.It is illustrated clearly in the functional diagram of the NGDC Web site.展开更多
Earthquake prediction is currently the most crucial task required for the probability,hazard,risk mapping,and mitigation purposes.Earthquake prediction attracts the researchers'attention from both academia and ind...Earthquake prediction is currently the most crucial task required for the probability,hazard,risk mapping,and mitigation purposes.Earthquake prediction attracts the researchers'attention from both academia and industries.Traditionally,the risk assessment approaches have used various traditional and machine learning models.However,deep learning techniques have been rarely tested for earthquake probability mapping.Therefore,this study develops a convolutional neural network(CNN)model for earthquake probability assessment in NE India.Then conducts vulnerability using analytical hierarchy process(AHP),Venn's intersection theory for hazard,and integrated model for risk mapping.A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators.Prediction classification results and intensity variation were then used for probability and hazard mapping,respectively.Finally,earthquake risk map was produced by multiplying hazard,vulnerability,and coping capacity.The vulnerability was prepared by using six vulnerable factors,and the coping capacity was estimated by using the number of hospitals and associated variables,including budget available for disaster management.The CNN model for a probability distribution is a robust technique that provides good accuracy.Results show that CNN is superior to the other algorithms,which completed the classification prediction task with an accuracy of 0.94,precision of 0.98,recall of 0.85,and F1 score of 0.91.These indicators were used for probability mapping,and the total area of hazard(21,412.94 km^(2)),vulnerability(480.98 km^(2)),and risk(34,586.10 km^(2))was estimated.展开更多
文摘This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.
文摘The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.
文摘Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity and mortality rates than the past two decades. Lack of spatiotemporal-based technology and variability assessment tools in Malawi’s Cholera monitoring and management, limit our understanding of the disease’s epidemiology. The present work developed a spatiotemporal variability model for Cholera disease at district level and its relationship to socioeconomic and climatic factors based on cumulative confirmed Cholera cases in Malawi from March 2022 to July 2023 using Z-score statistic and multiscale geographically weighted regression (MGWR) in a Geographical Information System (GIS). We found out that socioeconomic factors such as access to safe drinking water, population density and poverty level, and climatic factors including temperature and rainfall strongly influenced Cholera prevalence in a complex and multifaceted manner. The model shows that Lilongwe, Mangochi, Blantyre and Balaka districts were highly vulnerable to Cholera disease followed by lakeshore districts of Salima, Nkhotakota, Nkhata-Bay and Karonga than other districts. We recommend strategic measures such as Water, Sanitation, and Hygiene (WASH) interventions, community awareness on proper water storage, Cholera case management, vaccination campaigns and spatial-based surveillance systems in the most affected districts. This research has shown that MGWR, as a surveillance system, has the potential of providing insights on the disease’s spatial patterns for public health authorities to identify high-risk districts and implement early response interventions to reduce the spread of the disease.
文摘About 23%of the surface area and 44%of the volume of all the lakes are occupied by saline lakes in the world.Importantly,agricultural diversion,illegal encroachment,pollution,and invasive species could cause these lakes to dry up completely or partially by 2025.Illegal saltpan encroachment is causing Sambhar,India’s largest saline lake,to shrink by 4.23%every decade.This study aims to characterize the soil parameters where halophytes are growing.A literature survey was conducted for halophytes and soil characteristics.The study area was divided into four zones for stratified random sampling.Soil sampling was conducted in February 2021.The soil indicators for halophyte selected were pH,electrical conductivity,moisture,salinity,organic carbon,and organic matter.The obtained results were interpolated in the geospatial platform for soil characteristic mapping.It is found that no research is conducted on halophytes of the lake.Studies on soil are also inconsistent and only six common parameters could be identified.Results show that the pH ranged 9.37-7.66,electrical conductivity was 16.1-0.38,moisture 23.37%-1.2%,organic carbon 3.29%-0.15%,organic matter 5.6%-0.2%,and salinity 8.86%-0.72%.Though these results show improved condition as compared to last few years,in long term,the lake is desiccating.During the UN Decade of Ecosystem Restoration(2021-2030),if these causes are not addressed,the ecosystem may completely dry up.
基金This research was supported by the National Natural Science Foundation of China(41977064)the National Key R&D Program of China(2021YFD1900700).
文摘Check dams are widely used on the Loess Plateau in China to control soil and water losses,develop agricultural land,and improve watershed ecology.Detailed information on the number and spatial distribution of check dams is critical for quantitatively evaluating hydrological and ecological effects and planning the construction of new dams.Thus,this study developed a check dam detection framework for broad areas from high-resolution remote sensing images using an ensemble approach of deep learning and geospatial analysis.First,we made a sample dataset of check dams using GaoFen-2(GF-2)and Google Earth images.Next,we evaluated five popular deep-learning-based object detectors,including Faster R-CNN,You Only Look Once(version 3)(YOLOv3),Cascade R-CNN,YOLOX,and VarifocalNet(VFNet),to identify the best one for check dam detection.Finally,we analyzed the location characteristics of the check dams and used geographical constraints to optimize the detection results.Precision,recall,average precision at intersection over union(IoU)threshold of 0.50(AP_(50)),IoU threshold of 0.75(AP_(75)),and average value for 10 IoU thresholds ranging from 0.50-0.95 with a 0.05 step(AP_(50-95)),and inference time were used to evaluate model performance.All the five deep learning networks could identify check dams quickly and accurately,with AP_(50-95),AP_(50),and AP_(75)values higher than 60.0%,90.0%,and 70.0%,respectively,except for YOLOv3.The VFNet had the best performance,followed by YOLOX.The proposed framework was tested in the Yanhe River Basin and yielded promising results,with a recall rate of 87.0%for 521 check dams.Furthermore,the geographic analysis deleted about 50%of the false detection boxes,increasing the identification accuracy of check dams from 78.6%to 87.6%.Simultaneously,this framework recognized 568 recently constructed check dams and small check dams not recorded in the known check dam survey datasets.The extraction results will support efficient watershed management and guide future studies on soil erosion in the Loess Plateau.
基金This work was supported by the National Natural Science Foundation for Distinguished Young Scholars of China(Grant No.41725006).
文摘Growing attention has been directed to the use of satellite imagery and open geospatial data to understand large-scale sustainable development outcomes.Health and education are critical domains of the Unites Nations’Sus-tainable Development Goals(SDGs),yet existing research on the accessibility of corresponding services focused mainly on detailed but small-scale studies.This means that such studies lack accessibility metrics for large-scale quantitative evaluations.To address this deficiency,we evaluated the accessibility of health and education ser-vices in China's Mainland in 2021 using point-of-interest data,OpenStreetMap road data,land cover data,and WorldPop spatial demographic data.The accessibility metrics used were the least time costs of reaching hospital and school services and population coverage with a time cost of less than 1 h.On the basis of the road network and land cover information,the overall average time costs of reaching hospital and school were 20 and 22 min,respectively.In terms of population coverage,94.7%and 92.5%of the population in China has a time cost of less than 1 h in obtaining hospital and school services,respectively.Counties with low accessibility to hospitals and schools were highly coupled with poor areas and ecological function regions,with the time cost incurred in these areas being more than twice that experienced in non-poor and non-ecological areas.Furthermore,the cumulative time cost incurred by the bottom 20%of counties(by GDP)from access to hospital and school services reached approximately 80%of the national total.Low-GDP counties were compelled to suffer disproportionately increased time costs to acquire health and education services compared with high-GDP counties.The accessibil-ity metrics proposed in this study are highly related to SDGs 3 and 4,and they can serve as auxiliary data that can be used to enhance the evaluation of SDG outcomes.The analysis of the uneven distribution of health and education services in China can help identify areas with backward public services and may contribute to targeted and efficient policy interventions.
文摘In the past two to three years, the world has been heavily affected by the infectious coronavirus disease and Malawi has not been spared due to its interconnection with neighboring countries. There is no management tool to identify and model the vulnerabilities of Malawi’s districts in prioritizing health services as far as coronavirus prevalence and other infectious diseases are concerned. The aim of this study was to model coronavirus vulnerability in all districts in Malawi using Geographic Information System (GIS) to monitor the disease’s cumulative prevalence over the severely affected period between 2020 and 2021. To achieve this, four parameters associated with coronavirus prevalence, including population density, percentage of older people, temperature, and humidity, were prepared in a GIS environment and used in the modelling process. A multiscale geographically weighted regression (MGWR) model was used to model and determine the vulnerability of coronavirus in Malawi. In the MGWR modelling, the Fixed Spatial Kernel was used following a Gaussian distribution model type. The Results indicated that population density and older people (age greater than 60 years) have a more significant impact on coronavirus prevalence in Malawi. The modelling further shows that Malawi, between April 2020 and May 2021, Lilongwe, Blantyre and Thyolo were more vulnerable to coronavirus than other districts. This research has shown that spatial variability of Covid-19 cases using MGWR has the potential of providing useful insights to policymakers for targeted interventions that could otherwise not be possible to detect using non-geovisualization techniques.
文摘Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, resulting in reduced harvests and sometimes losses for farmers. Better availability of information such as weather patterns, suitable crops, nutrient requirements based on soil types and conditions would greatly alleviate these challenges. While geospatial information is being developed and improved continuously by researchers, its accessibility and use by the counties has not been established and cannot be identified as contributing to better crop production outcomes. The aim of this study, therefore, was to assess the awareness and status of geospatial data availability and use for crop production, and the level of the relevant capacities, both human and infrastructural, in selected Counties of Kenya. A survey was conducted in the four counties of Vihiga, Kilifi, Wajir and Nyeri and key informant interviews were conducted with both management and technical County Agricultural Officers, as well as sub-county agricultural extension officers. From the results of the survey, out of the four counties, only one has adequate infrastructure in terms of hard-ware, software and connectivity to conduct useful geospatial data acquisition and processing. While most indicated awareness of the existence of geospatial data, limited resources, low skills and knowledge have restricted any meaningful sourcing of and access to data, with only 38% moderately or highly skilled in acquisition, 48% in processing and 57% in interpretation and use of geospatial data. The study concludes that moderate skills and capacities available within the counties have considerable potential to make use the available geospatial data to inform farmers accordingly and improve their farming outcomes.
文摘According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food consumed there;their farming activities are therefore critical to the economies of their countries and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions;but even when they are on registered land, the fear of losing such land that they should default on loan payments often prevents them from applying for farm credit;and even if they apply, they still get disadvantaged by low credit scores (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and good seeds among others. This depresses their yields, and in turn, has negative implications for the food security in their communities, and in the world, hence making it difficult for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used and the results showed that over 40% of the farmers could not qualify for credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing credit, thus enabling such a farmer to be better evaluated for credit on the basis of the health of their crop, rather than on a traditional form of collateral.
文摘The balanced development of the elementary education sector has been a long goal pursued by the education departments of various places, and is also an outcome expected by the people. Based on a study of the equilibrium of the spatial distribution of the capacity saturation models of all the primary schools in Changsha’s (China) five core districts, the results show that the overall geographical distribution of the primary schools in Changsha is relatively balanced, based on the natural characteristics of Changsha, such as human geography, and the moderate gradient between the central urban area and the primary schools in the suburbs and outer suburbs. Then the Theil index model was introduced, and the results of the model analysis show that the differences between elementary schools in Changsha urban area are relatively small, and the main differences originate from between districts rather than within districts, and subtle differences among regions mainly reflect in the teacher strength.
文摘Although big data are widely used in various fields,its application is still rare in the study of mining subsidence prediction(MSP)caused by underground mining.Traditional research in MSP has the problem of oversimplifying geological mining conditions,ignoring the fluctuation of rock layers with space.In the context of geospatial big data,a data-intensive FLAC3D(Fast Lagrangian Analysis of a Continua in 3 Dimensions)model is proposed in this paper based on borehole logs.In the modeling process,we developed a method to handle geospatial big data and were able to make full use of borehole logs.The effectiveness of the proposed method was verified by comparing the results of the traditional method,proposed method,and field observation.The findings show that the proposed method has obvious advantages over the traditional prediction results.The relative error of the maximum surface subsidence predicted by the proposed method decreased by 93.7%and the standard deviation of the prediction results(which was 70 points)decreased by 39.4%,on average.The data-intensive modeling method is of great significance for improving the accuracy of mining subsidence predictions.
文摘Based on the developing tendency of present China’s basic GIS,this paper discusses the designing idea for scales of 1∶10 000,1∶50 000, 1∶250 000 and 1∶1 000 000 pyramid_like multi_layer and multi_resolution of the basic GIS.A technical line for the construction of basic GIS of the whole country and various provinces for sustainable development is put forward.And some important theoretical GIS issues touched by the technical process are discussed.
文摘Inserting Groundwater quality variability and sources potentially contributing to aquifer recharge was evaluated in metropolitan Karachi. Selected sampling sites were characterized by large waste dumping sites, industrial zones, and the presence of open streams receiving heavy loads of industrial and domestic wastes. Levels of pH, electrical conductivity (EC), fluoride (F-), chloride (Cl-), bromide (Br-), nitrate-N (NO-3-N), sulfate (SO2-4), sodium (Na+), potassium (K+), calcium (Ca2+), magnesium (Mg2+), and ammonium (NH+4) were determined and compared with the WHO permissible limits. Concentrations of the measured ions were in the order of Cl- > Na+ > SO2-4 > Mg2+ > Ca2+ > NO-3-N > K+ > F- > Br-. EC values were above the WHO guidelines, representing the presence of high ionic concentration in the groundwater. The health risk index (HRI) for indicated that inhabitants of Karachi are at risk of high exposure. Ingestion of high concentrations of NO-3-N in water can cause methemoglobinemia and birth defects. Results of multivariate statistical analysis, principal component analysis (PCA), cluster analysis (CA), and geographic information system (GIS) map analysis revealed that human activities are leading to adverse effects on the existing groundwater quality in Karachi.
文摘Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus.Limited studies have,however,reported on COVID-19 transmission pattern analysis,and using geography features for prediction of potential outbreak sites.Predicting the next most probable outbreak site is crucial,particularly for optimizing the planning of medical personnel and supply resources.To tackle the challenge,this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude,when would the outbreak likely to happen and the duration of the outbreak.The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia.The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19.Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases,next outbreak location,and the time interval between start dates of two similar sites.Such findings provided valuable insights for policymakers to perform Active Case Detection.
文摘Geospatial technology is increasingly being used for various applications in environmental management as the need for sustainable development becomes more evident in today’s rapidly-developing world. As a decision tool, Geographic Information system (GIS) and Global positioning System (GPS) can support major decisions dealing with natural phenomena distributed in space and time. Such is the case for land use/cover known to impact ecosystems health in very direct ways. Our study examined one such application in managing land use of some sub-watersheds in the eastern Shore of Maryland, USA. We conducted a 20-year historical land use/cover evaluation using Landsat-TM remotely sensed images and GIS analysis and water monitoring data acquired during the period by Maryland Department of Natural Resources, including sewage discharge of some municipalities in the area. The results not only showed general trends in land use patterns, but also detailed dynamics of land use-land cover classes, impact on water quality, as well as other useful information for guiding both terrestrial and aquatic ecosystems management decisions of the sub-watersheds. The use of this technology for evaluating trends in land use/cover on a decade-by-decade basis is recommended as standard practice for managing ecosystem health on a sustainable basis.
文摘Development of irrigation infrastructure and its efficient management is the primary concern for sustainable food production. The assessment of irrigation infrastructure creation, its utilization, diagnostic evaluation of the various performance indices (monitoring) are important to measure the efficiency. Benchmarking of Irrigation Systems (BIS) is for the diagnostic analysis of irrigation performance indicators comprising of Irrigation Infrastructure System (IIS), Agricultural System (AS), Water Delivery Dynamics (WDD). Since, the performance of an irrigation command varies with space and time, utilization of spatial information technologies viz. Remote Sensing (RS), Geographical Information Systems (GIS), Global Positioning Systems (GPS) useful to provide spatial information on several indices in the process of benchmarking (BM). Information requirements for BIS at different stages, utilization of spatial information technologies to derive irrigation performance indicators was discussed with suitable examples and demonstrated in this study. The studies carried out indicates that the geospatial approach for BIS enabled the improvements in data collection methods, diagnostic analysis, spatio-temporal visualisation of BM indicators at disaggregated canal level which would be useful for decision support during the corrective management measures. The conjunctive use of multi-date (medium resolution) satellite data, high spatial resolution data, field data on water deliveries was found to be an alternative to the conventional non-spatial approaches for BIS and thereby better water resources planning and management.
文摘Climatic zone is one of the most important units for horticultural planning. Survival and failure of particular land use or farming system in a given region heavily relies on careful assessment of agroclimatic resources. Large variety of crops is being grown in Meghalaya traditionally, based on the needs and cultural practices of people without any consideration to the suitability of the climate. This study attempts to make adjustments with the climate based upon scientific knowledge to make the best use of the natural resources so as to recommend more area under horticultural crops. This has been made by identifying existing land use and appropriate land use strategies mainly for horticultural crops for each agro-climatic region. Modern Geospatial technology such as satellite Remote Sensing (RS) and Geographical Information System (GIS) and GPS have been used effectively to provide newer dimensions in identifying suitable sites for various horticultural crops and also for appropriate monitoring and management of land resources in an integrated manner with reference to agro-climatic condition.
文摘River flooding has become a widely distributed and devastating natural disaster that has caused significant damages both economically and socially in Nigeria. Although, the Nigeria Meteorological Agency (NiMet) are always on time to provide flood warnings prior to flood disasters, spatial information pertaining to the areal extent vulnerable to the hazard are not usually made available. This study focuses on the application of geospatial techniques and quantitative research method for the assessment and analysis of flood risk and vulnerability assessment along the River Benue Basin of Kogi State. Digital Elevation Model (DEM) and Landsat Thematic Mapper (TM) images for 2010, 2014 and 2018 were used. These images were processed, digitized, interpolated, classified, overlaid and buffered using the ArcMap 10.3.1 GIS software to generate classified land use/cover maps, topographic map and flood physical vulnerability maps. Household questionnaires which contained structured questions were distributed and obtained to analyse the social-economic vulnerability of the study area using descriptive analysis. The study revealed that areas within 6-kilometre buffer from the River Benue are most vulnerable to flood hazard with the vulnerability decreasing towards the southern part. The two urban areas in the study area (Abejukolo and Oguma) are vulnerable to flood threat. Abejukolo is vulnerable due to high water footprint in the town and its surroundings. The factors that increase flood vulnerability of the inhabitants are low elevation, high illiteracy rate, high numbers of dependent population, poverty, low quality housing, poor land use and planning, inadequacy or absence of risk communication tools. The study recommends the development of guidelines for land use, continuous vulnerability mapping of the study area especially in the flood risk probability months (April to October), construction of dams to contain release water from the upstream, collaboration of NEMA with the local Government and traditional rulers for effective risk communication and relocation of communities lying on the river Benue basin in other to reduce the population vulnerability to flood impacts.
文摘On the basis of the authors’ experiences of setting up an NGDC Web site,this paper attempts to present some significant aspects about the security of NGDC based on ASP.They include data storing,database maintenance,new technical support and so on.Firstly,this paper discusses how to provide the security of data which is saved in the hosts of NGDC.The security model of "Networks_DB Server_DB_DB Object" is also presented.In Windows NT Server,Internet Information Server (i.e.,IIS) is in charge of transferring message and the management of Web sites.ASP is also based on IIS.The advantages of virtual directory technique provided by IIS are emphasized. An NGDC Web site,at the Research Center of GIS in Wuhan Technical University of Surveying and Mapping is also mentioned in this paper.Because it is only an analoge used for case study,the transmission of digital spatial products is not included in the functions in this NGDC Web site.However,the management of spatial metadata is more important and some functions of metadata query are implemented in it.It is illustrated clearly in the functional diagram of the NGDC Web site.
基金fully funded by the Center for Advanced Modeling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydneysupported by Researchers Supporting Project number RSP-2020/14,King Saud University,Riyadh,Saudi Arabia。
文摘Earthquake prediction is currently the most crucial task required for the probability,hazard,risk mapping,and mitigation purposes.Earthquake prediction attracts the researchers'attention from both academia and industries.Traditionally,the risk assessment approaches have used various traditional and machine learning models.However,deep learning techniques have been rarely tested for earthquake probability mapping.Therefore,this study develops a convolutional neural network(CNN)model for earthquake probability assessment in NE India.Then conducts vulnerability using analytical hierarchy process(AHP),Venn's intersection theory for hazard,and integrated model for risk mapping.A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators.Prediction classification results and intensity variation were then used for probability and hazard mapping,respectively.Finally,earthquake risk map was produced by multiplying hazard,vulnerability,and coping capacity.The vulnerability was prepared by using six vulnerable factors,and the coping capacity was estimated by using the number of hospitals and associated variables,including budget available for disaster management.The CNN model for a probability distribution is a robust technique that provides good accuracy.Results show that CNN is superior to the other algorithms,which completed the classification prediction task with an accuracy of 0.94,precision of 0.98,recall of 0.85,and F1 score of 0.91.These indicators were used for probability mapping,and the total area of hazard(21,412.94 km^(2)),vulnerability(480.98 km^(2)),and risk(34,586.10 km^(2))was estimated.
