Endorheic basins(ENBs) are inland drainage basins allowing no outflow to oceans.These basins in the active mountain chains of the convergence zones are under the influence of compressional tectonic activity and climat...Endorheic basins(ENBs) are inland drainage basins allowing no outflow to oceans.These basins in the active mountain chains of the convergence zones are under the influence of compressional tectonic activity and climate condition.The Zagros Mountains of Iran is one of the youngest convergence zones in which continental-continental collision has occurred.In this paper we hypothesize the formation of ENBs among the Zagros range after the epeirogenic stage in the Late Paleogene-Early Neogene.Due to tectonic activity and Quaternary climatic conditions,the ENBs pass the transition stage to exorheic,and still,some tectonic depressions are not linked to the evolutionary process of exorheic drainage of Zagros.The geometry of the drainage network of Kul and Mond basins in Fars arch shows that 67% of their water gaps are located along the thrusts and transverse basement faults in the east and west of the Fars arch.Geometrically,the Kul and Mond basins form triangles with their sides matching with the edges of the Arabian Plate where the major inherited faults of Arabian plate controls the shape of the Zagros basin and a low strain zone along the Razak fault with lower salt tectonic activity,where the wind gaps are created.The ENBs are located in the rainshadow slopes,but the Kul and Mond basins are located in the upwind slopes of rain waves.This factor and the heavy rains of the basin lead to increase of the erosion potential,destruction of depressions,and floods and consequently,the funnel-shaped gaps have a significant impact on the flood flow.展开更多
The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are resp...The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)techniques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model’s result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.展开更多
Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two archite...Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.展开更多
Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it o...Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local communities.We focused on the Bastam watershed where 9.3%of its surface is currently affected by gullying.Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability.However,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors.To cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of Entropy.And,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation stages.Being the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we chose.Such differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns.This is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application.展开更多
The Zemo Imereti Plateau is located in the easternmost part of the limestone region of western Georgia and is Caucasus’ only karst plateau. It is centrally located in a relatively elevated part of the intermountain p...The Zemo Imereti Plateau is located in the easternmost part of the limestone region of western Georgia and is Caucasus’ only karst plateau. It is centrally located in a relatively elevated part of the intermountain plain of the country of Georgia. Lithostratigraphical (petrographic, mineralogical, and XRD) research on terrigenous sediments found in caves in the region was conducted in the Upper Cretaceous limestones located at different hypsometric levels (400 - 700 m). This study focused on allochthonous deposits, which, due to sedimentological features, contains significant and complete information for paleogeographic reconstruction as opposed to the autochthonous sediments. Source provinces of the sediments’ origin were determined using petrographic analysis. Lithological study of the terrigenous sediments indicated their origin from the Racha range, as well as their transportation mechanisms, and sedimentation conditions during deposition. Approximate ages (the end of the Middle Pleistocene and the beginning of the Late Pleistocene) of ancient terrestrial sediments in Rganisklde Cave were achieved by taking into account the geological and geomorphological development of the region. Lithostratigraphical analysis of the cave deposits and modern archaeological data indicate that the formation of the caves in the Zemo Imereti plateau took place mainly during the end of the Middle Pleistocene and in the beginning of the Pleistocene;while on the southern slope of the Racha range and in the surrounding area of Ertso-Tsona (Caucasus southern slope), they formed in the Early Pleistocene.展开更多
The sub-watershed prioritization is the ranking of different areas of a river basin according to their need to proper planning and management of soil and water resources.Decision makers should optimally allocate the i...The sub-watershed prioritization is the ranking of different areas of a river basin according to their need to proper planning and management of soil and water resources.Decision makers should optimally allocate the investments to critical sub-watersheds in an economically effective and technically efficient manner.Hence,this study aimed at developing a user-friendly geographic information system(GIS)tool,Sub-Watershed Prioritization Tool(SWPT),using the Python programming language to decrease any possible uncertainty.It used geospatial-statistical techniques for analyzing morphometric and topohydrological factors and automatically identifying critical and priority sub-watersheds.In order to assess the capability and reliability of the SWPT tool,it was successfully applied in a watershed in the Golestan Province,Northern Iran.Historical records of flood and landslide events indicated that the SWPT correctly recognized critical sub-watersheds.It provided a cost-effective approach for prioritization of sub-watersheds.Therefore,the SWPT is practically applicable and replicable to other regions where gauge data is not available for each sub-watershed.展开更多
Karst groundwater is the major natural resource of drinking water for many countries in the world. Especially in karstic regions, karst water requirements for settlements are provided from karst aquifers. Also, we sho...Karst groundwater is the major natural resource of drinking water for many countries in the world. Especially in karstic regions, karst water requirements for settlements are provided from karst aquifers. Also, we should consider, that karst groundwater is becoming more and more valuable for drinking water supply. Thus, karst groundwater quality and permanent ecological monitoring are very important for populations. Moreover, if we consider that the karst landscape is the extremely sensitive system towards anthropogenic activities, since exaclty the anthropogenic activities largely identify the karst water pollution-turbidity causing factors. This paper presents a new study regarding the quality of the karst groundwater of the study area, which contains important resource of drinking water. In the mentioned study, 12 water samples were collected from different locations of the 4 main karst springs (Krikhula, Dolabistavi, Kidobana and Sakishore) during the spring and summer of 2014 and 2015 years. The main aim was to identify chemical compositions (Ni, Ag, Co, Cd, Zn, Pb, Al, Mg, Fe, F, Cu), and also, it was important to detect Escherichia coli (E. coli). Our research regarding all these chemical compositions shows that all the values are low and under the environmental limit according to the Georgian standards. We measured chemical parameters of all these samples by Atomic Absorption Spectroscopy (AAS) in the chemical laboratory of Ivane Javakhishvili Tbilisi State University, country of Georgia.展开更多
The purpose of this study is to determine the size of air temperature changes with altitude in the mountains of the arid zone, on the example of the Upper Dades valley(High Atlas, Morocco). The air temperature change ...The purpose of this study is to determine the size of air temperature changes with altitude in the mountains of the arid zone, on the example of the Upper Dades valley(High Atlas, Morocco). The air temperature change with altitude was determined on the basis of 5 years data from three meteorological stations. The analysis was carried out on an annual and seasonal basis. The annual and daily variations of thermal gradients between pairs of stations were also determined. It was found that the average thermal gradient in the Upper Dades valley was-1.02℃ per 100 m. The highest values of the thermal gradient occur in winter and the lowest in summer. In winter,the thermal gradient was characterized by the greatest variability. Minima of the daily variation of air temperature gradients were observed in early morning hours and maxima around midday. In the lower part of the valley, air temperature inversion frequently developed between 10 AM and 3 PM UTC.The obtained results show high thermal gradients in the mountains of the arid zone, with their annual amplitude increasing in the lower parts of the valley.The instantaneous values of the gradients were significantly modified by the supply of latent heat and the occurrence of dust storms. It has been shown that the advection factor plays an important role in shaping large gradient values. The study contains novel results of thermal gradient measurements in high mountains of arid zone.展开更多
Pre-crisis management involves the optimal selection of relief and rescue centers to minimize vulnerability.Iran is particularly vulnerable due to its location on the Alpine-Himalaya seismic belt,resulting in an avera...Pre-crisis management involves the optimal selection of relief and rescue centers to minimize vulnerability.Iran is particularly vulnerable due to its location on the Alpine-Himalaya seismic belt,resulting in an average death rate six times higher than the global average during earthquakes.Therefore,selecting appropriate relief and rescue centers is crucial to Iran’s disaster preparedness.When selecting the placement of rescue centers,accessibility and the appropriateness of the land should be taken into account as well as the distance from high-risk areas.The location of these centers does not require any particular combinations.To address this issue,a study was conducted utilizing GIS,artificial neural networks,fuzzy logic,and mathematical models to determine the optimal placement based on 12 indicators within two clusters:natural and human.To examine the information layers of the initial stage,a spatial data repository concerning the variables impacting the placement of these centers was established using ARCGIS.Using functions and algorithms such as Fuzzy Logic in IDRISI,TOPSIS,and VIKOR software,the layers were assessed for weightage before being overlaid.The study’s analysis of the models used revealed that the positioning priority limits of the areas differed across all four models.Notably,the areas with high desirability varied to a greater extent:the fuzzy model varied by 9.3%,neural network by 12.4%,VIKOR by 4.5%,and TOPSIS by 16.2%.The variance in results can be attributed to the differing levels of risk acceptance and non-acceptance in each model.Additionally,the study yielded other significant findings such as the correlation between study area size and model accuracy.Specifically,smaller study areas exhibited higher model accuracy.The research also demonstrated that both fuzzy and VIKOR models achieved greater accuracy.As a result,employing these models in crisis management planning,particularly in pre-crisis management for identifying rescue center locations,would be highly advantageous and increase the precision of these endeavors.展开更多
In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar re...In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.展开更多
Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately use...Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA)based on Deep Belief Network(DBN)with Back Propagation(BP)algorithm optimized by the Genetic Algorithm(GA).For this task,a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE)technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy,root mean square error(RMSE),and area under the receiver operatic characteristic curve(AUC)were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC=0.989)and prediction accuracy(AUC=0.985),and based on the validation dataset it outperforms benchmark models including LR(0.885),LMT(0.934),BLR(0.936),ADT(0.976),NBT(0.974),REPTree(0.811),ANFIS-BAT(0.944),ANFIS-CA(0.921),ANFIS-IWO(0.939),ANFIS-ICA(0.947),and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.展开更多
This article presents the information of the Georgian Caucasus glaciation at the Late Pleistocene and Holocene period (~126,000-11,700 calendar years ago). Our primary aim was to numerically reproduce the ice extent d...This article presents the information of the Georgian Caucasus glaciation at the Late Pleistocene and Holocene period (~126,000-11,700 calendar years ago). Our primary aim was to numerically reproduce the ice extent deduced from geological and geomorphological mapping. We used the analog method with the 30 m resolution SRTM DEM (Shuttle Radar Topography Mission Digital Elevation Model). In addition, the rates of glaciation of those times are identified based on the stadial moraines and erratic boulders. The current investigation has revealed that in the Late Pleistocene, the central and western Caucasus characterized the highest glaciation, while the eastern Caucasus boasted the lowest glaciated area, and in the southern Georgia’s highland glaciation has almost the same form as there is in the Eastern Caucasus today. The longest glaciers were located in the Enguri (Nenskra ~36 km, Mulkhura ~35.1 km, Dolra ~34.5 km), Kodori (Sakeni ~25 km, Klichi ~20 km, Marukhi ~17.3 km) Rioni (Buba ~23 km, Kirtisho ~20.5 km, Jejora ~17.5 km) and Tergi (Devdoraki ~38.5 km, Suatisi ~32 km) river basins. We found, that topography thresholds related to the elevation and hypsometry of individual catchments controlled the gradient of the rate of glacier expansion in the domain at that time.展开更多
The article discusses postglacial landscape transformation in the Northern Hemisphere Middle Pleistocene glaciation area located in the center of the Russian Plain.We attempted to verify the regional paleogeographic m...The article discusses postglacial landscape transformation in the Northern Hemisphere Middle Pleistocene glaciation area located in the center of the Russian Plain.We attempted to verify the regional paleogeographic model by reconstructing the Late Pleistocene incision-infill cycles at the Eastern Borisoglebsk Upland based on a comparison of inactive interfluves headwaters and actual fluvial network palaeoarchives.The study was also aimed to determine the past extent of fluvial systems.Interdisciplinary research of the actual and buried topography,Ethology and pedogenic properties of surface deposits was carrid out with remote sensing data interpretation,DGPS survey,and detailed description of geological cores involved.The study was followed by analysis of grain size,chemical and organic contents,microstructure,and numerical dating.Integrating the available results,we propose a scenario of the fluvial network transformation at the Eastem Borisoglebsk Upland over the last 150 ka.At least four fluvial incision stages were determined while network extent has significantly changed through the Late Pleistocene.Three can be generally associated with the regional base level decrease-Late Moscow,Late Valdai and Late Holocene and accompanying isolation of the Nero Lake terraces of 130 m,100-105 m and 95-98 m.Incision stages were separated by landscape stability or aggradation periods those were asynchronous at the middle and upper parts of the fluvial network.The main agent of initial valley infill appears to be local lacustrine sedimentation altered by alluvial and colluvial deposition towards the second half of Valdai.Revealed landscape conditions variability emphasize the importance of comprehensive local correlations for regional retrospective models.展开更多
文摘Endorheic basins(ENBs) are inland drainage basins allowing no outflow to oceans.These basins in the active mountain chains of the convergence zones are under the influence of compressional tectonic activity and climate condition.The Zagros Mountains of Iran is one of the youngest convergence zones in which continental-continental collision has occurred.In this paper we hypothesize the formation of ENBs among the Zagros range after the epeirogenic stage in the Late Paleogene-Early Neogene.Due to tectonic activity and Quaternary climatic conditions,the ENBs pass the transition stage to exorheic,and still,some tectonic depressions are not linked to the evolutionary process of exorheic drainage of Zagros.The geometry of the drainage network of Kul and Mond basins in Fars arch shows that 67% of their water gaps are located along the thrusts and transverse basement faults in the east and west of the Fars arch.Geometrically,the Kul and Mond basins form triangles with their sides matching with the edges of the Arabian Plate where the major inherited faults of Arabian plate controls the shape of the Zagros basin and a low strain zone along the Razak fault with lower salt tectonic activity,where the wind gaps are created.The ENBs are located in the rainshadow slopes,but the Kul and Mond basins are located in the upwind slopes of rain waves.This factor and the heavy rains of the basin lead to increase of the erosion potential,destruction of depressions,and floods and consequently,the funnel-shaped gaps have a significant impact on the flood flow.
文摘The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)techniques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model’s result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.
基金conducted by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources(KIGAM)funded by the Ministry of Science and ICT。
文摘Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.
文摘Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local communities.We focused on the Bastam watershed where 9.3%of its surface is currently affected by gullying.Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability.However,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors.To cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of Entropy.And,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation stages.Being the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we chose.Such differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns.This is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application.
文摘The Zemo Imereti Plateau is located in the easternmost part of the limestone region of western Georgia and is Caucasus’ only karst plateau. It is centrally located in a relatively elevated part of the intermountain plain of the country of Georgia. Lithostratigraphical (petrographic, mineralogical, and XRD) research on terrigenous sediments found in caves in the region was conducted in the Upper Cretaceous limestones located at different hypsometric levels (400 - 700 m). This study focused on allochthonous deposits, which, due to sedimentological features, contains significant and complete information for paleogeographic reconstruction as opposed to the autochthonous sediments. Source provinces of the sediments’ origin were determined using petrographic analysis. Lithological study of the terrigenous sediments indicated their origin from the Racha range, as well as their transportation mechanisms, and sedimentation conditions during deposition. Approximate ages (the end of the Middle Pleistocene and the beginning of the Late Pleistocene) of ancient terrestrial sediments in Rganisklde Cave were achieved by taking into account the geological and geomorphological development of the region. Lithostratigraphical analysis of the cave deposits and modern archaeological data indicate that the formation of the caves in the Zemo Imereti plateau took place mainly during the end of the Middle Pleistocene and in the beginning of the Pleistocene;while on the southern slope of the Racha range and in the surrounding area of Ertso-Tsona (Caucasus southern slope), they formed in the Early Pleistocene.
基金supported by the Geographic Information Science Research Group,Ton Duc Thang University,Ho Chi Minh City,Viet Nam
文摘The sub-watershed prioritization is the ranking of different areas of a river basin according to their need to proper planning and management of soil and water resources.Decision makers should optimally allocate the investments to critical sub-watersheds in an economically effective and technically efficient manner.Hence,this study aimed at developing a user-friendly geographic information system(GIS)tool,Sub-Watershed Prioritization Tool(SWPT),using the Python programming language to decrease any possible uncertainty.It used geospatial-statistical techniques for analyzing morphometric and topohydrological factors and automatically identifying critical and priority sub-watersheds.In order to assess the capability and reliability of the SWPT tool,it was successfully applied in a watershed in the Golestan Province,Northern Iran.Historical records of flood and landslide events indicated that the SWPT correctly recognized critical sub-watersheds.It provided a cost-effective approach for prioritization of sub-watersheds.Therefore,the SWPT is practically applicable and replicable to other regions where gauge data is not available for each sub-watershed.
文摘Karst groundwater is the major natural resource of drinking water for many countries in the world. Especially in karstic regions, karst water requirements for settlements are provided from karst aquifers. Also, we should consider, that karst groundwater is becoming more and more valuable for drinking water supply. Thus, karst groundwater quality and permanent ecological monitoring are very important for populations. Moreover, if we consider that the karst landscape is the extremely sensitive system towards anthropogenic activities, since exaclty the anthropogenic activities largely identify the karst water pollution-turbidity causing factors. This paper presents a new study regarding the quality of the karst groundwater of the study area, which contains important resource of drinking water. In the mentioned study, 12 water samples were collected from different locations of the 4 main karst springs (Krikhula, Dolabistavi, Kidobana and Sakishore) during the spring and summer of 2014 and 2015 years. The main aim was to identify chemical compositions (Ni, Ag, Co, Cd, Zn, Pb, Al, Mg, Fe, F, Cu), and also, it was important to detect Escherichia coli (E. coli). Our research regarding all these chemical compositions shows that all the values are low and under the environmental limit according to the Georgian standards. We measured chemical parameters of all these samples by Atomic Absorption Spectroscopy (AAS) in the chemical laboratory of Ivane Javakhishvili Tbilisi State University, country of Georgia.
基金in part financed by the National Science Centre,Poland,project no.UMO2011/01/B/ST10/07295。
文摘The purpose of this study is to determine the size of air temperature changes with altitude in the mountains of the arid zone, on the example of the Upper Dades valley(High Atlas, Morocco). The air temperature change with altitude was determined on the basis of 5 years data from three meteorological stations. The analysis was carried out on an annual and seasonal basis. The annual and daily variations of thermal gradients between pairs of stations were also determined. It was found that the average thermal gradient in the Upper Dades valley was-1.02℃ per 100 m. The highest values of the thermal gradient occur in winter and the lowest in summer. In winter,the thermal gradient was characterized by the greatest variability. Minima of the daily variation of air temperature gradients were observed in early morning hours and maxima around midday. In the lower part of the valley, air temperature inversion frequently developed between 10 AM and 3 PM UTC.The obtained results show high thermal gradients in the mountains of the arid zone, with their annual amplitude increasing in the lower parts of the valley.The instantaneous values of the gradients were significantly modified by the supply of latent heat and the occurrence of dust storms. It has been shown that the advection factor plays an important role in shaping large gradient values. The study contains novel results of thermal gradient measurements in high mountains of arid zone.
文摘Pre-crisis management involves the optimal selection of relief and rescue centers to minimize vulnerability.Iran is particularly vulnerable due to its location on the Alpine-Himalaya seismic belt,resulting in an average death rate six times higher than the global average during earthquakes.Therefore,selecting appropriate relief and rescue centers is crucial to Iran’s disaster preparedness.When selecting the placement of rescue centers,accessibility and the appropriateness of the land should be taken into account as well as the distance from high-risk areas.The location of these centers does not require any particular combinations.To address this issue,a study was conducted utilizing GIS,artificial neural networks,fuzzy logic,and mathematical models to determine the optimal placement based on 12 indicators within two clusters:natural and human.To examine the information layers of the initial stage,a spatial data repository concerning the variables impacting the placement of these centers was established using ARCGIS.Using functions and algorithms such as Fuzzy Logic in IDRISI,TOPSIS,and VIKOR software,the layers were assessed for weightage before being overlaid.The study’s analysis of the models used revealed that the positioning priority limits of the areas differed across all four models.Notably,the areas with high desirability varied to a greater extent:the fuzzy model varied by 9.3%,neural network by 12.4%,VIKOR by 4.5%,and TOPSIS by 16.2%.The variance in results can be attributed to the differing levels of risk acceptance and non-acceptance in each model.Additionally,the study yielded other significant findings such as the correlation between study area size and model accuracy.Specifically,smaller study areas exhibited higher model accuracy.The research also demonstrated that both fuzzy and VIKOR models achieved greater accuracy.As a result,employing these models in crisis management planning,particularly in pre-crisis management for identifying rescue center locations,would be highly advantageous and increase the precision of these endeavors.
基金supported by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),UTS under grant numbers 321740.2232335,323930,and 321740.2232357
文摘In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.
基金financial supported by the Iran National Science Foundation(INSF)through research project No.96004000the GIS research group(Ton Duc Thang University)for supports via the research project“GIS-based applications for solving realworld problems”。
文摘Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA)based on Deep Belief Network(DBN)with Back Propagation(BP)algorithm optimized by the Genetic Algorithm(GA).For this task,a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE)technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy,root mean square error(RMSE),and area under the receiver operatic characteristic curve(AUC)were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC=0.989)and prediction accuracy(AUC=0.985),and based on the validation dataset it outperforms benchmark models including LR(0.885),LMT(0.934),BLR(0.936),ADT(0.976),NBT(0.974),REPTree(0.811),ANFIS-BAT(0.944),ANFIS-CA(0.921),ANFIS-IWO(0.939),ANFIS-ICA(0.947),and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.
文摘This article presents the information of the Georgian Caucasus glaciation at the Late Pleistocene and Holocene period (~126,000-11,700 calendar years ago). Our primary aim was to numerically reproduce the ice extent deduced from geological and geomorphological mapping. We used the analog method with the 30 m resolution SRTM DEM (Shuttle Radar Topography Mission Digital Elevation Model). In addition, the rates of glaciation of those times are identified based on the stadial moraines and erratic boulders. The current investigation has revealed that in the Late Pleistocene, the central and western Caucasus characterized the highest glaciation, while the eastern Caucasus boasted the lowest glaciated area, and in the southern Georgia’s highland glaciation has almost the same form as there is in the Eastern Caucasus today. The longest glaciers were located in the Enguri (Nenskra ~36 km, Mulkhura ~35.1 km, Dolra ~34.5 km), Kodori (Sakeni ~25 km, Klichi ~20 km, Marukhi ~17.3 km) Rioni (Buba ~23 km, Kirtisho ~20.5 km, Jejora ~17.5 km) and Tergi (Devdoraki ~38.5 km, Suatisi ~32 km) river basins. We found, that topography thresholds related to the elevation and hypsometry of individual catchments controlled the gradient of the rate of glacier expansion in the domain at that time.
基金the Russian Foundation for Basic Research,Russia[projects№16-35-00605 and№18-05-01118],the Department of Geomorphology and Paleogeography,Faculty of Geography,Lomonosov MSU,Russia[GM AAAA-A16-11632810089-5]and Department of Soil Geography and Evolution,Institute of Geography,RAS,Russia[GM 0148-2019-0006]in part of field working organization and Russian Science Foundation,Russia[project№14-27-00133]in part of microscopic and radioisotope research.Authors are grateful to A.P.Vergun and M.M.Ivanov for the provided aerial photo imagery,to Google Earth and Yandex Maps services for remote sensing data and open sources of global satellite radar DEMs(SRTM,ALOS 3D).
文摘The article discusses postglacial landscape transformation in the Northern Hemisphere Middle Pleistocene glaciation area located in the center of the Russian Plain.We attempted to verify the regional paleogeographic model by reconstructing the Late Pleistocene incision-infill cycles at the Eastern Borisoglebsk Upland based on a comparison of inactive interfluves headwaters and actual fluvial network palaeoarchives.The study was also aimed to determine the past extent of fluvial systems.Interdisciplinary research of the actual and buried topography,Ethology and pedogenic properties of surface deposits was carrid out with remote sensing data interpretation,DGPS survey,and detailed description of geological cores involved.The study was followed by analysis of grain size,chemical and organic contents,microstructure,and numerical dating.Integrating the available results,we propose a scenario of the fluvial network transformation at the Eastem Borisoglebsk Upland over the last 150 ka.At least four fluvial incision stages were determined while network extent has significantly changed through the Late Pleistocene.Three can be generally associated with the regional base level decrease-Late Moscow,Late Valdai and Late Holocene and accompanying isolation of the Nero Lake terraces of 130 m,100-105 m and 95-98 m.Incision stages were separated by landscape stability or aggradation periods those were asynchronous at the middle and upper parts of the fluvial network.The main agent of initial valley infill appears to be local lacustrine sedimentation altered by alluvial and colluvial deposition towards the second half of Valdai.Revealed landscape conditions variability emphasize the importance of comprehensive local correlations for regional retrospective models.
