Based on the meteorological and geological disaster data, ground observation data set, CLDAS grid point data set, and EC, BJ and other model product data during 2008-2020, the temporal and spatial distribution charact...Based on the meteorological and geological disaster data, ground observation data set, CLDAS grid point data set, and EC, BJ and other model product data during 2008-2020, the temporal and spatial distribution characteristics of meteorological and geological disasters and precipitation were analyzed, and the causes of the occurrence of meteorological geological disasters and the deviation of model precipitation forecast were revealed. Besides, an objective precipitation forecast system and a forecast and early warning system of meteorological and geological disasters were established. The results show that meteorological and geological disasters and precipitation were mainly concentrated from May to October, of which continuous precipitation appeared frequently in June and September, and convective precipitation was mainly distributed in July-August;the occurrence frequency of meteorological and geological disasters was basically consistent with the distribution of accumulated precipitation and short-term heavy precipitation, and they were mainly concentrated in the southern and eastern parts of Qinghai. Meteorological and geological disasters were basically caused by heavy rain and above, and meteorological and geological disasters were divided into three types: continuous precipitation(type I), short-term heavy precipitation(type II) and mixed precipitation(type III). For type I, the early warning conditions of meteorological and geological disasters in Qinghai are as follows: if the soil volumetric water content difference between 0-10 and 10-40 cm is ≤0.03 mm^(3)/mm^(3), or the soil volumetric water content at one of the depths is ≥0.25 mm^(3)/mm^(3), the future effective precipitation reaches 8.4 mm in 1 h, 10.2 mm in 2 h, 11.5 mm in 3 h, 14.2 mm in 6 h, 17.7 mm in 12 h, and 18.2 mm in 24 h, and such warning conditions are mainly used in Yushu, Guoluo, southern Hainan, southern Huangnan and other places. For type II, when the future effective precipitation is up to 11.5 mm in 1 h, 14.9 mm in 2 h, 16.2 mm in 3 h, 19.9 mm in 6 h, 25.3 mm in 12 h, and 26.3 mm in 24 h, such precipitation thresholds are mainly used in Hainan, Huangnan, and eastern Guoluo;as it is up to 13.3 mm in 1 h, 15.5 mm in 2 h, 16.6 mm in 3 h, 19.9 mm in 6 h, 31.1 mm in 12 h, and 34.0 mm in 24 h, such precipitation thresholds are mainly used in Hehuang valley. The precipitation thresholds of type III are between type I and type II, and closer to that of type II;such precipitation thresholds are mainly used in Hainan, Huangnan, and northern Guoluo. The forecasting ability of global models for heavy rain and above was not as good as that of mesoscale numerical prediction model, and global models had a wet bias for small-scale precipitation and a dry bias for large-scale precipitation;meso-scale models had a significantly larger precipitation bias. The forecast ability of precipitation objective forecast system constructed by frequency matching and multi-model integration has improved. At the same time, the constructed grid forecast and early warning system of meteorological and geological disasters is more precise and accurate, and is of instructive significance for the forecast and early warning of meteorological and geological disasters.展开更多
Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Co...Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.展开更多
Ocean mining activities have been ongoing for nearly 70 years,making great contributions to industrialization.Given the increasing demand for energy,along with the restructuring of the energy supply catalyzed by effor...Ocean mining activities have been ongoing for nearly 70 years,making great contributions to industrialization.Given the increasing demand for energy,along with the restructuring of the energy supply catalyzed by efforts to achieve a low-carbon economy,deep seabed mining will play an important role in addressing energy-and resource-related problems in the future.However,deep seabed mining remains in the exploratory stage,with many challenges presented by the high-pressure,low-temperature,and complex geologic and hydrodynamic environments in deep-sea mining areas,which are inaccessible to human activities.Thus,considerable efforts are required to ensure sustainable,economic,reliable,and safe deep seabed mining.This study reviews the latest advances in marine engineering geology and the environment related to deep-sea min-ing activities,presents a bibliometric analysis of the development of ocean mineral resources since the 1950s,summarizes the development,theory,and issues related to techniques for the three stages of ocean mining(i.e.,exploration,extraction,and closure),and discusses the engineering geology environment,geological disasters,in-situ monitoring techniques,envi-ronmental protection requirements,and environmental effects in detail.Finally,this paper gives some key conclusions and future perspectives to provide insights for subsequent studies and commercial mining operations.展开更多
Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-drive...Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.展开更多
Vegetation plays an important role in soil and water conservation, water conservation and carbon sequestration of an ecosystem. The restoration of damaged vegetation is of great significance to the maintenance of spec...Vegetation plays an important role in soil and water conservation, water conservation and carbon sequestration of an ecosystem. The restoration of damaged vegetation is of great significance to the maintenance of species diversity and the restoration of the regional ecological environment. It is also one of the most effective measures to improve the fragile ecosystem. This paper summarizes the research results from decades of damaged vegetation recovery in the process of vegetation recovery, the main driving factor and the restoration mode.展开更多
The susceptibility evaluation of landslides has become one of the key environmental issues that people are concerned about. This study took the land-slides in Xishuangbanna, Yunnan Province as the study object, and se...The susceptibility evaluation of landslides has become one of the key environmental issues that people are concerned about. This study took the land-slides in Xishuangbanna, Yunnan Province as the study object, and selected 10 evaluation factors such as digital elevation model (DEM), slope aspect, precipitation, land use, water system, roads, population density, lithology, faults, and NDVI. Different machine learning methods were compared and studied, and the ROC (receiver operating characteristics) curve verification revealed that the accuracy of the random forest evaluation model was high. In the prediction and evaluation of the susceptibility of landslides, five risk levels were divided. After the superimposed analysis, 87.26% of the disaster points fell in the first and second susceptibility areas. The spot analysis found that the distribution of hot spots is consistent with the distribution of disaster spots. In a word, the results of this study can provide better technical support for the evaluation and early warning of landslides in Southwest China.展开更多
Many landslide disasters,which tend to result in significant damage,are caused by typhoon-triggered rainstorms.In this case,it is very important to study the dynamic characteristics of the hydrological response of lan...Many landslide disasters,which tend to result in significant damage,are caused by typhoon-triggered rainstorms.In this case,it is very important to study the dynamic characteristics of the hydrological response of landslide bodies since it enables the early warning and prediction of landslide disasters in typhoon periods.To investigate the dynamic mechanisms of groundwater in a landslide body under typhoon-triggered rainstorm conditions,the authors selected the landslide occurring in Zhonglin Village,Wencheng County,China(also referred to as Zhonglin Village landslide)as a case study.The transient seepage field characteristics of groundwater in the landslide body were simulated with two different rainfall models by using the finite element method(FEM).The research results show that the impact of typhoon-triggered rainstorms on landslides can be divided into three stages:(i)Rapid rise of groundwater level;(ii)infiltration of groundwater from the surface to deeper level,and(iii)surface runoff erosion.Moreover,the infiltration rate of groundwater in the landslide body is mainly affected by the intensity of typhoon-induced rainfall.It can be deduced that higher rainfall intensity leads to a greater potential difference and a higher infiltration rate.The rainfall intensity also determines the development mode of landslide deformation and destruction.展开更多
On August 7,2023,Mangshi City,Dehong Prefecture experienced a local heavy rainstorm,and the geological disaster caused by the heavy rainfall caused casualties and property losses.Based on the real-time observation dat...On August 7,2023,Mangshi City,Dehong Prefecture experienced a local heavy rainstorm,and the geological disaster caused by the heavy rainfall caused casualties and property losses.Based on the real-time observation data of automatic stations,Doppler weather radar detection and meteorological risk warning products,the disaster situation,social impact,forecast and early warning service,causes of heavy precipitation and forecast and early warning inspection were summarized and analyzed.The results show that the heavy rainfall was prominent locally,lasted for a long time and accumulated a large amount of rainfall.There were biases in model products,and it was difficult for forecasters to make subjective corrections in complex terrain.The analysis ideas and focus points of heavy rainfall forecast,the improvement ideas and technical schemes of forecast deviation,and the improvement ideas and suggestions of services were summarized.It provides a reference for the forecast and early warning of severe weather in the future.展开更多
Identifying the types of disasters and analyzing the distribution pattern of and the spatial correlation between villages and disasters are important prerequisite for the disaster prevention and mitigation of traditio...Identifying the types of disasters and analyzing the distribution pattern of and the spatial correlation between villages and disasters are important prerequisite for the disaster prevention and mitigation of traditional villages.Taking Aba Tibetan and Qiang Autonomous Prefecture as an example and using the tools of kernel density evaluation and spatial statistics,this paper analyzes the distribution pattern of and the spatial correlation between traditional villages and typical geological disasters on the three spatial scales of Aba Prefecture,village agglomeration area,and village administrative area with necessary explanations.It concludes that most of the traditional villages in Aba Prefecture agglomerate in the middle-and high-level alpine gorges and some are clustered at either the border area or the central hinterland of counties;with the increase of maximum seismic intensity and frequency of earthquakes,it appears there is an increase of traditional villages and their trend of agglomeration,which implies an overlap of the areas of dense traditional villages and high-density geological disasters,as well as the synchronous increase of traditional village density and geological disaster density;and the traditional villages in Aba Prefecture are affected by multiple disasters,in particular landslide and debris flow.Finally,the paper discusses the issue of disaster prevention and mitigation in the protection and development planning of traditional villages.展开更多
基金Supported by the Project of Key Laboratory for Disaster Prevention and Mitigation of Qinghai Province (QFZ-2021-Z04)Project of Qinghai Science and Technology Department (2020-ZJ-739)Key Project of Qinghai Provincial Meteorological Bureau (QXZ2020-03)。
文摘Based on the meteorological and geological disaster data, ground observation data set, CLDAS grid point data set, and EC, BJ and other model product data during 2008-2020, the temporal and spatial distribution characteristics of meteorological and geological disasters and precipitation were analyzed, and the causes of the occurrence of meteorological geological disasters and the deviation of model precipitation forecast were revealed. Besides, an objective precipitation forecast system and a forecast and early warning system of meteorological and geological disasters were established. The results show that meteorological and geological disasters and precipitation were mainly concentrated from May to October, of which continuous precipitation appeared frequently in June and September, and convective precipitation was mainly distributed in July-August;the occurrence frequency of meteorological and geological disasters was basically consistent with the distribution of accumulated precipitation and short-term heavy precipitation, and they were mainly concentrated in the southern and eastern parts of Qinghai. Meteorological and geological disasters were basically caused by heavy rain and above, and meteorological and geological disasters were divided into three types: continuous precipitation(type I), short-term heavy precipitation(type II) and mixed precipitation(type III). For type I, the early warning conditions of meteorological and geological disasters in Qinghai are as follows: if the soil volumetric water content difference between 0-10 and 10-40 cm is ≤0.03 mm^(3)/mm^(3), or the soil volumetric water content at one of the depths is ≥0.25 mm^(3)/mm^(3), the future effective precipitation reaches 8.4 mm in 1 h, 10.2 mm in 2 h, 11.5 mm in 3 h, 14.2 mm in 6 h, 17.7 mm in 12 h, and 18.2 mm in 24 h, and such warning conditions are mainly used in Yushu, Guoluo, southern Hainan, southern Huangnan and other places. For type II, when the future effective precipitation is up to 11.5 mm in 1 h, 14.9 mm in 2 h, 16.2 mm in 3 h, 19.9 mm in 6 h, 25.3 mm in 12 h, and 26.3 mm in 24 h, such precipitation thresholds are mainly used in Hainan, Huangnan, and eastern Guoluo;as it is up to 13.3 mm in 1 h, 15.5 mm in 2 h, 16.6 mm in 3 h, 19.9 mm in 6 h, 31.1 mm in 12 h, and 34.0 mm in 24 h, such precipitation thresholds are mainly used in Hehuang valley. The precipitation thresholds of type III are between type I and type II, and closer to that of type II;such precipitation thresholds are mainly used in Hainan, Huangnan, and northern Guoluo. The forecasting ability of global models for heavy rain and above was not as good as that of mesoscale numerical prediction model, and global models had a wet bias for small-scale precipitation and a dry bias for large-scale precipitation;meso-scale models had a significantly larger precipitation bias. The forecast ability of precipitation objective forecast system constructed by frequency matching and multi-model integration has improved. At the same time, the constructed grid forecast and early warning system of meteorological and geological disasters is more precise and accurate, and is of instructive significance for the forecast and early warning of meteorological and geological disasters.
基金supported by the projects of the China Geological Survey(DD20221729,DD20190291)Zhuhai Urban Geological Survey(including informatization)(MZCD–2201–008).
文摘Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.
基金Funding for this research was provided by the National Natural Science Foundation of China (42022052,42277138,and 52108337)the National Key R&D Program of China (2022YFC2803800)+1 种基金the Shandong Provincial Natural Science Foundation (ZR2020YQ29)UCL's Department of Civil,Environmental and Geomatic Engineering,and Ocean University of China.
文摘Ocean mining activities have been ongoing for nearly 70 years,making great contributions to industrialization.Given the increasing demand for energy,along with the restructuring of the energy supply catalyzed by efforts to achieve a low-carbon economy,deep seabed mining will play an important role in addressing energy-and resource-related problems in the future.However,deep seabed mining remains in the exploratory stage,with many challenges presented by the high-pressure,low-temperature,and complex geologic and hydrodynamic environments in deep-sea mining areas,which are inaccessible to human activities.Thus,considerable efforts are required to ensure sustainable,economic,reliable,and safe deep seabed mining.This study reviews the latest advances in marine engineering geology and the environment related to deep-sea min-ing activities,presents a bibliometric analysis of the development of ocean mineral resources since the 1950s,summarizes the development,theory,and issues related to techniques for the three stages of ocean mining(i.e.,exploration,extraction,and closure),and discusses the engineering geology environment,geological disasters,in-situ monitoring techniques,envi-ronmental protection requirements,and environmental effects in detail.Finally,this paper gives some key conclusions and future perspectives to provide insights for subsequent studies and commercial mining operations.
基金This work was financially supported by National Natural Science Foundation of China(41972262)Hebei Natural Science Foundation for Excellent Young Scholars(D2020504032)+1 种基金Central Plains Science and technology innovation leader Project(214200510030)Key research and development Project of Henan province(221111321500).
文摘Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.
文摘Vegetation plays an important role in soil and water conservation, water conservation and carbon sequestration of an ecosystem. The restoration of damaged vegetation is of great significance to the maintenance of species diversity and the restoration of the regional ecological environment. It is also one of the most effective measures to improve the fragile ecosystem. This paper summarizes the research results from decades of damaged vegetation recovery in the process of vegetation recovery, the main driving factor and the restoration mode.
文摘The susceptibility evaluation of landslides has become one of the key environmental issues that people are concerned about. This study took the land-slides in Xishuangbanna, Yunnan Province as the study object, and selected 10 evaluation factors such as digital elevation model (DEM), slope aspect, precipitation, land use, water system, roads, population density, lithology, faults, and NDVI. Different machine learning methods were compared and studied, and the ROC (receiver operating characteristics) curve verification revealed that the accuracy of the random forest evaluation model was high. In the prediction and evaluation of the susceptibility of landslides, five risk levels were divided. After the superimposed analysis, 87.26% of the disaster points fell in the first and second susceptibility areas. The spot analysis found that the distribution of hot spots is consistent with the distribution of disaster spots. In a word, the results of this study can provide better technical support for the evaluation and early warning of landslides in Southwest China.
基金the Investigation Project of Geological Disasters in Feiyun Basin of Zhejiang Province(D20190648)the Disaster Geological Survey Project of Lishui,Zhejiang Province(D20160282).
文摘Many landslide disasters,which tend to result in significant damage,are caused by typhoon-triggered rainstorms.In this case,it is very important to study the dynamic characteristics of the hydrological response of landslide bodies since it enables the early warning and prediction of landslide disasters in typhoon periods.To investigate the dynamic mechanisms of groundwater in a landslide body under typhoon-triggered rainstorm conditions,the authors selected the landslide occurring in Zhonglin Village,Wencheng County,China(also referred to as Zhonglin Village landslide)as a case study.The transient seepage field characteristics of groundwater in the landslide body were simulated with two different rainfall models by using the finite element method(FEM).The research results show that the impact of typhoon-triggered rainstorms on landslides can be divided into three stages:(i)Rapid rise of groundwater level;(ii)infiltration of groundwater from the surface to deeper level,and(iii)surface runoff erosion.Moreover,the infiltration rate of groundwater in the landslide body is mainly affected by the intensity of typhoon-induced rainfall.It can be deduced that higher rainfall intensity leads to a greater potential difference and a higher infiltration rate.The rainfall intensity also determines the development mode of landslide deformation and destruction.
基金Supported by the Research on the Spatial and Temporal Characteristics and Occurrence Mechanism of Rainstorm in Dehong (STIAP202244)Key Laboratory of Heavy Rainfall in River Basins,China Meteorological Administration (2023BHR-Y09)+1 种基金Project of Key Laboratory of Hydrometeorology,China Meteorological Administration (23SWQXZ009)National Natural Science Foundation of China (42075013,41765003,41665005).
文摘On August 7,2023,Mangshi City,Dehong Prefecture experienced a local heavy rainstorm,and the geological disaster caused by the heavy rainfall caused casualties and property losses.Based on the real-time observation data of automatic stations,Doppler weather radar detection and meteorological risk warning products,the disaster situation,social impact,forecast and early warning service,causes of heavy precipitation and forecast and early warning inspection were summarized and analyzed.The results show that the heavy rainfall was prominent locally,lasted for a long time and accumulated a large amount of rainfall.There were biases in model products,and it was difficult for forecasters to make subjective corrections in complex terrain.The analysis ideas and focus points of heavy rainfall forecast,the improvement ideas and technical schemes of forecast deviation,and the improvement ideas and suggestions of services were summarized.It provides a reference for the forecast and early warning of severe weather in the future.
基金supported by the National Key R&D Program entitled Research on Natural Disaster Risk Assessment and Emergency Response of Immovable Cultural Relics(No.2019YFC1520800)the Sichuan Province Key R&D Program entitled Research on Natural Disaster Risk Assessment and Early Warning System of Traditional Villages in Ethnic Minority Areas of Western Sichuan Province(No.2021YFS0367)。
文摘Identifying the types of disasters and analyzing the distribution pattern of and the spatial correlation between villages and disasters are important prerequisite for the disaster prevention and mitigation of traditional villages.Taking Aba Tibetan and Qiang Autonomous Prefecture as an example and using the tools of kernel density evaluation and spatial statistics,this paper analyzes the distribution pattern of and the spatial correlation between traditional villages and typical geological disasters on the three spatial scales of Aba Prefecture,village agglomeration area,and village administrative area with necessary explanations.It concludes that most of the traditional villages in Aba Prefecture agglomerate in the middle-and high-level alpine gorges and some are clustered at either the border area or the central hinterland of counties;with the increase of maximum seismic intensity and frequency of earthquakes,it appears there is an increase of traditional villages and their trend of agglomeration,which implies an overlap of the areas of dense traditional villages and high-density geological disasters,as well as the synchronous increase of traditional village density and geological disaster density;and the traditional villages in Aba Prefecture are affected by multiple disasters,in particular landslide and debris flow.Finally,the paper discusses the issue of disaster prevention and mitigation in the protection and development planning of traditional villages.
