Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world.The number of landslides and the level of damage across the globe has been increasing over time.Ther...Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world.The number of landslides and the level of damage across the globe has been increasing over time.Therefore,landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region.Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study.The prime goal of the study is to prepare landslide susceptibility maps(LSMs)using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide,twenty factors,including triggering and causative factors,were selected.A deep learning algorithm viz.convolutional neural network model(CNN)and three popular machine learning techniques,i.e.,random forest model(RF),artificial neural network model(ANN),and bagging model,were employed to prepare the LSMs.Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points.A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor,and the role of individual conditioning factors was estimated using information gain ratio.The result reveals that there is no severe multicollinearity among the landslide conditioning factors,and the triggering factor rainfall appeared as the leading cause of the landslide.Based on the final prediction values of each model,LSM was constructed and successfully portioned into five distinct classes,like very low,low,moderate,high,and very high susceptibility.The susceptibility class-wise distribution of landslides shows that more than 90%of the landslide area falls under higher landslide susceptibility grades.The precision of models was examined using the area under the curve(AUC)of the receiver operating characteristics(ROC)curve and statistical methods like root m展开更多
A detailed landslide susceptibility map was produced in the Youfang catchment using logistic regression method with datasets developed for a geographic information system(GIS).Known as one of the most landslide-prone ...A detailed landslide susceptibility map was produced in the Youfang catchment using logistic regression method with datasets developed for a geographic information system(GIS).Known as one of the most landslide-prone areas in China, the Youfang catchment of Longnan mountain region,which lies in the transitional area among QinghaiTibet Plateau, loess Plateau and Sichuan Basin, was selected as a representative case to evaluate the frequency and distribution of landslides.Statistical relationships for landslide susceptibility assessment were developed using landslide and landslide causative factor databases.Logistic regression(LR)was used to create the landslide susceptibility maps based on a series of available data sources: landslide inventory; distance to drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index, and land use.The quality of the landslide susceptibility map produced in this paper was validated and the result can be used fordesigning protective and mitigation measures against landslide hazards.The landslide susceptibility map is expected to provide a fundamental tool for landslide hazards assessment and risk management in the Youfang catchment.展开更多
Retrogressive thaw slumps(RTSs)caused by the thawing of ground ice on permafrost slopes have dramatically increased and become a common permafrost hazard across the Northern Hemisphere during previous decades.However,...Retrogressive thaw slumps(RTSs)caused by the thawing of ground ice on permafrost slopes have dramatically increased and become a common permafrost hazard across the Northern Hemisphere during previous decades.However,a gap remains in our comprehensive understanding of the spatial controlling factors,including the climate and terrain,that are conducive to these RTSs at a global scale.Using machine learning methodologies,we mapped the current and future RTSs susceptibility distributions by incorporating a range of environmental factors and RTSs inventories.We identified freezing-degree days and maximum summer rainfall as the primary environmental factors affecting RTSs susceptibility.The final ensemble susceptibility map suggests that regions with high to very high susceptibility could constitute(11.6±0.78)%of the Northern Hemisphere's permafrost region.When juxtaposed with the current(2000-2020)RTSs susceptibility map,the total area with high to very high susceptibility could witness an increase ranging from(31.7±0.65)%(SSP585)to(51.9±0.73)%(SSP126)by the 2041-2060.The insights gleaned from this study not only offer valuable implications for engineering applications across the Northern Hemisphere,but also provide a long-term insight into the potential change of RTSs in permafrost regions in response to climate change.展开更多
Windthrow problem is a difficult task for the forest managers in the Romanian Carpathians and especially in Iezer Mountains. The last windthrow, in July 2005, affected about 370 ha within the study area and left unpro...Windthrow problem is a difficult task for the forest managers in the Romanian Carpathians and especially in Iezer Mountains. The last windthrow, in July 2005, affected about 370 ha within the study area and left unprotected large slopes with important declivities （20-30°）. In our study, we try to propose a tool for forest management, in order to control and minimize the negative effect of wind upon the mountain forest ecosystem. The digital data input derived from forestry data （forest stand typology, age, canopy coverage index, forest productivity class） and from the forest biotope features （soil and topography parameters）. The main goal was to find a more objective way for digital layer reclassification in order to obtain the windthrow areas susceptibility map for the Iezer Mountains. Each digital layer has its own weight within the analysis and one of them was difficult to be modeled （the wind features）. A statistical approach was developed on the basis of local phenomena and the wind- throw features in the Romanian Carpathians. This allowed us to obtain the reclassification conditions for each digital layer. Forest canopy typology and soil features （mainly its volume） were considered as the key factors for the windthrow occurrence analysis. The final windthrow susceptibility map was validated with the help of the statistic occurrence of windthrow areas within each susceptibility class and after a field check of the sites. The result was encouraging, because 92.5% of the windthrow areas fall into the highest windthrow susceptibility class.展开更多
The area is a part of the Egyptian Eastern Desert in the northwestward to the Gulf of Suez. It covers an area of about 542 square kilometers. Wadi Bada’a is devoid of vegetation, because of the arid climate and water...The area is a part of the Egyptian Eastern Desert in the northwestward to the Gulf of Suez. It covers an area of about 542 square kilometers. Wadi Bada’a is devoid of vegetation, because of the arid climate and water scarcity. However, the present study concerns the flash flood and its impact on the industrial zone and connected road at wadi Bada’a. In this work, the bivariate statistical method using frequency ratio was used to evaluate the areas of potential risk. Geographic Information System package (GIS) was used to analyze and calculate different data sets. The different data source has been used in the research to produce a flood hazard susceptibility map of the area, including the geologic maps, Landsat-8 imagery, land use, and soil type associated with field investigation and data collection. Spatial database with elements at risk, related features and attributes at wadi Bada’a, were constructed. Training data were created randomly in the study area to create an inventory map with testing data. The inventory location of 95 location points has been created. The inventory datasets were divided into 75% of training datasets and 25% testing data. The independent flood-related factors were evaluated by analyzing each independently and assessing their impact on flooding with inventory datasets. The flood susceptibility maps were constructed-using training and testing datasets have been used to evaluate using the success rate method. The results of the accuracy assessment showed a success rate of 76.6% of Area Under Curve. Therefore, the main road in the study area almost at high risk in many parts because of flash flood, additionally the industrial activities located in the moderate risk zone.展开更多
The hilly regions of Nepal are potential for land sliding in rainy season. Lying between two major thrusts: Main Frontal Thrust (MFT) and Main Boundary Thrust (MBT), the rocks of Siwalik zone are very weak and fragile...The hilly regions of Nepal are potential for land sliding in rainy season. Lying between two major thrusts: Main Frontal Thrust (MFT) and Main Boundary Thrust (MBT), the rocks of Siwalik zone are very weak and fragile. Shallow and deep landslides are very common in the Siwalik zone during heavy and continuous rainfall. The landslide in the busy road and agglomerate settlements are destroying the life and properties every year in rainy season. This study aims to develop a landslide susceptibility map of Chatara-Barahakshetra area, Siwalik zones of eastern Nepal by the means of frequency ratio model. The paper utilized the remote sensing and GIS to develop a landslide susceptibility map. Total of 382 landslide polygons were mapped from Google earth and by field verification. The validation results showed that the success rate curve with 72.55 percentage of the area lying under the curve and the prediction rate curve with 71.73 percentage of the area lying under the curve indicating that prediction ability of the Frequency Ratio model. These landslide susceptibility maps can be used as a planning tool by prioritizing areas for controlling the landslide effects. More than 71% success rate indicate that frequency ratio model is suitable model for the landslide susceptibility in the study area.展开更多
Studies on susceptibility to debris flows at regional scale （ioo-looo km2） are important for the protection and management of mountain areas. To reach this objective, routing models, mainly based on land topography,...Studies on susceptibility to debris flows at regional scale （ioo-looo km2） are important for the protection and management of mountain areas. To reach this objective, routing models, mainly based on land topography, can be used to predict susceptible areas rapidly while necessitating few input data. In this research, Flow-R model is implemented to create the susceptibility map for the debris flow of the Vizze Valley （BZ, North-Eastern Italy; 134 km^2）. The analysis considers the model application at local scale for three sub-catchments and then it explores the model upsealing at the regional scale by verifying two methods to generate the source areas of debris-flow initiation. Using data of an extreme event occurred in the Vizze Valley （4 August 2012） and historical information, the modeling verification highlights that the propagation parameters are relatively simple to set in order to obtain correct runout distances. A double DTM filtering - using a threshold for the upslope contributing area （0.1 km^2） and a threshold for the terrain-slope angle （15°） provides a satisfactory prediction of source areas and susceptibility map within the geological conditions of the Vizze Valley.展开更多
文摘Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world.The number of landslides and the level of damage across the globe has been increasing over time.Therefore,landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region.Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study.The prime goal of the study is to prepare landslide susceptibility maps(LSMs)using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide,twenty factors,including triggering and causative factors,were selected.A deep learning algorithm viz.convolutional neural network model(CNN)and three popular machine learning techniques,i.e.,random forest model(RF),artificial neural network model(ANN),and bagging model,were employed to prepare the LSMs.Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points.A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor,and the role of individual conditioning factors was estimated using information gain ratio.The result reveals that there is no severe multicollinearity among the landslide conditioning factors,and the triggering factor rainfall appeared as the leading cause of the landslide.Based on the final prediction values of each model,LSM was constructed and successfully portioned into five distinct classes,like very low,low,moderate,high,and very high susceptibility.The susceptibility class-wise distribution of landslides shows that more than 90%of the landslide area falls under higher landslide susceptibility grades.The precision of models was examined using the area under the curve(AUC)of the receiver operating characteristics(ROC)curve and statistical methods like root m
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(164320H101)the Opening Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection of Chengdu University of Technology,China(SKLGP2012K012)+4 种基金the Opening Fund of Key Laboratory for Geo-hazards in Loess area(GLA2014005)the National Natural Science Foundation of China(No.40801212 and No.41201424)the 973 National Basic Research Program(Nos.2013CB733203,2013CB733204)the 863 National High-Tech Rand D Program(No.2012AA121302)the FP6 project"Mountain Risks"of the European Commission(No.MRTNCT-2006-035798)
文摘A detailed landslide susceptibility map was produced in the Youfang catchment using logistic regression method with datasets developed for a geographic information system(GIS).Known as one of the most landslide-prone areas in China, the Youfang catchment of Longnan mountain region,which lies in the transitional area among QinghaiTibet Plateau, loess Plateau and Sichuan Basin, was selected as a representative case to evaluate the frequency and distribution of landslides.Statistical relationships for landslide susceptibility assessment were developed using landslide and landslide causative factor databases.Logistic regression(LR)was used to create the landslide susceptibility maps based on a series of available data sources: landslide inventory; distance to drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index, and land use.The quality of the landslide susceptibility map produced in this paper was validated and the result can be used fordesigning protective and mitigation measures against landslide hazards.The landslide susceptibility map is expected to provide a fundamental tool for landslide hazards assessment and risk management in the Youfang catchment.
基金This study was jointly supported by the National Science Foundation of China(42071097 and 42372334)the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(2019QZKK0905)+1 种基金the Youth Innovation Promotion Association of the Chinese Academy of Sciences(2020421)the Program of China State Railway Group Co.Ltd.(K2022G017).
文摘Retrogressive thaw slumps(RTSs)caused by the thawing of ground ice on permafrost slopes have dramatically increased and become a common permafrost hazard across the Northern Hemisphere during previous decades.However,a gap remains in our comprehensive understanding of the spatial controlling factors,including the climate and terrain,that are conducive to these RTSs at a global scale.Using machine learning methodologies,we mapped the current and future RTSs susceptibility distributions by incorporating a range of environmental factors and RTSs inventories.We identified freezing-degree days and maximum summer rainfall as the primary environmental factors affecting RTSs susceptibility.The final ensemble susceptibility map suggests that regions with high to very high susceptibility could constitute(11.6±0.78)%of the Northern Hemisphere's permafrost region.When juxtaposed with the current(2000-2020)RTSs susceptibility map,the total area with high to very high susceptibility could witness an increase ranging from(31.7±0.65)%(SSP585)to(51.9±0.73)%(SSP126)by the 2041-2060.The insights gleaned from this study not only offer valuable implications for engineering applications across the Northern Hemisphere,but also provide a long-term insight into the potential change of RTSs in permafrost regions in response to climate change.
文摘Windthrow problem is a difficult task for the forest managers in the Romanian Carpathians and especially in Iezer Mountains. The last windthrow, in July 2005, affected about 370 ha within the study area and left unprotected large slopes with important declivities （20-30°）. In our study, we try to propose a tool for forest management, in order to control and minimize the negative effect of wind upon the mountain forest ecosystem. The digital data input derived from forestry data （forest stand typology, age, canopy coverage index, forest productivity class） and from the forest biotope features （soil and topography parameters）. The main goal was to find a more objective way for digital layer reclassification in order to obtain the windthrow areas susceptibility map for the Iezer Mountains. Each digital layer has its own weight within the analysis and one of them was difficult to be modeled （the wind features）. A statistical approach was developed on the basis of local phenomena and the wind- throw features in the Romanian Carpathians. This allowed us to obtain the reclassification conditions for each digital layer. Forest canopy typology and soil features （mainly its volume） were considered as the key factors for the windthrow occurrence analysis. The final windthrow susceptibility map was validated with the help of the statistic occurrence of windthrow areas within each susceptibility class and after a field check of the sites. The result was encouraging, because 92.5% of the windthrow areas fall into the highest windthrow susceptibility class.
文摘The area is a part of the Egyptian Eastern Desert in the northwestward to the Gulf of Suez. It covers an area of about 542 square kilometers. Wadi Bada’a is devoid of vegetation, because of the arid climate and water scarcity. However, the present study concerns the flash flood and its impact on the industrial zone and connected road at wadi Bada’a. In this work, the bivariate statistical method using frequency ratio was used to evaluate the areas of potential risk. Geographic Information System package (GIS) was used to analyze and calculate different data sets. The different data source has been used in the research to produce a flood hazard susceptibility map of the area, including the geologic maps, Landsat-8 imagery, land use, and soil type associated with field investigation and data collection. Spatial database with elements at risk, related features and attributes at wadi Bada’a, were constructed. Training data were created randomly in the study area to create an inventory map with testing data. The inventory location of 95 location points has been created. The inventory datasets were divided into 75% of training datasets and 25% testing data. The independent flood-related factors were evaluated by analyzing each independently and assessing their impact on flooding with inventory datasets. The flood susceptibility maps were constructed-using training and testing datasets have been used to evaluate using the success rate method. The results of the accuracy assessment showed a success rate of 76.6% of Area Under Curve. Therefore, the main road in the study area almost at high risk in many parts because of flash flood, additionally the industrial activities located in the moderate risk zone.
文摘The hilly regions of Nepal are potential for land sliding in rainy season. Lying between two major thrusts: Main Frontal Thrust (MFT) and Main Boundary Thrust (MBT), the rocks of Siwalik zone are very weak and fragile. Shallow and deep landslides are very common in the Siwalik zone during heavy and continuous rainfall. The landslide in the busy road and agglomerate settlements are destroying the life and properties every year in rainy season. This study aims to develop a landslide susceptibility map of Chatara-Barahakshetra area, Siwalik zones of eastern Nepal by the means of frequency ratio model. The paper utilized the remote sensing and GIS to develop a landslide susceptibility map. Total of 382 landslide polygons were mapped from Google earth and by field verification. The validation results showed that the success rate curve with 72.55 percentage of the area lying under the curve and the prediction rate curve with 71.73 percentage of the area lying under the curve indicating that prediction ability of the Frequency Ratio model. These landslide susceptibility maps can be used as a planning tool by prioritizing areas for controlling the landslide effects. More than 71% success rate indicate that frequency ratio model is suitable model for the landslide susceptibility in the study area.
基金granted by the Junior Research Grant Universitàdegli Studi di Padova,year 2013,prot.CPDR138494(“Criticitàidrauliche nel reticolo montano nei riguardi del movimento di detrito legnoso e di colate detritiche”Prof.Vincenzo D’Agostino)
文摘Studies on susceptibility to debris flows at regional scale （ioo-looo km2） are important for the protection and management of mountain areas. To reach this objective, routing models, mainly based on land topography, can be used to predict susceptible areas rapidly while necessitating few input data. In this research, Flow-R model is implemented to create the susceptibility map for the debris flow of the Vizze Valley （BZ, North-Eastern Italy; 134 km^2）. The analysis considers the model application at local scale for three sub-catchments and then it explores the model upsealing at the regional scale by verifying two methods to generate the source areas of debris-flow initiation. Using data of an extreme event occurred in the Vizze Valley （4 August 2012） and historical information, the modeling verification highlights that the propagation parameters are relatively simple to set in order to obtain correct runout distances. A double DTM filtering - using a threshold for the upslope contributing area （0.1 km^2） and a threshold for the terrain-slope angle （15°） provides a satisfactory prediction of source areas and susceptibility map within the geological conditions of the Vizze Valley.
