The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study are...The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.展开更多
The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques we...The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.展开更多
Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited applica...Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited application because of the uncertainties in identifying negative samples.The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows(GDFs)and is characterized by a prominent landform featuring deep gullies.Considering the limitations of the ML model,we developed and compared two combined statistical models(FA-WE and FA-IC)based on factor analysis(FA),weight of evidence(WE),and the information content(IC)method.The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation.The results show that the FA-IC model has the best performance.The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream,on both sides of the valley in the middle and downstream of the Parlung Tsangpo River,and in the narrow valley section of each tributary.These areas encompass 86 gullies and are characterized as"narrow and steep".展开更多
The ever-increasing deepwater oil and gas development in the Qiongdongnan Basin,South China Sea has initiated the need to evaluate submarine debris-flow hazard risks to seafloor infrastructures.This paper presents a c...The ever-increasing deepwater oil and gas development in the Qiongdongnan Basin,South China Sea has initiated the need to evaluate submarine debris-flow hazard risks to seafloor infrastructures.This paper presents a case study on evaluating the debris-flow hazard risks to the planned pipeline systems in this region.We used a numerical model to perform simulations to support this quantitative evaluation.First,one relict failure interpreted across the development site was simulated.The back-analysis modeling was used to validate the applicability of the rheological parameters.Then,this model was applied to forecast the runout behaviors of future debris flows originating from the unstable upslope regions considered to be the most critical to the pipeline systems surrounding the Manifolds A and B.The model results showed that the potential debris-flow hazard risks rely on the location of structures and the selection of rheological parameters.For the Manifold B and connected pipeline systems,because of their remote distances away from unstable canyon flanks,the potential debris flows impose few risks.However,the pipeline systems around the Manifold A are exposed to significant hazard risks from future debris flows with selected rheological parameters.These results are beneficial for the design of a more resilient pipeline route in consideration of future debris-flow hazard risks.展开更多
The quality of debris flow susceptibility mapping varies with sampling strategies. This paper aims at comparing three sampling strategies and determining the optimal one to sample the debris flow watersheds. The three...The quality of debris flow susceptibility mapping varies with sampling strategies. This paper aims at comparing three sampling strategies and determining the optimal one to sample the debris flow watersheds. The three sampling strategies studied were the centroid of the scarp area(COSA), the centroid of the flowing area(COFA), and the centroid of the accumulation area(COAA) of debris flow watersheds. An inventory consisting of 150 debris flow watersheds and 12 conditioning factors were prepared for research. Firstly, the information gain ratio(IGR) method was used to analyze the predictive ability of the conditioning factors. Subsequently, 12 conditioning factors were involved in the modeling of artificial neural network(ANN), random forest(RF) and support vector machine(SVM). Then, the receiver operating characteristic curves(ROC) and the area under curves(AUC) were used to evaluate the model performance. Finally, a scoring system was used to score the quality of the debris flow susceptibility maps. Samples obtained from the accumulation area have the strongest predictive ability and can make the models achieve the best performance. The AUC values corresponding to the best model performance on the validation dataset were 0.861, 0.804 and 0.856 for SVM, ANN and RF respectively. The sampling strategy of the centroid of the scarp area is optimal with the highest quality of debris flow susceptibility maps having scores of 373470, 393241 and 362485 for SVM, ANN and RF respectively.展开更多
Debris flow susceptibility mapping(DFSM)has been reported in many studies,however,the irrational use of the same conditioning factor system for DFSM in regional-scale has not been thoroughly resolved.In this paper,a r...Debris flow susceptibility mapping(DFSM)has been reported in many studies,however,the irrational use of the same conditioning factor system for DFSM in regional-scale has not been thoroughly resolved.In this paper,a region-partitioning method that is based on the topographic characteristics of watershed units was developed with the objective of establishing multiple conditioning factor systems for regional-scale DFSM.First,watershed units were selected as the mapping units and created throughout the entire research area.Four topographical factors,namely,elevation,slope,aspect and relative height difference,were selected as the basis for clustering watershed units.The k-means clustering analysis was used to cluster the watershed units according to their topographic characteristics to partition the study area into several parts.Then,the information gain ratio method was used to filter out superfluous factors to establish conditioning factor systems in each region for the subsequent debris flow susceptibility modeling.Last,a debris flow susceptibility map of the whole study area was acquired by merging the maps from all parts.DFSM of Yongji County in Jilin Province,China was selected as a case study,and the analytical hierarchy process method was used to conduct a comparative analysis to evaluate the performance of the region-partitioning method.The area under curve(AUC)values showed that the partitioning of the study area into two parts improved the prediction rate from 0.812 to 0.916.The results demonstrate that the region-partitioning method on the basis of topographic characteristics of watershed units can realize more reasonable regional-scale DFSM.Hence,the developed region-partitioning method can be used as a guide for regional-scale DFSM to mitigate the imminent debris flow risk.展开更多
The upper Yangtze River region is one of the most frequent debris flow areas in China. The study area contains a cascade of six large hydropower stations located along the river with total capacity of more than 70 mil...The upper Yangtze River region is one of the most frequent debris flow areas in China. The study area contains a cascade of six large hydropower stations located along the river with total capacity of more than 70 million kilowatts. The purpose of the study was to determine potential and dynamic differences in debris flow susceptibility and intensity with regard to seasonal monsoon events. We analyzed this region's debris flow history by examining the effective peak acceleration of antecedent earthquakes,the impacts of antecedent droughts, the combined effects of earthquakes and droughts, with regard to topography, precipitation, and loose solid material conditions. Based on these factors, we developed a debris flow susceptibility map. Results indicate that the entire debris flow susceptibility area is 167,500 km^2, of which 26,800 km^2 falls within the high susceptibility area, with 60,900 km^2 in medium and 79,800 km^2 are in low susceptibility areas. Three of the six large hydropower stations are located within the areas with high risk of debris flows. The synthetic zonation map of debris flow susceptibility for the study area corresponds with both the investigation data and actual distribution of debris flows. The results of debris flow susceptibility provide base-line data for mitigating, assessing, controlling and monitoring of debris flows hazards.展开更多
In the meizoseismal areas hit by the China Wenchuan earthquake on May 12, 2008, the disasterprone environment has changed dramatically, making the susceptibility assessment of debris flow more complex and uncertain. A...In the meizoseismal areas hit by the China Wenchuan earthquake on May 12, 2008, the disasterprone environment has changed dramatically, making the susceptibility assessment of debris flow more complex and uncertain. After the earthquake, debris flow hazards occurred frequently and effective susceptibility assessment of debris flow has become extremely important. Shenxi gully in Du Jiangyan city, located in the meizoseismal areas, was selected as the study area. Based on the research of disaster-prone environment and the main factors controlling debris flow, the susceptibility zonations of debris flow were mapped using factor weight method(FW), certainty coefficient method(CF) and geomorphic information entropy method(GI). Through comparative analysis, the study showed that these three methods underestimated susceptible degree of debris flow when used in the meizoseismal areas of Wenchuan earthquake. In order to solve this problem, this paper developed a modified certainty coefficient method(M-CF) to reflect the impact of rich loose materials on the susceptible degree of debris flow. In the modified method, the distribution and area of loose materials were obtained by field investigations and postearthquake remote sensing image, and four data sets, namely, lithology, elevation, slop and aspect, wereused to calculate the CF values. The result of M-CF method is in agreement with field investigations and the accuracy of the method is satisfied. The method has a wide application to the susceptibility assessment of debris flow in the earthquake stricken areas.展开更多
This research describes a quantitative,rapid,and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases.The proposed approach can aid decision makers ...This research describes a quantitative,rapid,and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases.The proposed approach can aid decision makers in land management and territorial planning,by first screening for areas with a higher debris flow susceptibility.Five environmental predisposing factors,namely,bedrock lithology,fracture network,quaternary deposits,slope inclination,and hydrographic network,were selected as independent parameters and their mutual interactions were described and quantified using the Rock Engineering System(RES)methodology.For each parameter,specific indexes were proposed,aiming to provide a final synthetic and representative index of debris flow susceptibility at the basin scale.The methodology was tested in four basins located in the Upper Susa Valley(NW Italian Alps)where debris flow events are the predominant natural hazard.The proposed matrix can represent a useful standardized tool,universally applicable,since it is independent of type and characteristic of the basin.展开更多
The Longchi area with the city of Dujiangyan, in the Sichuan province of China, is composed of Permian stone and diorites and Triassic sandstones and mudstones intercalated with slates. An abundance of loose co-seismi...The Longchi area with the city of Dujiangyan, in the Sichuan province of China, is composed of Permian stone and diorites and Triassic sandstones and mudstones intercalated with slates. An abundance of loose co-seismic materials were present on the slopes after the May 12, 2008 Wenchuan earthquake, which in later years served as source material for rainfall-induced debris flows or shallow landslides. A total of 48 debris flows, all triggered by heavy rainfall on 13 th August 2010, are described in this paper. Field investigation, supported by remote sensing image interpretation, was conducted to interpret the co-seismic landslides in the debris flow gullies. Specific characteristics of the study area such as slope, aspect, elevation, channel gradient, lithology, and gully density were selected for the evaluation of debris flow susceptibility. A score was given to all the debris flow gullies based on the probability of debris flow occurrence for the selected factors. In order to get the contribution of the different factors, principal component analyses were applied. A comprehensive score was obtained for the 48 debris flow gullies which enabled us to make a susceptibility map for debris flows with three classes. Twenty-two gullies have a high susceptibility, twenty gullies show a moderate susceptibility and six gullies have a low susceptibility for debris flows.展开更多
Turkey is highly prone to landslides because of the geological and geographic location.The study area,which is located in a tectonically active region,has been significantly affected by mass movements.Flow type landsl...Turkey is highly prone to landslides because of the geological and geographic location.The study area,which is located in a tectonically active region,has been significantly affected by mass movements.Flow type landslides are frequently observed due to this location.This study aims at determining the source area and propagation of debris flows in the study area.We used the heuristic method to extract source areas of debris flow,and then used receiver operating characteristic(ROC)curve analysis to assess the performance of the method,and finally calculated the Area under curve(AUC)values being 83.64%and 80.39%for the success rate and prediction rate,respectively.We calculated potential propagation area and runout distance with Flow-R software.In conclusion,the obtained results(susceptibility map,propagation and runout distance)are very important for decisionmakers at the region located on an active fault zone,which is highly prone to natural disasters.The outputs of this study could be used in site selection studies,designing erosion prevention systems and protecting existing human-made structures.展开更多
Studies on susceptibility to debris flows at regional scale(100-1000 km^2) are important for the protection and management of mountain areas. To reach this objective, routing models, mainly based on land topography, c...Studies on susceptibility to debris flows at regional scale(100-1000 km^2) 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 upscaling 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
Jiuzhaigou is situated on a mountain-canyon region and is famous for frequent tectonic activities.An abundance of loose co-seismic landslides and collapses were produced on gullies after the Jiuzhaigou Earthquake on A...Jiuzhaigou is situated on a mountain-canyon region and is famous for frequent tectonic activities.An abundance of loose co-seismic landslides and collapses were produced on gullies after the Jiuzhaigou Earthquake on August 8,2017,which was served as material source for debris flow in later years.Debris flow appears frequently which are seriously endangering the safety of people's lives and properties.Even the earliest debris flow appeared in areas where no case ever reported before.The debris flow susceptibility evaluation(DFSE)is used for predicting the areas prone to debris flow,which is urgently required to avoid hazards and help to guide the strategy of preventive measures.Therefore,this work employs debris flow in Jiuzhaigou to reveal the characteristics of disaster-pregnant environment and to explore the application of machine learning in DFSE.Some new viewpoints are suggested:(i)Material density factor of debris flow is first adopted in this work,and it is proved to be a critical factor for triggering debris flows by sensitivity analysis method.(ii)Deep neural network and convolutional neural network(CNN)achieve relatively good area under the curve(AUC)values and are 0.021-0.024 higher than traditional machine learning methods.(iii)Watershed units combined with CNNbased model can achieve more accurate,reliable and practical susceptibility map.This work provides an idea for prevention of debris flow in mountainous lands.展开更多
Debris-flow disasters occurred frequently after the Mw 8.0 Wenchuan earthquake on 12 May 2008 in Sichuan Province, China. Based on historical accounts of debris-flow disaster events, it found that debris flow occurren...Debris-flow disasters occurred frequently after the Mw 8.0 Wenchuan earthquake on 12 May 2008 in Sichuan Province, China. Based on historical accounts of debris-flow disaster events, it found that debris flow occurrence is closely related to the impact of earthquakes and droughts, because earthquakedrought activities can increase the loose solid materials, which can transform into debris flows under the effect of rainstorms. Based on the analysis of historical earthquake activity(frequency, magnitude and location), drought indexes and the trend of climate change(amount of rainfall), a prediction method was established, and the regional debris flow susceptibility was predicted. Furthermore, in a debris flow-susceptible site, effective warning and monitoring are essential not only from an economicpoint of view but are also considered as a frontline approach to alleviate hazards. The advantages of the prediction and early monitoring include(1) the acquired results being sent to the central government for policy making;(2) lives and property in mountainous areas can be protected, such as the 570 residents in the Aizi valley, who evacuated successfully before debris flows in 2012;(3) guiding the government to identify the areas of disasters and the preparation for disaster prevention and mitigation, such as predicting disasters in high-risk areas in the period 2012-2017, helping the government to recognize the development trend of disasters;(4) the quantitative prediction of regional debris-flow susceptibility, such as after the Wenchuan earthquake, can promote scientific and sustainable development and socioeconomic planning in earthquake-struck areas.展开更多
The study area,located in the southeast of Tibet along the Sichuan-Tibet highway,is a part of Palongzangbu River basin where mountain hazards take place frequently.On the ground of field surveying,historical data and ...The study area,located in the southeast of Tibet along the Sichuan-Tibet highway,is a part of Palongzangbu River basin where mountain hazards take place frequently.On the ground of field surveying,historical data and previous research,a total of 31 debris flow gullies are identified in the study area and 5 factors are chosen as main parameters for evaluating the hazard of debris flows in this study.Spatial analyst functions of geographic information system (GIS) are utilized to produce debris flow inventory and parameter maps.All data are built into a spatial database for evaluating debris flow hazard.Integrated with GIS techniques,the fuzzy relation method is used to calculate the strength of relationship between debris flow inventory and parameters of the database.With this methodology,a hazard map of debris flows is produced.According to this map,6.6% of the study area is classified as very high hazard,7.3% as high hazard,8.4% as moderate hazard,32.1% as low hazard and 45.6% as very low hazard or non-hazard areas.After validating the results,this methodology is ultimately confirmed to be available.展开更多
Compared to large-scale infrequent disasters like volcanic eruptions, earthquakes, and gas explosions from volcanic (maar) lakes, most small-scale everyday disasters (e.g., landslides and floods) are not well reported...Compared to large-scale infrequent disasters like volcanic eruptions, earthquakes, and gas explosions from volcanic (maar) lakes, most small-scale everyday disasters (e.g., landslides and floods) are not well reported and documented in Cameroon, despite the fact that cumulatively, they cause the most casualties and distress to the people affected. This paper documents a debris flow that occurred on the 1st of August 2012 in Kakpenyi, a quarter found in Tinta, one of the villages of Akwaya Sub Division in Manyu Division of the Southwest Region of Cameroon. The event started from the western slope (06°14.350'N & 09°31.475'E) of a hogback in the settlement, and mobilized ca 3.47 × 106 m3 of material over a ca 1 km distance. The material was made up of a chaotic mix of mud, rock fragments, boulders, twigs, tree logs, trunks, and roots. Its distal part dammed river Kakpenyi forming a 10 m deep lake which eventually safely emptied itself. No casualties were recorded but 20 people got injured and 21 people lost farmland. The debris flow was not caused by earthquake shaking. Instead, inappropriate land use acted as a remote cause to predispose the steep slope, while heavy rainfall triggered the flow. Verbal reports talk of a similar event 40 years ago in the area. This shows that Kakpenyi is vulnerable to this kind of hazard, requiring that major infrastructural development projects like roads and bridges in the area be preceded by detailed hazard and vulnerability assessments.展开更多
基金financially supported by the Higher Education Commission of Pakistan (HEC) grant under National Research Program for Universities (NRPU) with No: (20-14681/NRPU/R&D/HEC/20212021)。
文摘The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(Grant no.2019QZKK0904)Natural Science Foundation of Hebei Province(Grant no.D2022403032)S&T Program of Hebei(Grant no.E2021403001).
文摘The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.
基金funded by the National Natural Science Foundation of China(Grant Nos.42377170).
文摘Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited application because of the uncertainties in identifying negative samples.The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows(GDFs)and is characterized by a prominent landform featuring deep gullies.Considering the limitations of the ML model,we developed and compared two combined statistical models(FA-WE and FA-IC)based on factor analysis(FA),weight of evidence(WE),and the information content(IC)method.The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation.The results show that the FA-IC model has the best performance.The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream,on both sides of the valley in the middle and downstream of the Parlung Tsangpo River,and in the narrow valley section of each tributary.These areas encompass 86 gullies and are characterized as"narrow and steep".
基金The National Natural Science Foundation of China under contract Nos 42106198 and 41720104001the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)under contract No.GML2019ZD0210.
文摘The ever-increasing deepwater oil and gas development in the Qiongdongnan Basin,South China Sea has initiated the need to evaluate submarine debris-flow hazard risks to seafloor infrastructures.This paper presents a case study on evaluating the debris-flow hazard risks to the planned pipeline systems in this region.We used a numerical model to perform simulations to support this quantitative evaluation.First,one relict failure interpreted across the development site was simulated.The back-analysis modeling was used to validate the applicability of the rheological parameters.Then,this model was applied to forecast the runout behaviors of future debris flows originating from the unstable upslope regions considered to be the most critical to the pipeline systems surrounding the Manifolds A and B.The model results showed that the potential debris-flow hazard risks rely on the location of structures and the selection of rheological parameters.For the Manifold B and connected pipeline systems,because of their remote distances away from unstable canyon flanks,the potential debris flows impose few risks.However,the pipeline systems around the Manifold A are exposed to significant hazard risks from future debris flows with selected rheological parameters.These results are beneficial for the design of a more resilient pipeline route in consideration of future debris-flow hazard risks.
基金This work was supported by National Natural Science Foundation of China(Grant no.41972267 and no.41572257)Graduate Innovation Fund of Jilin University(Grant no.101832020CX232)。
文摘The quality of debris flow susceptibility mapping varies with sampling strategies. This paper aims at comparing three sampling strategies and determining the optimal one to sample the debris flow watersheds. The three sampling strategies studied were the centroid of the scarp area(COSA), the centroid of the flowing area(COFA), and the centroid of the accumulation area(COAA) of debris flow watersheds. An inventory consisting of 150 debris flow watersheds and 12 conditioning factors were prepared for research. Firstly, the information gain ratio(IGR) method was used to analyze the predictive ability of the conditioning factors. Subsequently, 12 conditioning factors were involved in the modeling of artificial neural network(ANN), random forest(RF) and support vector machine(SVM). Then, the receiver operating characteristic curves(ROC) and the area under curves(AUC) were used to evaluate the model performance. Finally, a scoring system was used to score the quality of the debris flow susceptibility maps. Samples obtained from the accumulation area have the strongest predictive ability and can make the models achieve the best performance. The AUC values corresponding to the best model performance on the validation dataset were 0.861, 0.804 and 0.856 for SVM, ANN and RF respectively. The sampling strategy of the centroid of the scarp area is optimal with the highest quality of debris flow susceptibility maps having scores of 373470, 393241 and 362485 for SVM, ANN and RF respectively.
基金funded by the National Natural Science Foundation of China(Grant Nos.41977221 and 41202197)Jilin Provincial Science and Technology Department(No.20190303103SF,No.20170101001JC)。
文摘Debris flow susceptibility mapping(DFSM)has been reported in many studies,however,the irrational use of the same conditioning factor system for DFSM in regional-scale has not been thoroughly resolved.In this paper,a region-partitioning method that is based on the topographic characteristics of watershed units was developed with the objective of establishing multiple conditioning factor systems for regional-scale DFSM.First,watershed units were selected as the mapping units and created throughout the entire research area.Four topographical factors,namely,elevation,slope,aspect and relative height difference,were selected as the basis for clustering watershed units.The k-means clustering analysis was used to cluster the watershed units according to their topographic characteristics to partition the study area into several parts.Then,the information gain ratio method was used to filter out superfluous factors to establish conditioning factor systems in each region for the subsequent debris flow susceptibility modeling.Last,a debris flow susceptibility map of the whole study area was acquired by merging the maps from all parts.DFSM of Yongji County in Jilin Province,China was selected as a case study,and the analytical hierarchy process method was used to conduct a comparative analysis to evaluate the performance of the region-partitioning method.The area under curve(AUC)values showed that the partitioning of the study area into two parts improved the prediction rate from 0.812 to 0.916.The results demonstrate that the region-partitioning method on the basis of topographic characteristics of watershed units can realize more reasonable regional-scale DFSM.Hence,the developed region-partitioning method can be used as a guide for regional-scale DFSM to mitigate the imminent debris flow risk.
基金supported by the National Natural Science Foundation of China (Grant No. 41661134012 and 41501012)the Taiwan Youth Visiting Scholar Fellowship of Chinese Academy of Sciences (Grant No. 2015TW2ZB0001)
文摘The upper Yangtze River region is one of the most frequent debris flow areas in China. The study area contains a cascade of six large hydropower stations located along the river with total capacity of more than 70 million kilowatts. The purpose of the study was to determine potential and dynamic differences in debris flow susceptibility and intensity with regard to seasonal monsoon events. We analyzed this region's debris flow history by examining the effective peak acceleration of antecedent earthquakes,the impacts of antecedent droughts, the combined effects of earthquakes and droughts, with regard to topography, precipitation, and loose solid material conditions. Based on these factors, we developed a debris flow susceptibility map. Results indicate that the entire debris flow susceptibility area is 167,500 km^2, of which 26,800 km^2 falls within the high susceptibility area, with 60,900 km^2 in medium and 79,800 km^2 are in low susceptibility areas. Three of the six large hydropower stations are located within the areas with high risk of debris flows. The synthetic zonation map of debris flow susceptibility for the study area corresponds with both the investigation data and actual distribution of debris flows. The results of debris flow susceptibility provide base-line data for mitigating, assessing, controlling and monitoring of debris flows hazards.
基金Financial support was provided by Ministry of Water Resources welfare industry funding(Grant No.201301058)Key Laboratory of Mountain Hazards and Earth Surface Processes independent project funding:Dynamic process and buried risk of debris flow in Shenxi gully after Wenchuan earthquakethe international cooperation project of Ministry of Science and Technology(Grant No.2013DFA21720)
文摘In the meizoseismal areas hit by the China Wenchuan earthquake on May 12, 2008, the disasterprone environment has changed dramatically, making the susceptibility assessment of debris flow more complex and uncertain. After the earthquake, debris flow hazards occurred frequently and effective susceptibility assessment of debris flow has become extremely important. Shenxi gully in Du Jiangyan city, located in the meizoseismal areas, was selected as the study area. Based on the research of disaster-prone environment and the main factors controlling debris flow, the susceptibility zonations of debris flow were mapped using factor weight method(FW), certainty coefficient method(CF) and geomorphic information entropy method(GI). Through comparative analysis, the study showed that these three methods underestimated susceptible degree of debris flow when used in the meizoseismal areas of Wenchuan earthquake. In order to solve this problem, this paper developed a modified certainty coefficient method(M-CF) to reflect the impact of rich loose materials on the susceptible degree of debris flow. In the modified method, the distribution and area of loose materials were obtained by field investigations and postearthquake remote sensing image, and four data sets, namely, lithology, elevation, slop and aspect, wereused to calculate the CF values. The result of M-CF method is in agreement with field investigations and the accuracy of the method is satisfied. The method has a wide application to the susceptibility assessment of debris flow in the earthquake stricken areas.
文摘This research describes a quantitative,rapid,and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases.The proposed approach can aid decision makers in land management and territorial planning,by first screening for areas with a higher debris flow susceptibility.Five environmental predisposing factors,namely,bedrock lithology,fracture network,quaternary deposits,slope inclination,and hydrographic network,were selected as independent parameters and their mutual interactions were described and quantified using the Rock Engineering System(RES)methodology.For each parameter,specific indexes were proposed,aiming to provide a final synthetic and representative index of debris flow susceptibility at the basin scale.The methodology was tested in four basins located in the Upper Susa Valley(NW Italian Alps)where debris flow events are the predominant natural hazard.The proposed matrix can represent a useful standardized tool,universally applicable,since it is independent of type and characteristic of the basin.
基金supported by the Key Projects in the National Science & Technology Pillar Program (Grant No. 2011BAK12B01)Basic Scientific Project of Ministry of Sciences and Technology of China (Grant No. 2011FY110100-3)
文摘The Longchi area with the city of Dujiangyan, in the Sichuan province of China, is composed of Permian stone and diorites and Triassic sandstones and mudstones intercalated with slates. An abundance of loose co-seismic materials were present on the slopes after the May 12, 2008 Wenchuan earthquake, which in later years served as source material for rainfall-induced debris flows or shallow landslides. A total of 48 debris flows, all triggered by heavy rainfall on 13 th August 2010, are described in this paper. Field investigation, supported by remote sensing image interpretation, was conducted to interpret the co-seismic landslides in the debris flow gullies. Specific characteristics of the study area such as slope, aspect, elevation, channel gradient, lithology, and gully density were selected for the evaluation of debris flow susceptibility. A score was given to all the debris flow gullies based on the probability of debris flow occurrence for the selected factors. In order to get the contribution of the different factors, principal component analyses were applied. A comprehensive score was obtained for the 48 debris flow gullies which enabled us to make a susceptibility map for debris flows with three classes. Twenty-two gullies have a high susceptibility, twenty gullies show a moderate susceptibility and six gullies have a low susceptibility for debris flows.
文摘Turkey is highly prone to landslides because of the geological and geographic location.The study area,which is located in a tectonically active region,has been significantly affected by mass movements.Flow type landslides are frequently observed due to this location.This study aims at determining the source area and propagation of debris flows in the study area.We used the heuristic method to extract source areas of debris flow,and then used receiver operating characteristic(ROC)curve analysis to assess the performance of the method,and finally calculated the Area under curve(AUC)values being 83.64%and 80.39%for the success rate and prediction rate,respectively.We calculated potential propagation area and runout distance with Flow-R software.In conclusion,the obtained results(susceptibility map,propagation and runout distance)are very important for decisionmakers at the region located on an active fault zone,which is highly prone to natural disasters.The outputs of this study could be used in site selection studies,designing erosion prevention systems and protecting existing human-made structures.
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(100-1000 km^2) 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 upscaling 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.
基金funded by National Natural Science Foundation of China(Nos.42172322,U2268213 and 42007419)National Key Research and Development Program of China(No.2018YFC1505403)+2 种基金Natural Sciences Funding Project of Hunan Province(No.2020JJ5981)Excellent Youth Fund Project of Hunan Provincial Education Department(No.21B0226)Fundamental Research Funds for the Central Universities of Central South University(No.2022ZZTS0646)。
文摘Jiuzhaigou is situated on a mountain-canyon region and is famous for frequent tectonic activities.An abundance of loose co-seismic landslides and collapses were produced on gullies after the Jiuzhaigou Earthquake on August 8,2017,which was served as material source for debris flow in later years.Debris flow appears frequently which are seriously endangering the safety of people's lives and properties.Even the earliest debris flow appeared in areas where no case ever reported before.The debris flow susceptibility evaluation(DFSE)is used for predicting the areas prone to debris flow,which is urgently required to avoid hazards and help to guide the strategy of preventive measures.Therefore,this work employs debris flow in Jiuzhaigou to reveal the characteristics of disaster-pregnant environment and to explore the application of machine learning in DFSE.Some new viewpoints are suggested:(i)Material density factor of debris flow is first adopted in this work,and it is proved to be a critical factor for triggering debris flows by sensitivity analysis method.(ii)Deep neural network and convolutional neural network(CNN)achieve relatively good area under the curve(AUC)values and are 0.021-0.024 higher than traditional machine learning methods.(iii)Watershed units combined with CNNbased model can achieve more accurate,reliable and practical susceptibility map.This work provides an idea for prevention of debris flow in mountainous lands.
基金funded by National Natural Science Foundation of China(Grant No.41671112 and 41861134008)National Key Research and Development Plan(Grant No.2018YFC1505202)Sichuan Province Science and Technology Plan Project Key research and development projects(Grant No.18ZDYF0329)
文摘Debris-flow disasters occurred frequently after the Mw 8.0 Wenchuan earthquake on 12 May 2008 in Sichuan Province, China. Based on historical accounts of debris-flow disaster events, it found that debris flow occurrence is closely related to the impact of earthquakes and droughts, because earthquakedrought activities can increase the loose solid materials, which can transform into debris flows under the effect of rainstorms. Based on the analysis of historical earthquake activity(frequency, magnitude and location), drought indexes and the trend of climate change(amount of rainfall), a prediction method was established, and the regional debris flow susceptibility was predicted. Furthermore, in a debris flow-susceptible site, effective warning and monitoring are essential not only from an economicpoint of view but are also considered as a frontline approach to alleviate hazards. The advantages of the prediction and early monitoring include(1) the acquired results being sent to the central government for policy making;(2) lives and property in mountainous areas can be protected, such as the 570 residents in the Aizi valley, who evacuated successfully before debris flows in 2012;(3) guiding the government to identify the areas of disasters and the preparation for disaster prevention and mitigation, such as predicting disasters in high-risk areas in the period 2012-2017, helping the government to recognize the development trend of disasters;(4) the quantitative prediction of regional debris-flow susceptibility, such as after the Wenchuan earthquake, can promote scientific and sustainable development and socioeconomic planning in earthquake-struck areas.
文摘The study area,located in the southeast of Tibet along the Sichuan-Tibet highway,is a part of Palongzangbu River basin where mountain hazards take place frequently.On the ground of field surveying,historical data and previous research,a total of 31 debris flow gullies are identified in the study area and 5 factors are chosen as main parameters for evaluating the hazard of debris flows in this study.Spatial analyst functions of geographic information system (GIS) are utilized to produce debris flow inventory and parameter maps.All data are built into a spatial database for evaluating debris flow hazard.Integrated with GIS techniques,the fuzzy relation method is used to calculate the strength of relationship between debris flow inventory and parameters of the database.With this methodology,a hazard map of debris flows is produced.According to this map,6.6% of the study area is classified as very high hazard,7.3% as high hazard,8.4% as moderate hazard,32.1% as low hazard and 45.6% as very low hazard or non-hazard areas.After validating the results,this methodology is ultimately confirmed to be available.
文摘Compared to large-scale infrequent disasters like volcanic eruptions, earthquakes, and gas explosions from volcanic (maar) lakes, most small-scale everyday disasters (e.g., landslides and floods) are not well reported and documented in Cameroon, despite the fact that cumulatively, they cause the most casualties and distress to the people affected. This paper documents a debris flow that occurred on the 1st of August 2012 in Kakpenyi, a quarter found in Tinta, one of the villages of Akwaya Sub Division in Manyu Division of the Southwest Region of Cameroon. The event started from the western slope (06°14.350'N & 09°31.475'E) of a hogback in the settlement, and mobilized ca 3.47 × 106 m3 of material over a ca 1 km distance. The material was made up of a chaotic mix of mud, rock fragments, boulders, twigs, tree logs, trunks, and roots. Its distal part dammed river Kakpenyi forming a 10 m deep lake which eventually safely emptied itself. No casualties were recorded but 20 people got injured and 21 people lost farmland. The debris flow was not caused by earthquake shaking. Instead, inappropriate land use acted as a remote cause to predispose the steep slope, while heavy rainfall triggered the flow. Verbal reports talk of a similar event 40 years ago in the area. This shows that Kakpenyi is vulnerable to this kind of hazard, requiring that major infrastructural development projects like roads and bridges in the area be preceded by detailed hazard and vulnerability assessments.
