Debris Flow Susceptibility Research Articles

Susceptibility assessment of “2020.3.30” Xichang post-fire debris flow using a machine learning method

In this study, a machine learning method, i.e., random forest (RF) model, was employed to assess post-fire debris flow (PFDF) susceptibility after the Xichang forest fire occurred on 30 March, 2020. First, we conducted a tracking survey in the rainy season, on-site tests, and remote sensing image interpretation and obtained 10 impacting factors, i.e., the basin area, relief ratio, basin shape coefficient, percentage of area with a slope greater than 50%, proportion of moderate or high severity burned areas, distribution of gravel in the basin, vegetation types and distribution in the basin, early cumulative erosion after fire, peak rainfall in a 1-h interval, and peak rainfall in a 24-h interval, after their correlation test to build a spatial database. Subsequently, a total of 181 PFDF events in the database were randomly divided into training (70%) and validation (30%) samples. Thereafter, the RF model was used to acquire the susceptibility of PFDF in the study area. Finally, receiver operating characteristic (ROC) curve, area under curve (AUC) value, sensitivity, specificity, and accuracy were utilized to validate the predictive performance of the model. Results show that the RF model has good predictive ability with AUC of 93.4%, sensitivity of 88.3%, specificity of 99.3%, and accuracy of 97.8%. This study provides a scientific basis for PFDF disaster prevention and risk management in Xichang City and its surrounding areas.

Susceptibility assessment of debris flow from Baoxing River basin in Lushan earthquake zone, China

As one of the prevalent natural disasters, debris flow often occurs in mountainous areas and causes severe property losses. This study primarily focuses on describing the spatial distribution of debris flow in the Baoxing River basin in the Lushan earthquake zone, Sichuan Province, and then a susceptibility index map. Geographic information systems (GIS) and certainty factor (CF) method were used to establish the relationship between debris flow sub-basins and six predisposing parameters: elevation, slope angle, slope aspect, geologic periods, lithology, and NDVI (normalized difference vegetation index). The results show that elevations above 2400m, slopes from 20° to 45°, slope aspects of east, south-east, and south, geologic periods of Sinian, Ordovician, and Carboniferous, NDVIs of -1 to 0.2, lithology areas such as pyroclastic and carbonate of sedimentary rock, and metamorphic rock containing mafic and magnesian are the special conditions that cause debris flows. Furthermore, it has higher susceptibility potential for the combination of topography and geological parameters, including these predisposing parameters of elevation, slope angle, slope aspect, geologic periods, and lithology, than other combinations. The debris flow susceptibility assessment results show that the moderate and above susceptibility area accounts for around 94% of the total basin area or 51.5% of the study area; more than half of the study area is debris flow-prone, indicating that debris flows are quite active in the study area.

Morphometry and Debris-Flow Susceptibility Map in Mountain Drainage Basins of the Vallo di Diano, Southern Italy

In watershed mountain basins, affected in the last decades by strong rainfall events, the role of debris-flow and debris flood processes was investigated. Morphometric parameters have proven to be useful first-approximation indicators in discriminating those processes, especially in large areas of investigation. Computation of morphometric parameters in 19 watershed mountain basins of the western side valley of the Vallo di Diano intermontane basin (southern Italy) was carried out. This procedure was integrated by a semi-automatic elaboration of the potential susceptibility to debris flows, using Flow-R modelling. This software, providing an empirical model of the preliminary susceptibility assessment at a regional scale, was applied in many countries of the world. The implementation of Flow-R modelling requires a GIS application and some thematic base maps extracted using DEMs analysis. A 5-meter-resolution DEM has been used in order to produce the susceptibility maps of the whole study area, and the results are compared and discussed with the real debris flow/flood events that occurred in 1993, 2005, 2010, and 2017 in the studied area. The results have provided a good reliability of Flow-R modelling within small catchment mountain basins.

Susceptibility Analysis of Glacier Debris Flow by Investigating the Changes in Glaciers Based on Remote Sensing: A Case Study

Glacier debris flow is one of the most critical categories of geological hazards in high-mountain regions. To reduce its potential negative effects, it needs to investigate the susceptibility of glacier debris flow. However, when evaluating the susceptibility of glacier debris flow, most research work considered the impact of existing glacier area, while ignoring the impact of changes in glacier ablation volume. In this paper, we considered the impact of the changes in the glacier ablation volume to investigate the susceptibility of glacier debris flow. We proposed to evaluate the susceptibility analysis in G217 gullies with frequent glacial debris flow on the Duku highway, Xinjiang Province. Specifically, by using the simple band ratio method with the manual correction to identify glacier outlines, we identified the ablation zone by comparing the glacier boundary in 2000 with that in 2015. We then calculated ablation volume by changes in glacier elevation and ablation area from 2000 to 2015. Finally, we used the volume of glacier melting in different watersheds as the main factor to evaluate the susceptibility based on the improved geomorphic information entropy (GIE) method. We found that, overall, the improved GIE method with a correction coefficient based on the glacier ablation volume is better than the previous method. Deglaciation can be adapted to analyze glacier debris flow susceptibility based on glaciology and geomorphology. Our presented work can be applied to other similar glacial debris flow events in high-mountain regions.

Effects of terrain on the occurrence of debris flows after forest harvesting

ABSTRACT Forest harvesting and subsequent forest regeneration represent widespread changes in land cover in mountain regions. Although impacts of forest harvesting on landslide initiation has been widely reported, the effects of forest harvesting on the occurrence of debris flows remains unclear. We propose that forest harvesting will differentially affect the susceptibility of debris flows amongst catchments with different terrain characteristics. This hypothesis was assessed in the Sanko catchment, Japan, where comprehensive forest harvest records date back to 1913. The frequency of debris flows directly originating from landslides occurred in similar timeframes as the occurrence of landslides. Landslides that reached channels and continued downstream as debris flows were more prevalent in steep channel reaches with small hillslope-channel junction angles. In addition to the increase in the frequency of landslides, especially within 10 years after forest harvesting, debris flows caused by mass movement of channel deposits in steep reaches increased during this period. These relationships between the occurrence of debris flows and channel topography indicate a high susceptibility of debris flow occurrence after forest harvesting in first and zero-order mountain streams. Sediment previously routed into channel networks by landslides is likely a more important factor for in-channel debris flow initiation in the lower channel reaches, while instability of areas proximate to riparian zones, including stream banks and geomorphic hollows, possibly accelerate occurrence of debris flows in upper channel reaches. Consequently, catchment topography should be considered in evaluating debris flow risk after forest harvesting.

Susceptibility Assessment of Single Gully Debris Flow Based on AHP and Extension Method

Debris flow mainly happens in mountainous areas all around the world with deadly social and economic impacts. With the speedy development of the mountainous economy, the debris flow susceptibility evaluation in the mountainous areas is of crucial importance for the safety of mountainous life and economy. Yunnan province of China is one of the worst hitting areas by debris flow in the world. In this paper, debris flow susceptibility assessment of Datong and Taicun gully near the first bend of Jinsha River has been done with the help of site investigation and GIS and remote sensing techniques. Eight causative factors, including slope, topographic wetness index, sediments transport index, ground roughness, basin area, bending coefficient, source material, and normalised difference vegetation index, have been selected for debris flow susceptibility evaluation. Analytical hierarchy process combined with Extension method has been used to calculate the susceptibility level of Datong and Taicun gullies. The evaluation result shows that both the gullies have a moderate susceptibility to debris flow. The result suggests that all the ongoing engineering projects such as mining and road construction work should be done with all precautionary measures, and the excavated material should adequately store in the gullies. Doi: 10.28991/cej-2021-03091702 Full Text: PDF

Comparison of different sampling strategies for debris flow susceptibility mapping: A case study using the centroids of the scarp area, flowing area and accumulation area of debris flow watersheds

Comparison of different sampling strategies for debris flow susceptibility mapping: A case study using the centroids of the scarp area, flowing area and accumulation area of debris flow watersheds

Development of a region-partitioning method for debris flow susceptibility mapping

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.

Debris flow susceptibility assessment based on topo-hydrological factors at different unit scales: a case study of Mentougou district, Beijing

Debris flows are a widespread and dangerous geological disaster that cause severe damage to residents, buildings and roads in the Mentougou mountain belt, Beijing. According to existing reports and field investigation results, previous records tend to be of the areas affected by debris flow, not the process area where the debris flow occurred. This study selected eight topo-hydrological factors related to debris flow initiation and hydrological processes to explore the susceptibility of debris flow in the source area. The weights of the factors are determined by the analytic hierarchy process (AHP) and the principal component analysis (PCA) method, and the final evaluation result is calculated based on the information content model (ICM). For different hydrological response units, the evaluation results are quite different. The reason for this is that the information content of topo-hydrological factors varies with the unit size. Based on the verification results of the receiver operating characteristic curve (ROC) and Kappa coefficient, the evaluation model with a hydrological response unit of group A2 performs better, and nearly half (48%) of the results are with high susceptibility or extremely high susceptibility. Therefore, satisfactory evaluation of the debris flow susceptibility can be achieved based on the application of topo-hydrological factors. The established model is especially suitable for exploring the initiation and migration process of debris flow in Mentougou mountain area and places with similar topography background.

Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms

Although the prediction of debris flow-prone areas represents a key step towards reducing damages, modeling debris flow susceptibility is complicated. In addition, the role of debris flow causal drivers in forested mountain landscapes is still poorly understood. To gain a holistic view of the causes of debris flows in the Umyeonsan, Seoul, South Korea region, we coupled the convolutional neural network (CNN) with two evolutionary optimization algorithms – grey wolf optimization (GWO) and cuckoo optimization algorithm (COA). Applying geoinformatics to debris flow factors, debris-flow susceptibility maps were generated and their validities were assessed with receiver operating characteristic (ROC) curves. The results reveal that three causative factors seem to contribute most to debris flows in the study area. The evolutionary optimization algorithms achieved higher goodness-of-fit and predictive power than the standalone CNN model. The goodness-of-fit and predictive skill measures of the CNN susceptibility map were 0.76 and 0.73. The values of CNN hybridized with GWO were 0.81 and 0.81 and hybridized with COA were 0.83 and 0.82. Slope degree, tree age, stream power index, geographical class, and soil drainage were the factors most affecting debris flow likelihood. The CNN-COA is the superior model and it predicted that 40.6% of the study area (i.e., 1844.96 km2) is highly and very highly susceptible to debris flows. The methodology can be applied for analysis of other region to improve risk management and guide development and land use planning.

Debris-Flow and Debris-Flood Susceptibility Mapping for Geohazard Risk Prioritization

ABSTRACT Regional-scale assessments for debris-flow and debris-flood propagation and avulsion on fans can be challenging. Geomorphological mapping based on aerial or satellite imagery requires substantial field verification effort. Surface evidence of past events may be obfuscated by development or obscured by repeat erosion or debris inundation, and trenching may be required to record the sedimentary architecture and date past events. This paper evaluates a methodology for debris-flow and debris-flood susceptibility mapping at regional scale based on a combination of digital elevation model (DEM) metrics to identify potential debris source zones and flow propagation modeling using the Flow-R code that is calibrated through comparison to mapped alluvial fans. The DEM metrics enable semi-automated identification and preliminary, process-based classification of streams prone to debris flow and debris flood. Flow-R is a susceptibility mapping tool that models potential flow inundation based on a combination of spreading and runout algorithms considering DEM topography and empirical propagation parameters. The methodology is first evaluated at locations where debris-flow and debris-flood hazards have been previously assessed based on field mapping and detailed numerical modeling. It is then applied over a 125,000 km2 area in southern British Columbia, Canada. The motivation for the application of this methodology is that it represents an objective and repeatable approach to susceptibility mapping, which can be integrated in a debris-flow and debris-flood risk prioritization framework at regional scale to support risk management decisions.

Mapping Susceptibility to Debris Flows Triggered by Tropical Storms: A Case Study of the San Vicente Volcano Area (El Salvador, CA)

In this study, an inventory of storm-triggered debris flows performed in the area of the San Vicente volcano (El Salvador, CA) was used to calibrate predictive models and prepare a landslide susceptibility map. The storm event struck the area in November 2009 as the result of the simultaneous action of low-pressure system 96E and Hurricane Ida. Multivariate Adaptive Regression Splines (MARS) was employed to model the relationships between a set of environmental variables and the locations of the debris flows. Validation of the models was performed by splitting 100 random samples of event and non-event 10 m pixels into training and test subsets. The validation results revealed an excellent (area under the receiver operating characteristic (ROC) curve (AUC) = 0.80) and stable (AUC std. dev. = 0.01) ability of MARS to predict the locations of the debris flows which occurred in the study area. However, when using the Youden’s index as probability threshold to discriminate between pixels predicted as positives and negatives, MARS exhibits a moderate ability to identify stable cells (specificity = 0.66). The final debris flow susceptibility map, which was prepared by averaging for each pixel the score of the 100 MARS repetitions, shows where future debris flows are more likely to occur, and thus may help in mitigating the risk associated with these landslides.

Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility

Regional scale debris flow susceptibility is widely evaluated by statistical methods. However, the initiation mechanism of debris flow is not considered, which leads to the neglect of the microtopographic characteristics. To address this problem, we established three novel combined models by introducing the physical model into statistical methods. The integrating models consists of two parts, the statistical models and the TRIGRS model. The eventual results obtained with the integrating model consider both the prediction result of the statistical method for debris flow susceptibility and the mechanism of debris flow initiation. To test the feasibility of the integrating model, three representative statistical models, the analytic hierarchy process (AHP), Shannon entropy (Entropy) and support vector machine (SVM) were selected to evaluate debris flow susceptibility in Yongji County of Jilin Province, China. The results demonstrate that the performance of the integrated models is significantly better than that of the single statistical model, especially in the local areas. The integrating models (AHP-TR, Entropy-TR, SVM-TR) can generate higher quality debris flow susceptibility maps (DFSMs) than the single model, which clearly reflect the scope and boundaries of the areas which are most prone to debris flow and identify the flat land and valleys between adjacent high-prone areas. It also reduces the overprediction generated by the physical model. In general, combining the statistical methods with the TRIGRS model can maximize the strengths of these models and avoid their weaknesses and obtain the effect of 1 + 1 > 2.

Changes in Debris-Flow Susceptibility after the Wenchuan Earthquake Revealed by Meteorological and Hydro-Meteorological Thresholds

Changes in Debris-Flow Susceptibility after the Wenchuan Earthquake Revealed by Meteorological and Hydro-Meteorological Thresholds

Analysis of Characteristics of Debris Flow Susceptibility in Rugged Mountain Range in the Seoraksan National Park, Korea

Analysis of Characteristics of Debris Flow Susceptibility in Rugged Mountain Range in the Seoraksan National Park, Korea

Na\xefve Bayes Classifier for Debris Flow Disaster Mitigation in Mount Merapi Volcanic Rivers, Indonesia, Using X-band Polarimetric Radar

Debris flow triggered by rainfall that accompanies a volcanic eruption is a serious secondary impact of a volcanic disaster. The probability of debris flow events can be estimated based on the prior information of rainfall from historical and geomorphological data that are presumed to relate to debris flow occurrence. In this study, a debris flow disaster warning system was developed by applying the Naïve Bayes Classifier (NBC). The spatial likelihood of the hazard is evaluated at a small subbasin scale by including high-resolution rainfall measurements from X-band polarimetric weather radar, a topographic factor, and soil type as predictors. The study was conducted in the Gendol River Basin of Mount Merapi, one of the most active volcanoes in Indonesia. Rainfall and debris flow occurrence data were collected for the upper Gendol River from October 2016 to February 2018 and divided into calibration and validation datasets. The NBC was used to estimate the status of debris flow incidences displayed in the susceptibility map that is based on the posterior probability from the predictors. The system verification was performed by quantitative dichotomous quality indices along with a contingency table. Using the validation datasets, the advantage of the NBC for estimating debris flow occurrence is confirmed. This work contributes to existing knowledge on estimating debris flow susceptibility through the data mining approach. Despite the existence of predictive uncertainty, the presented system could contribute to the improvement of debris flow countermeasures in volcanic regions.

Application of the borderline-SMOTE method in susceptibility assessments of debris flows in Pinggu District, Beijing, China

According to information provided by the residents of Pinggu District, Beijing, 79 gullies were investigated. Based on the results of field investigations, 65 gullies were classified as debris flow gullies, and 14 were classified as non-debris flow gullies. By using ArcGIS software, the area and the quantity information of various factors affecting the occurrence of debris flows were obtained. The frequency ratio method was used to obtain the combinations of factors conducive to the debris flow occurrences in the study area. The susceptibility of debris flows in Pinggu District was evaluated by the information value method, certainty factor method, and logistic regression (LR) method. When using the LR method, the number of positive and negative samples should be balanced; therefore, the borderline-SMOTE method was used to generate samples belonging to the minority class. By comparing the results obtained by the three methods, it was found that the results obtained by the LR method are best. This result indicates that for disaster susceptibility assessments, the borderline-SMOTE method can be used to synthesize samples. The susceptibility evaluation results show that debris flows are more likely to occur in the southeast part of Pinggu District.

Analysis on the Susceptibility of Debris Flow along a Highway in Gannan

Under the background of the rapid economic growth in China, domestic infrastructure construction is also constantly improving. Thereof, the rapid development of highways and railways is outstandingly expanding. During the construction and operation of highways and railways, geological disasters often occur along the railway, which seriously endanger people’s lives and property and sustainable economic and social development. Among them, debris flow is one of the most serious geological disasters that endanger highway safety. The proposed Zhuoni-Hezuo Section of Fengxian (Shaanxi) to Hezuo (Gansu) Expressway is an important part of the expressway in Gansu Province. The d geological hazard along the expressway, debris flow, seriously threatens the safety construction and later operation of the proposed highway. Taking the debris flow along the proposed expressway as the research object, this paper comprehensively collects the geological data of the area, studies its characteristics of distribution, formation and movement on that basis, and summarizes its formation conditions and mechanism, then evaluates its susceptibility. This paper is a certain reference for the study of debris flow in the region and the governance over it in next step.

Debris flow susceptibility and propagation assessment in West Koyulhisar, Turkey

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 decision-makers 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.

Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway

The China–Pakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway.