Debris Flow Susceptibility Assessment Research Articles

A novel framework for debris flow susceptibility assessment considering the uncertainty of sample selection

The uncertainty arising from random sampling of non-debris flow samples significantly impacts the accuracy of debris flow susceptibility assessments (DFSA). This study introduces a novel uncertainty elimination method, Kernel Density Estimation (KDE), and compares it with Mean and Maximum Probability Analysis (MPA) methods. Furthermore, we investigate the responses of four commonly used machine learning models to sampling uncertainty, comparing two structurally similar models (Random Forest (RF) and Extremely Randomized Trees (ERT)) with two structurally different models (Support Vector Machine (SVM) and Multilayer Perceptron (MLP)). The results indicate that the application of these uncertainty elimination methods can significantly enhance AUC values and zoning accuracy, with the KDE method outperforming the others. Specifically, the AUC values based on KDE for RF, ERT, SVM, and MLP are 0.995, 0.999, 0.999, and 0.853, respectively. The corresponding zoning accuracy for these models is 1.00, 1.00, 1.00, and 0.78, respectively. The study further reveals that the responses to sampling uncertainty vary by model architecture: RF, ERT, and SVM typically exhibit bimodal normal distributions, while the MLP model shows a unimodal distribution. Additionally, MLP is more sensitive to variations in negative samples, whereas RF and ERT are less affected due to the ensemble structure.

Semi-supervised deep learning based on label propagation algorithm for debris flow susceptibility assessment in few-label scenarios

Semi-supervised deep learning based on label propagation algorithm for debris flow susceptibility assessment in few-label scenarios

Debris flow susceptibility assessment based on boosting ensemble learning techniques: a case study in the Tumen River basin, China

Debris flow susceptibility assessment based on boosting ensemble learning techniques: a case study in the Tumen River basin, China

Gully-type debris flow susceptibility assessment based on a multi-channel multi-scale residual network fusing multi-source data: a case study of Nujiang Prefecture

Gully-type debris flow susceptibility assessment based on a multi-channel multi-scale residual network fusing multi-source data: a case study of Nujiang Prefecture

A progressive framework combining unsupervised and optimized supervised learning for debris flow susceptibility assessment

This research aims to improve the reliability of debris flow susceptibility (DFS) assessment, which is crucial for disaster prevention and mitigation in mountainous regions. A progressive framework was proposed and applied to Pinggu District, Beijing, China. First, 16 debris flow predisposing factors (DFPFs) were selected, and the slope structure and soil stability were incorporated to account for material sources. Watershed units instead of grid units were used to extract data. Then, the multi-collinearity among the factors was reduced by using variance inflation factors (VIF) and information gain (IG), and 13 DFPFs were retained. Three unsupervised learning algorithms (i.e., affinity propagation (AP), Gaussian mixture model (GMM) and self-organizing maps (SOM)) were used to optimize the sampling strategy of non-debris flow units. Subsequently, the occurrence probability of debris flows in each unit was predicted by using four supervised learning algorithms (i.e., logistic regression (LR), random forest (RF), adaptive boosting (ADAB) and extreme gradient boosting (XGB)). They were optimized by a state-of-the-art meta-modeling approach. Finally, the DFPF’s importance was ranked. The main contributions of our framework are establishing a high-quality data set and optimizing the prediction algorithms. The results show that the tree-based models perform well, and the boosting-based algorithms outperform the bagging-based algorithms. Supervised learning is more suitable for DFS assessment than unsupervised learning. Debris flows are most likely to occur on a dolomite consequent or diagonal slope with a relief amplitude above 540 m.

Debris-flow susceptibility assessment in Dongchuan using stacking ensemble learning including multiple heterogeneous learners with RFE for factor optimization

Debris-flow susceptibility assessment in Dongchuan using stacking ensemble learning including multiple heterogeneous learners with RFE for factor optimization

Debris flow susceptibility assessment based on information value and machine learning coupling method: from the perspective of sustainable development.

Accurately assessing the susceptibility of debris flow disasters is of great significance for reducing the cost of disaster prevention and mitigation, as well as disaster losses. Machine learning (ML) models have been widely used in the susceptibility assessment of debris flow disasters. However, these models often have randomness in the selection of non-disaster data, which can lead to redundant information and poor applicability and accuracy of susceptibility evaluation results. To address this issue, this paper focuses on debris flow disasters in Yongji County, Jilin Province, China; optimizes the sampling method of non-disaster datasets in machine learning susceptibility assessment; and proposes a susceptibility prediction model that couples information value (IV) with artificial neural network (ANN) and logistic regression (LR) models. A debris flow disaster susceptibility distribution map with higher accuracy was drawn based on this model. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), information gain ratio (IGR), and typical disaster point verification methods. The results show that the rainfall and topography were found to be decisive factors in the occurrence of debris flow disasters, and the IV-ANN model established in this study had the highest accuracy (AUC = 0.968). Compared to traditional machine learning models, the coupling model produced an increase in economic benefit of about 25% while reducing the average disaster prevention and control investment cost by about 8%. Based on model's susceptibility map, this paper proposes practical disaster prevention and control suggestions that promote sustainable development in the region, such as establishing monitoring systems and information platforms to aid disaster management.

Debris Flow Gully Classification and Susceptibility Assessment Model Construction

The location of debris flow occurring in a gully determines the observable differences in its formation, evolution and effects. In this piece of research, we propose a new method for debris flow gully classification based on the locations of the debris flows occurring in the gullies. It is termed the three-section method (TSM). It includes eight different types of gullies with different digital identifications (IDs) and susceptibility degrees (SDs). By taking the Jiangjia Gully (JJG), in Yunnan, China, as a case study site, the main gully and the sub-gullies at different levels were identified using a hydrological analysis method. Then, the gullies were divided into different types using the new classification TSM. The results show that there are seven different types of debris flow gullies in the JJG. The number of different types varied greatly in gullies at different levels. In particular, the topological diagram of debris flow gullies was drawn after simplifying the shape of the gullies, and it was a good way to understand the characteristics of debris flow gullies. Finally, the relationships were explored between the hypsometric integrals (HIs), surface exposures (SEs) and susceptibility degrees (SDs), and a new calculation model construction method for determining the degree of debris flow susceptibility was proposed. This model, using the above method, can not only be used to calculate the SDs of debris flows in the gullies, but can also be instrumental in pointing out the approximate locations of the debris flow commonly and easily occurring in the gullies. We hope that our research can provide a new concept for the assessment of debris flow susceptibility.

Comparison of Machine Learning and Traditional Statistical Methods in Debris Flow Susceptibility Assessment: A Case Study of Changping District, Beijing

As a common geological hazard, debris flow is widely distributed around the world. Meanwhile, due to the influence of many factors such as geology, geomorphology and climate, the occurrence frequency and main inducing factors are different in different places. Therefore, the evaluation of debris flow sensitivity can provide a very important theoretical basis for disaster prevention and control. In this research, 43 debris flow gullies in Changping District, Beijing were cataloged and studied through field surveys and the 3S technology (GIS (Geography Information Systems), GPS (Global Positioning Systems), RS (Remote Sensing)). Eleven factors, including elevation, slope, plane curvature, profile curvature, roundness, geomorphic information entropy, TWI, SPI, TCI, NDVI and rainfall, were selected to establish a comprehensive evaluation index system. The watershed unit is directly related to the development and activities of debris flow, which can fully reflect the geomorphic and geological environment of debris flow. Therefore, the watershed unit was selected as the basic mapping unit to establish four evaluation models, namely ACA–PCA–FR (Analytic Hierarchy Process–Principal Component Analysis–Frequency Ratio), FR (Frequency Ratio), SVM (Support Vector Machines) and LR (Logistic Regression). In other words, this research evaluates debris flow susceptibility by comparingit with two traditional weight methods (ACA–PCA–FR and FR) and two machine learning methods (SVM and LR). The results show that the SVM evaluation model is superior to the other three models, and thevalueofthe area under the receiver-operating characteristic curve (AUC) is 0.889 from the receiver operating characteristic curve (ROC). It verifies that the SVM model has strong adaptability to small sample data. The study was divided into five regions, which were very low, low, moderate, high and very high, accounting for 22.31%, 25.04%, 17.66%, 18.85% and 16.14% of the total study area, respectively, by SVM model. The results obtained in this researchagree with the actual survey results, and can provide theoretical help for disaster prevention and reduction projects.

Debris Flow Susceptibility Assessment of Sichuan-Tibet Railway Based on GIS and CF-LR Coupling Model

Sichuan-Tibet Railway is an important transportation system in China, which is of great significance to promote the communication between Tibet and the mainland. This paper takes the lin-chang section of the Sichuan-Tibet railway with a width of 2km on both sides as the research area. The occurrence of debris flow must have appropriate solid source, water source and topographic conditions. In this paper, ten disaster-causing factors are selected, including relief degree of land surface, profile curvature, silty soil, distance to the rive, distance to the fault, elevation, slope aspect, NDVI, average annual rainfall and land utilization rate. The information of disaster-causing factors was extracted from Arcgis and evaluated by the certainty factor model. The probability of debris flow in the study area was calculated by the coupling model of binary Logistic regression model combined with certainty factor model. ROC curve analysis of the CF-LR model shows that AUC is 0.89, indicating that the model has a good effect. The distribution map of debris flow susceptibility in the study area was obtained by using grid calculator in GIS. The results show that under the CF-LR model, extremely low, low, medium and high prone areas cover 2547km₂, 235km₂, 246km₂ and 1099km₂ respectively, accounting for 61.72%, 5.69%, 5.96% and 26.63% of the total area of the study area, respectively. The high risk areas are mainly in Milin, Nyingchi and Bomi counties. This study provides an important reference for assessing the risk of debris flow geological disaster on Sichuan-Tibet Railway.

Debris flow susceptibility assessment of Leh Valley, Ladakh, based on concepts of connectivity, propagation and evidence-based probability

The Leh Valley which lies within the Trans Himalayan state of Ladakh, India, is known to be affected almost annually by debris flows ranging from minor to catastrophic scale events. The effect has been getting magnified due to increased urbanization and rapid growth in tourism industry. Though these flows are triggered by intense and abnormal rainfall events the conditioning factor has always been the topography and sediment availability. A lucid acknowledgement of the terrain condition and the degree of vulnerability of such events is required. For this a detail investigation of sediment availability, topographic conditions and their relation with known events becomes crucial. This study utilizes index of connectivity (IC) model to understand the sediment source-sink relationship and farther applied Flow-R model to simulate the probable scenario of events through predefined algorithms. We then use the Weights of evidence (WOE) method to compute the statistical probability of debris flow occurrence. This paper demonstrates the application of these three independent techniques and their implementation in a highly rugged terrain of Ladakh which is a region of frequent debris flows onslaught. The IC and Flow-R models are found to be counter supportive and effective in delineating areas which could be affected by flows that will solely originate in upstream areas where high angle channels directly connected to sediment sources are present. WOE-based model determines the probability of the rare and extensive flows that results from downward integration of other drainage networks in an open fan area.

Debris-Flow Susceptibility Assessment in China: A Comparison between Traditional Statistical and Machine Learning Methods

Debris flows, triggered by dual interferences extrinsically and intrinsically, have been widespread in China. The debris-flow susceptibility (DFS) assessment is acknowledged as the benchmark for the mitigation and prevention of debris flow risks, but DFS assessments at the national level are lacking. The role of human activities in the DFS assessment has always been overlooked. On the basis of a detailed inventory of debris-flow sites and a large set of environmental and human-related characteristics, this research presents the comparative performance of the well-known information value (IV), logistic regression (LR) and random forest (RF) models for DFS assessments in China. Twelve causative factors, namely, elevation, slope, aspect, rainfall, the normalized difference vegetation index (NDVI), land use, landform, geology, distance to faults, density of villages, distance to rivers and distance to roads, were considered. Debris-flow susceptibility maps were then generated after the nonlinear relationship between the debris-flow occurrence and the causative factors was captured. Finally, the predictive performance of the three maps was evaluated through receiver operating characteristic (ROC) curves, and the validation results showed that areas under the ROC curves were 81.98%, 79.96% and 97.38% for the IV, LR and RF models, respectively, indicating that the RF model outperformed the other two traditional statistical methods. The importance ranking of the RF model also revealed that distance to roads, slope and rainfall dominated the spatial distribution of debris flows. This is the first experiment to compare between the traditional statistical and machine learning methods in DFS studies for the whole of China. Our results could provide some empirical support for China’s policymakers and local practitioners in their efforts to enable residents to be less vulnerable to disasters.

Susceptibility Assessment of Debris Flows Coupled with Ecohydrological Activation in the Eastern Qinghai-Tibet Plateau

The eastern margin of the Qinghai-Tibet Plateau is an extreme topography transition zone, and characterized by significant vegetation zonation, in addition to geographic features (such as enormous topographic relief and active tectonics) that control the occurrence of debris flows, which are rapid, surging flows of water-charged clastic sediments moving along a steep channel and are one of the most dangerous mountain hazards in this region. There is thus an urgent need in this region to conduct a regional-scale debris flow susceptibility assessment to determine the spatial likelihood of a debris flow occurrence and guarantee the safety of people and property, in addition to the smooth operation of the Sichuan-Tibet transport corridor. It is, however, a challenging task to estimate the region’s debris flow susceptibility while taking into consideration the comprehensive impacts of vegetation on the occurrence of debris flows, such as the positive effect of root anchoring and the negative effect of vegetation weight loads. In this study, a novel regional-scale susceptibility assessment method was constructed by integrating state-of-the-art machine learning algorithms (such as support vector classification (SVC), random forest (RF), and eXtreme Gradient Boosting (XGB)) with the removing outliers (RO) algorithm and particle swarm optimization (PSO), allowing the impacts of vegetation on debris flow initiation to be integrated with the topographical conditions, hydrological conditions, and geotechnical conditions. This method is finally applied to assess the regional-scale susceptibility of debris flows in the Dadu River basin on the eastern margin of the Qinghai-Tibet Plateau. The study results show that (i) all hybrid machine learning techniques can effectively predict the occurrence of debris flows in the extreme topography transition zone; (ii) the hybrid machine learning technique RO-PSO-SVC has the best performance, and its accuracy (ACC) is 0.946 and the area under the ROC curve (AUC) is 0.981; (iii) the RO-PSO algorithm improves SVC, RF, and XGB performance (according to the ACC value) by 3.84%, 2.59%, and 5.94%, respectively; and (iv) the contribution rate of ecology-related variables is almost only one-tenth that of topography- and hydrology-related factors, according to the factor important analysis for RO-PSO-SVC. Furthermore, debris flow susceptibility maps for the Dadu River basin were created, which can be used to assess and mitigate debris flow hazards.

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.

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

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 Flow Susceptibility Assessment Using the Integrated Random Forest Based Steady-State Infinite Slope Method: A Case Study in Changbai Mountain, China

Debris flow events often pose significant damage and are a threat to infrastructure and even livelihoods. Recent studies have mainly focused on determining the susceptibility of debris flow using deterministic or heuristic/probabilistic models. However, each type of model has its own significant advantages with some irreparable disadvantages. The random forest model, which is sensitive to the region where the terrain conditions are suitable for the occurrence of debris flow, was applied along with the steady-state infinite slope method, which is capable of describing the initiation mechanism of debris flow. In this manner, a random-forest-based steady-state infinite slope method was used to conduct susceptibility assessment of debris-flow at Changbai mountain area. Results showed that the assessment accuracy of the proposed random-forest-based steady-state infinite slope method reached 90.88%; however, the accuracy of just the random forest model or steady-state infinite slope method was only 88.48% or 60.45%, respectively. Compared with the single-model assessment results, the assessment accuracy of the proposed method improved by 2.4% and 30.43%, respectively. Meanwhile, the debris-flow-prone area of the proposed method was reduced. The random-forest-based steady-state infinite slope method inherited the excellent diagnostic performance of the random-forest models in the region where the debris flow disaster already occurred; meanwhile, this method further refined the debris-flow-prone area from the suitable terrain area based on physico-mechanical properties; thus, the performance of this method was better than those of the other two models.

Debris Flow Susceptibility Assessment and Runout Prediction: A Case Study in Shiyang Gully, Beijing, China

At 14:00 on 18 June 2017, floods occurred in Shiyang gully at the junction of Beijing and Hebei Province, China. The floods put 12 people at risk, 6 of whom died. After the occurrence of the floods, the environment in Shiyang gully has undergone tremendous changes. There are many loose residual materials in the watershed. Therefore, it was necessary to evaluate the debris flow susceptibility of Shiyang gully after the disaster. The factor analysis method, entropy weighting method and geomorphic information entropy method were used to evaluate the debris flow susceptibility of each sub-watershed in Shiyang gully. By comparison with the technical standard published by the China Ministry of Lands and Resources, it was found that the susceptibility assessment results are not ideal. To improve the accuracy of the assessment results, we used a comprehensive method. The accuracy of the susceptibility assessment obtained by the comprehensive method is 87.5%. This accuracy shows that the comprehensive method is applicable to the susceptibility assessment of debris flows in this region. To predict the runout path of potential debris flows in four sub-watersheds with very high susceptibility, the authors used the SFLOW software developed by Jilin University to simulate the debris flow hazards. Combined with the on-site investigation data, debris flows in the four sub-watersheds were simulated at 10-, 20-, 50-, and 100-year recurrence intervals. The obtained runout path could provide some guidance and reference for the avoidance of disasters and treatment of debris flows.

A spatial model for the assessment of debris flow susceptibility along the Kodaikkanal-Palani traffic corridor

Debris flow is one of the most destructive water related mass movements that affects the development of mountain terrains. A reliable assessment of debris flow susceptibility requires adequate data, but in most developing countries like India, there is a dearth of such extensive scientific records. This study presents a novel approach for assessing debris flow using the analytical network process (ANP) in data insufficient regions. A stretch of hill road between Kumburvayal and Vadakaunchi along the Kodaikkanal-Palani Traffic Corridor (M171) was considered for this study. Five significant factors including the nature of slope forming materials, hydraulic conductivity, slope, vegetation, and drainage density were identified from intense field surveys and inspections in order to assess the susceptibility of the terrain to debris flow. This model endorsed the inter-dependencies between the selected factors. The resulting debris flow susceptibility map delineated regions highly prone to debris flow occurrences, which constituted nearly 23% of the selected road stretch.

A modified Logit model for assessment and validation of debris-flow susceptibility

The accuracy of a debris-flow susceptibility assessment model can not be achieved to an acceptable level without rigorously selecting/processing model input parameters and reliably validating the results with ground truth. In this study, a modified logistic regression model was applied to examine the debris-flow susceptibility in the semi-arid mountainous areas of the southeast Tibetan Plateau, taking the case of the Benzilan-Changbo segment of the Jinsha River. The logistic regression model validation was supported by a field investigation and interpreting aerial photographs. The frequency ratio method was adapted for the sensitivity analysis of ten predisposing factors, including a factor of human activities that has never been considered in previous studies for the selected study area. The model with the highest prediction accuracy (83%) was obtained by comparing 55 possible combinations of the ten factors evaluated using a receiver operating characteristic curve technique. Composite analysis and random sample testing were also conducted to verify the reliability of the susceptibility assessment. The prediction results showed that the actual debris-flow area accounted for 92% of the total predicted debris-flow area. In summary, this study reveals that: (1) the highest susceptibility area was mainly distributed in the northeast and middle riparian zones, partially scattered in the southwest. Human activity was found to be closely related to the probability of debris flow occurrences; (2) under the two situations of independent and index combination participation evaluation, the performances of the same related factors showed largely different effects on the assessment results, which reflected the interrelation and interaction of the factors causing the debris flow; (3) the accuracy of the modified logistic regression model increased by 15%, when compared with the index entropy model in a previous study, and the reasons for this were discussed in the current study. The modified model, along with its enhanced validation process, could be easily extended to other regional mass movement susceptibility analyses.