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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 CNN-based model can achieve more accurate, reliable and practical susceptibility map. This work provides an idea for prevention of debris flow in mountainous lands. [ABSTRACT FROM AUTHOR]

To study the susceptibility of debris flow in Kangding City, the study area was divided into 421 watershed units. Spatial analysis tools in ArcGIS software and SPSS software were used to analyze the internal superposition of evaluation indicators and the correlation between evaluation indicators and debris flows disasters. By screening out the evaluation factors with a high degree of overlap and poor correlation, eight evaluation factors were selected for debris flow susceptibility assessment. These included watershed unit area, melton rate, form factor ratio, collapse and landslides density of catchment, average fractional vegetation cover of catchment, road density of catchment, average stream power index of catchment, and average rainfall during the multi-year flood season. The susceptibility of debris flow was quantitatively evaluated by combining the information value model and the entropy method. The weights of the evaluation indicators were quantitatively determined by the entropy method, and the evaluation factor weighted information quantity value was calculated. Based on this, the debris flow susceptibility in Kangding City was divided into four grades: extremely high, high, medium and low. The results of debris flow susceptibility assessment were tested using the frequency ratio model and the Receiver-Operating Characteristic (ROC) curve, with an AUC curve of 0.842, indicating high accuracy of the evaluation model.

As a natural disaster prone to occur in mountainous areas, geological hazards cause serious economic losses to China every year. In order to reveal the factors influencing of geological hazards susceptibility in typical mountain valley, this paper takes Changdu City as the study area.Firstly, we analyze the spatial distribution of geological hazards, and then divides it into three major watersheds based on the differences of disaster-inducing environments in the study area, selects 10 indicators such as elevation, slope and terrain relief to build a evaluation index system of geological hazard susceptibility , and the weights of index determined based on the Random Forest. The spatial distribution of geological hazards in each watershed of Changdu City is obtained by overlaying with GIS, and it is found that: (1) The types of geological hazards in Changdu City are mainly small disasters, while the distribution of large disasters is relatively small but the hazards are huge and the danger level is high. (2) In general, the factors influencing geological hazards are more or less the same in each basin, but there are still some differences, with the factor of medium altitude and road being more prominent in the Jinsha River basin, the factor of density of settlements being more prominent in the Lancang River basin, and the factor of road being more prominent in the Nujiang River basin.(3) The Jinsha River basin has the largest area of low susceptibility, and the Lancang and Nujiang River basins have the largest area of medium susceptibility; all three basins have the smallest area of high susceptibility, but they are distributed in the whole basin, and mainly in areas with strong human activities and soft lithology.

To study the susceptibility of debris flow in Kangding City, the study area was divided into 421 watershed units. Spatial analysis tools in ArcGIS software and SPSS software were used to analyze the internal superposition of evaluation indicators and the correlation between evaluation indicators and debris flows disasters. By screening out the evaluation factors with a high degree of overlap and poor correlation, eight evaluation factors were selected for debris flow susceptibility assessment. These included watershed unit area, melton rate, form factor ratio, collapse and landslides density of catchment, average fractional vegetation cover of catchment, road density of catchment, average stream power index of catchment, and average rainfall during the multi-year flood season. The susceptibility of debris flow was quantitatively evaluated by combining the information value model and the entropy method. The weights of the evaluation indicators were quantitatively determined by the entropy method, and the evaluation factor weighted information quantity value was calculated. Based on this, the debris flow susceptibility in Kangding City was divided into four grades: extremely high, high, medium and low. The results of debris flow susceptibility assessment were tested using the frequency ratio model and the Receiver-Operating Characteristic (ROC) curve, with an AUC curve of 0.842, indicating high accuracy of the evaluation model. [ABSTRACT FROM AUTHOR]

The geological hazard assessment was carried out by the evaluation train thought the study area of multiple disaster species coupling , which has a complete range of disasters such as collapse, landslide and debris flow. The evaluate unit of collapse, landslide and other slopes are grids and the debris flow disasters evaluate units are watershed, Based on the information model and analytic hierarchy process, the risk assessment is carried out respectively, then, the comprehensive geological disaster risk evaluation results of the study area were obtained by taking the method of large value. In the study area, the area of extremely high and high risk area of comprehensive geological disaster is obviously larger than that of the evaluation results of single hazard. The extremely high and high risk area is mainly located in the cataclastic rock area with relatively developed collapse and landslide and extremely prone debris flow basin. In view of the risk assessment of geological disasters in high mountains and valleys, the idea of dividing first and combining later can more reasonably reflect the morphological and spatial differences of different disaster types in the process of risk assessment, and obtain more accurate risk assessment results.

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Bailong river basin is a densely distributed area of geological disasters such as debris flow. In August 2020, due to the stimulation of heavy rainfall, a large-scale group debris flow disaster occurred in Wudu section of Bailong river basin, causing serious losses. This paper takes the Wudu section (Lianghekou Township, Dangchang County to Jugan Town, Wudu District) of Longnan City in Gansu Province as the research area. Through field investigation, we selected the area of drainage basin, shape coefficient of basin, average slope, density of gully, reference value of material source (HI), lithology, distance between basin center and active fault, one hour maximum rainfall and vegetation coverage as debris flow hazard assessment factors. Taking single gully and small watershed units of debris flow as evaluation units, the regional debris flow hazard assessment is carried out by using ArcGIS software based on the theory of disaster entropy. The results show that most of the debris flows in the study area belong to medium and high hazard debris flow gullies. Some of the most heavily weighted disaster-causing factors like the weight of lithology, source reference value (HI), distance from fault, vegetation coverage and average slope are consistent with the actual investigation results. Moreover, the evaluation results of small watershed units are more consistent with the development of debris flow in the study area.

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Considering the impact of differences in watershed characteristics on river water quality, with the Chaohu Lake Basin as the research object, based on the data of water quality, meteorology, topography, soil, and remote sensing images of the river monitoring points from October 2019 to September 2020, the watershed unit at each monitoring point was divided through digital terrain analysis, and the comprehensive landscape characteristics based on the watershed unit were explored through the comprehensive use of correlation analysis, redundancy analysis, and multiple regression analysis to investigate the influence of comprehensive landscape characteristics based on watershed units (including land use, climate, topography, soil, etc.) on the water quality of rivers around Chaohu Lake. The results showed that:① the water quality of rivers around Chaohu Lake had large spatial differences, with the main pollutants being total nitrogen and ammonia nitrogen. Most of the rivers had total nitrogen concentrations exceeding the Class V water quality standards, and the areas with serious nitrogen and phosphorus pollution were concentrated in the urban area of Hefei and the surrounding rivers, as well as in the middle and lower reaches of the Fengle and Hangbu Rivers. ② The comprehensive landscape characteristics of the watershed unit had a significant impact on the river water quality. Among them, the proportion of built-up land, the density of patches, the dispersion and juxtaposition index, and the Shannon diversity index were positively correlated with the water quality indicators, whereas the proportion of forest and grassland and the spreading index were negatively correlated with the water quality indicators. ③ In different seasons, the effect of the integrated landscape characteristics of the watershed unit on river water quality was stronger in the wet season than in the dry season, which was mainly caused by the difference in precipitation in the dry and wet seasons.

• Consequent, diagonal, transverse and obsequent slope structures and soil stability are incorporated into the DFS assessment. • Unsupervised learning is used to optimize the sampling strategy of negatives. • A state-of-the-art meta-modeling technique is used to optimize supervised learning. • A progressive framework is proposed to improve the reliability of DFS 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. [ABSTRACT FROM AUTHOR]