AI-based rainfall prediction model for debris flows

Zhao, Yan; Meng, Xingmin; Qi, Tianjun; Li, Yajun; Chen, Guan; Yue, Dongxia; Qing, Feng

doi:10.1016/j.enggeo.2021.106456

Cited by 36 publications

(15 citation statements)

References 52 publications

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“…e comprehensive analysis shows that the average unit weight of debris flow in the study area was c c � 1.602 t/m 3 , and the density is moderate, belonging to rare debris flow. At the same time, according to the morphological investigation method, the fluid and motion characteristics of debris flow are described by the affected villagers [29]. It is concluded that the fluid properties of debris flow should be between dense debris flow and 6…”

Section: Unit Weight Of the Debris Flowmentioning

confidence: 99%

Risk Assessment of Debris Flow in Huyugou River Basin Based on Machine Learning and Mass Flow

2022

Mobile Information Systems

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The Huyugou river basin is a typical debris flow river basin in the Shanxi Province, which has great harm after the outbreak and seriously affects the safety of people’s lives and property. Therefore, it is urgent to carry out debris flow risk assessment. In this paper, a machine learning algorithm is implemented to assess the disaster susceptibility of each branch gully in a river basin of the Huyugou. Furthermore, its high-susceptibility branch gully and main gully were selected as the starting points of debris flow simulation for numerical simulation. The machine learning algorithm is implemented in a cloud-edge platform to minimize the model training and prediction times. Under the simulated rainfall conditions of major debris flow disasters, e.g., the one that occurred in 1996, the accuracy rate reached 84%. The results show that the debris flow susceptibility of each branch gully in the study area is mainly affected by the peak flow rate of the river basin, the length of the main gully, and the relative height difference of the river basin. The total risk area of debris flow is 1.91 × 105 m2, and the high-risk area accounts for 52.18% of the total area. It is mainly located in the upper part of the main gully accumulation area and the confluence of each channel and the main gully. The middle-risk area accounts for 36.14% of the total area, and the low-risk area accounts for less. We also observed significant reduction, from 34.68% to 36.98%, in the training and prediction times of the machine learning models when implemented over the proposed edge-cloud framework. The reappearance of debris flow in the study area is relatively accurate, which provides a certain scientific basis for the risk assessment of debris flow in the future.

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Section: Unit Weight Of the Debris Flowmentioning

confidence: 99%

Risk Assessment of Debris Flow in Huyugou River Basin Based on Machine Learning and Mass Flow

2022

Mobile Information Systems

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“…The Bailong River Basin is in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau, with elevations ranging from 406 to 4457 m [45]. Structurally, it is located on the eastern boundary of the Indian-Asian plate collision zone [46].…”

Section: Study Areamentioning

confidence: 99%

Modeling the Spatial Distribution of Debris Flows and Analysis of the Controlling Factors: A Machine Learning Approach

Zhao

Meng

et al. 2021

Remote Sensing

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Debris flows are a major geological hazard in mountainous regions. For improving mitigation, it is important to study the spatial distribution and factors controlling debris flows. In the Bailong River Basin, central China, landslides and debris flows are very well developed due to the large differences in terrain, the complex geological environment, and concentrated rainfall. For analysis, 52 influencing factors, statistical, machine learning, remote sensing and GIS methods were used to analyze the spatial distribution and controlling factors of 652 debris flow catchments with different frequencies. The spatial distribution of these catchments was divided into three zones according to their differences in debris flow frequencies. A comprehensive analysis of the relationship between various factors and debris flows was made. Through parameter optimization and feature selection, the Extra Trees classifier performed the best, with an accuracy of 95.6%. The results show that lithology was the most important factor controlling debris flows in the study area (with a contribution of 26%), followed by landslide density and factors affecting slope stability (road density, fault density and peak ground acceleration, with a total contribution of 30%). The average annual frequency of daily rainfall > 20 mm was the most important triggering factor (with a contribution of 7%). Forest area and vegetation cover were also important controlling factors (with a total contribution of 9%), and they should be regarded as an important component of debris flow mitigation measures. The results are helpful to improve the understanding of factors influencing debris flows and provide a reference for the formulation of mitigation measures.

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“…Machine learning is helpful for quantitatively evaluating the potential relationship between landslide spatial distribution and various influencing factors through the interpretability of the model [70,71]. Correlation and statistical analysis between the landslide distribution and the influencing factors were conducted on the four datasets, including the unclassified landslides and landslide classification based on relative age, area, and type of movement.…”

Section: Correlation Analysis Of Factorsmentioning

confidence: 99%

Distribution Modeling and Factor Correlation Analysis of Landslides in the Large Fault Zone of the Western Qinling Mountains: A Machine Learning Algorithm

Zhao

Meng

et al. 2021

Remote Sensing

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The area comprising the Langma-Baiya fault zone (LBFZ) and the Bailongjiang fault zone (BFZ) in the Western Qinling Mountains in China is characterized by intensive, frequent, multi-type landslide disasters. The spatial distribution of landslides is affected by factors, such as geological structure, landforms, climate and human activities, and the distribution of landslides in turn affects the geomorphology, ecological environment and human activities. Here, we present the results of a detailed landslide inventory of the area, which recorded a total of 2765 landslides. The landslides are divided into three categories according to relative age, area, and type of movement. Sixteen factors related to geological structure, geomorphology, materials composition and human activities were selected and four machine learning algorithms were used to model the spatial distribution of landslides. The aim was to quantitatively evaluate the relationship between the spatial distribution of landslides and the contributing factors. Based on a comparison of model accuracy and the Receiver Operating Characteristic (ROC) curve, RandomForest (RF) (accuracy of 92%, area under the ROC of 0.97) and GradientBoosting (GB) (accuracy of 96%, area under the ROC curve of 0.97) were selected to predict the spatial distribution of unclassified landslides and classified landslides, respectively. The evaluation results reveal the following. The vegetation coverage index (NDVI) (correlation of 0.2, and the same below) and distance to road (DTR) (0.13) had the highest correlations with the distribution of unclassified landslides. NDVI (0.18) and the annual precipitation index (API) (0.14) had the highest correlations with the distribution of landslides of different ages. API (0.16), average slope (AS) (0.14) and NDVI (0.1) had the highest correlations with the landslide distribution on different scales. API (0.28) had the highest correlation with the landslide distribution based on different types of landslide movement.

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AI-based rainfall prediction model for debris flows

Cited by 36 publications

References 52 publications

Risk Assessment of Debris Flow in Huyugou River Basin Based on Machine Learning and Mass Flow

Risk Assessment of Debris Flow in Huyugou River Basin Based on Machine Learning and Mass Flow

Modeling the Spatial Distribution of Debris Flows and Analysis of the Controlling Factors: A Machine Learning Approach

Distribution Modeling and Factor Correlation Analysis of Landslides in the Large Fault Zone of the Western Qinling Mountains: A Machine Learning Algorithm

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