An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea

Lee, Deukhwan; Cheon, Enok; Lim, Hwan-Hui; Choi, Si–Kyung; Kim, Yun-Tae; Lee, Seung-Rae

doi:10.1016/j.enggeo.2020.105979

Cited by 27 publications

(15 citation statements)

References 32 publications

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“…Most of the debris flows that occur in valleys are large-scale upstream collapse-entrained debris flows. Debris flow produces soil and rock that erode river banks and stream beds in valleys [10], [13].…”

Section: ) Loose Soil and Sandmentioning

confidence: 99%

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A Hybrid Fuzzy and K-Nearest Neighbor Approach for Debris Flow Disaster Prevention

Pan²,

Chang³

et al. 2022

IEEE Access

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Taiwan is located in a high-risk area for natural disasters. In recent years, violent natural disasters have occurred in Taiwan. Numerous disasters-such as flooding, surges of river water level, and earth and rock disasters-are caused by instant heavy rainfall. These disasters cause considerable loss of lives and property. Current disaster warning systems can only provide warnings to large areas and not to specific small areas. Therefore, the current study developed a disaster warning system based on machine learning for evaluating the likelihood of earth and rock disasters so that an early warning can be provided to people who may be affected by these disasters. In contrast to previous relevant studies, which have mostly used regional assessment methods, no large-scale regional simulation was conducted in the present study. Instead, a comprehensive debris flow evaluation model based on information related to soil flow, rock flow, typhoons, and rainfall history was established to provide warnings regarding debris flow disasters. The geological condition, rainfall, soil moisture and river water level in 1-h intervals were evaluated using the K-nearest neighbor algorithm, providing people earth and rock flow information for the area around their homes. Data related to Typhoon Kameiji, Typhoon Xinleke, Typhoon Morak, Typhoon Sura, Typhoon Megi, and the 0823 Tropical depression were used as training data for the developed model, and data related to Typhoon Megi and Typhoon Kangrui were used as testing data. The proposed model can provide earlier warnings than can the Taiwanese government's soil and stone flow warning system. The developed model was used to create a mobile phone application that presents comprehensive and easy-to-understand data on the debris flow warning level, hourly rainfall, total rainfall, and geological conditions in real time.INDEX TERMS K-nearest neighbor algorithm, fuzzy algorithm, debris flow, rainfall.

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Section: ) Loose Soil and Sandmentioning

confidence: 99%

“…After soil sand and water are mixed, the mixture flows down the slope under the influence of gravity [8]. The presence of abundant rainfall reduces the friction acting on moving bodies such that debris flows are built [10], [11].…”

Section: ) Abundant Rainfallmentioning

confidence: 99%

A Hybrid Fuzzy and K-Nearest Neighbor Approach for Debris Flow Disaster Prevention

Pan²,

Chang³

et al. 2022

IEEE Access

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“…In addition, Kern said that future work should use machine learning models to improve debris flow volume prediction [ 16 ]. Lee used artificial neural network (ANN) to predict the volume of debris flow under extreme rainfall in central South Korea [ 17 ]. The prediction results provide a favorable reference for the control design of debris flow-prone areas in South Korea.…”

Section: Introductionmentioning

confidence: 99%

Debris flow volume prediction model based on back propagation neural network optimized by improved whale optimization algorithm

Ni,

Li,

Lin

et al. 2024

PLoS ONE

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Debris flow is a sudden natural disaster in mountainous areas, which seriously threatens the lives and property of nearby residents. Therefore, it is necessary to predict the volume of debris flow accurately and reliably. However, the predictions of back propagation neural networks are unstable and inaccurate due to the limited dataset. In this study, the Cubic map optimizes the initial population position of the whale optimization algorithm. Meanwhile, the adaptive weight adjustment strategy optimizes the weight value in the shrink-wrapping mechanism of the whale optimization algorithm. Then, the improved whale optimization algorithm optimizes the final weights and thresholds in the back propagation neural network. Finally, to verify the performance of the final model, sixty debris flow gullies caused by earthquakes in Longmenshan area are selected as the research objects. Through correlation analysis, 4 main factors affecting the volume of debris flow are determined and inputted into the model for training and prediction. Four methods (support vector machine regression, XGBoost, back propagation neural network optimized by artificial bee colony algorithm, back propagation neural network optimized by grey wolf optimization algorithm) are used to compare the prediction performance and reliability. The results indicate that loose sediments from co-seismic landslides are the most important factor influencing the flow of debris flows in the earthquake area. The mean absolute percentage error, mean absolute error and R2 of the final model are 0.193, 29.197 × 104 m3 and 0.912, respectively. The final model is more accurate and stable when the dataset is insufficient and under complexity. This is attributed to the optimization of WOA by Cubic map and adaptive weight adjustment. In general, the model of this paper can provide reference for debris flow prevention and machine learning algorithms.

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“…Reichenbach et al [ 6 ] have conducted a cluster study on the thematic parameters for landslide prediction and it has reported that more than 70% of parameters are relevant to topographic information and they significantly impact the accuracy of landslide prediction. Accordingly, topographic information of landslide-prone areas plays a critical role in the landslide-related analyses—not only the occurrence prediction, flow analysis and vulnerability analysis, but also the countermeasure installations [ [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] ].…”

Section: Introductionmentioning

confidence: 99%

Acquisition of high-resolution topographic information in forest environments using integrated UAV-LiDAR system: System development and field demonstration

Choi,

Ramirez,

Kwon

2023

Heliyon

Self Cite

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An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea

Cited by 27 publications

References 32 publications

A Hybrid Fuzzy and K-Nearest Neighbor Approach for Debris Flow Disaster Prevention

A Hybrid Fuzzy and K-Nearest Neighbor Approach for Debris Flow Disaster Prevention

Debris flow volume prediction model based on back propagation neural network optimized by improved whale optimization algorithm

Acquisition of high-resolution topographic information in forest environments using integrated UAV-LiDAR system: System development and field demonstration

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