Prediction of the volume of shallow landslides due to rainfall using data-driven models
<p>Landslides due to rainfall are among the most destructive natural disasters and cause property damage, huge financial losses, and human deaths in different parts of the world. To plan for mitigation and resilience and to understand the relationship between the volume of soil materials debri...
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Copernicus Publications
2025-04-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/25/1481/2025/nhess-25-1481-2025.pdf |
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| author | J. Tuganishuri C.-Y. Yune G. Kim S. W. Lee M. D. Adhikari S.-G. Yum |
| author_facet | J. Tuganishuri C.-Y. Yune G. Kim S. W. Lee M. D. Adhikari S.-G. Yum |
| author_sort | J. Tuganishuri |
| collection | DOAJ |
| description | <p>Landslides due to rainfall are among the most destructive natural disasters and cause property damage, huge financial losses, and human deaths in different parts of the world. To plan for mitigation and resilience and to understand the relationship between the volume of soil materials debris and their associated predictors, prediction of the volume of rainfall-induced landslides is essential. The objectives of this research are to construct a model using advanced data-driven algorithms (i.e., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), <span class="inline-formula"><i>k</i></span>-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. Models were trained and tested on a South Korean landslide dataset, with the EGB predictions yielding the highest coefficient of determination (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.8841) and the lowest mean absolute error (MAE <span class="inline-formula">=</span> 146.6120 m<span class="inline-formula"><sup>3</sup></span>), followed by RF predictions (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.8435, MAE <span class="inline-formula">=</span> 330.4876 m<span class="inline-formula"><sup>3</sup></span>), on the holdout set. The DNN, EGB, and RF models exhibited <span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">></span> 0.8 on both the training and the test sets. The differences in the coefficient of determination <span class="inline-formula"><i>R</i><sup>2</sup></span> on the training and holdout set were 1.75 %, 7.72 %, and 12.17 % for RF, EGB, and DNN, respectively, signifying that these models could yield reliable volume estimates in adjacent areas with similar geomorphological and environmental settings. The volume of landslides was strongly influenced by slope length, maximum hourly rainfall, slope angle, aspect, and altitude. The anticipated volume of landslides can be important for land use allocation and efficient landslide risk management.</p> |
| format | Article |
| id | doaj-art-9844b077027b43bf8bdacf56237d1b34 |
| institution | OA Journals |
| issn | 1561-8633 1684-9981 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj-art-9844b077027b43bf8bdacf56237d1b342025-08-20T02:18:36ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812025-04-01251481149910.5194/nhess-25-1481-2025Prediction of the volume of shallow landslides due to rainfall using data-driven modelsJ. Tuganishuri0C.-Y. Yune1G. Kim2S. W. Lee3M. D. Adhikari4S.-G. Yum5Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South KoreaDepartment of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South KoreaDepartment of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South KoreaDepartment of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South KoreaDepartment of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South KoreaDepartment of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea<p>Landslides due to rainfall are among the most destructive natural disasters and cause property damage, huge financial losses, and human deaths in different parts of the world. To plan for mitigation and resilience and to understand the relationship between the volume of soil materials debris and their associated predictors, prediction of the volume of rainfall-induced landslides is essential. The objectives of this research are to construct a model using advanced data-driven algorithms (i.e., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), <span class="inline-formula"><i>k</i></span>-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. Models were trained and tested on a South Korean landslide dataset, with the EGB predictions yielding the highest coefficient of determination (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.8841) and the lowest mean absolute error (MAE <span class="inline-formula">=</span> 146.6120 m<span class="inline-formula"><sup>3</sup></span>), followed by RF predictions (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.8435, MAE <span class="inline-formula">=</span> 330.4876 m<span class="inline-formula"><sup>3</sup></span>), on the holdout set. The DNN, EGB, and RF models exhibited <span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">></span> 0.8 on both the training and the test sets. The differences in the coefficient of determination <span class="inline-formula"><i>R</i><sup>2</sup></span> on the training and holdout set were 1.75 %, 7.72 %, and 12.17 % for RF, EGB, and DNN, respectively, signifying that these models could yield reliable volume estimates in adjacent areas with similar geomorphological and environmental settings. The volume of landslides was strongly influenced by slope length, maximum hourly rainfall, slope angle, aspect, and altitude. The anticipated volume of landslides can be important for land use allocation and efficient landslide risk management.</p>https://nhess.copernicus.org/articles/25/1481/2025/nhess-25-1481-2025.pdf |
| spellingShingle | J. Tuganishuri C.-Y. Yune G. Kim S. W. Lee M. D. Adhikari S.-G. Yum Prediction of the volume of shallow landslides due to rainfall using data-driven models Natural Hazards and Earth System Sciences |
| title | Prediction of the volume of shallow landslides due to rainfall using data-driven models |
| title_full | Prediction of the volume of shallow landslides due to rainfall using data-driven models |
| title_fullStr | Prediction of the volume of shallow landslides due to rainfall using data-driven models |
| title_full_unstemmed | Prediction of the volume of shallow landslides due to rainfall using data-driven models |
| title_short | Prediction of the volume of shallow landslides due to rainfall using data-driven models |
| title_sort | prediction of the volume of shallow landslides due to rainfall using data driven models |
| url | https://nhess.copernicus.org/articles/25/1481/2025/nhess-25-1481-2025.pdf |
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