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Article

Rainfall-Induced Shallow Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms

1
Water Resources Research and Development Center, Ministry of Energy, Water Resources and Irrigation, Government of Nepal, Pulchok, Lalitpur 44700, Nepal
2
Department of Ocean Engineering, Geo-Systems Engineering Laboratory, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, Korea
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(10), 569; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100569
Received: 5 September 2020 / Revised: 20 September 2020 / Accepted: 28 September 2020 / Published: 29 September 2020
Landslides impact on human activities and socio-economic development, especially in mountainous areas. This study focuses on the comparison of the prediction capability of advanced machine learning techniques for the rainfall-induced shallow landslide susceptibility of Deokjeokri catchment and Karisanri catchment in South Korea. The influencing factors for landslides, i.e., topographic, hydrologic, soil, forest, and geologic factors, are prepared from various sources based on availability, and a multicollinearity test is also performed to select relevant causative factors. The landslide inventory maps of both catchments are obtained from historical information, aerial photographs and performed field surveys. In this study, Deokjeokri catchment is considered as a training area and Karisanri catchment as a testing area. The landslide inventories contain 748 landslide points in training and 219 points in testing areas. Three landslide susceptibility maps using machine learning models, i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN), are prepared and compared. The outcomes of the analyses are validated using the landslide inventory data. A receiver operating characteristic curve (ROC) method is used to verify the results of the models. The results of this study show that the training accuracy of RF is 0.756 and the testing accuracy is 0.703. Similarly, the training accuracy of XGBoost is 0.757 and testing accuracy is 0.74. The prediction of DNN revealed acceptable agreement between the susceptibility map and the existing landslides, with a training accuracy of 0.855 and testing accuracy of 0.802. The results showed that the DNN model achieved lower prediction error and higher accuracy results than other models for shallow landslide modeling in the study area. View Full-Text
Keywords: Deep Neural Network; Extreme Gradient Boosting; Random Forest; landslide susceptibility Deep Neural Network; Extreme Gradient Boosting; Random Forest; landslide susceptibility
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MDPI and ACS Style

Pradhan, A.M.S.; Kim, Y.-T. Rainfall-Induced Shallow Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms. ISPRS Int. J. Geo-Inf. 2020, 9, 569. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100569

AMA Style

Pradhan AMS, Kim Y-T. Rainfall-Induced Shallow Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms. ISPRS International Journal of Geo-Information. 2020; 9(10):569. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100569

Chicago/Turabian Style

Pradhan, Ananta M.S., and Yun-Tae Kim. 2020. "Rainfall-Induced Shallow Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms" ISPRS International Journal of Geo-Information 9, no. 10: 569. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100569

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