Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method

Sunil Saha; Alireza Arabameri; Anik Saha; Thomas Blaschke; Phuong Thao Thi Ngo; Viet Ha Nhu; Shahab S Band

doi:10.1016/j.scitotenv.2020.142928

Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method

Sci Total Environ. 2021 Apr 10:764:142928. doi: 10.1016/j.scitotenv.2020.142928. Epub 2020 Oct 20.

Authors

Sunil Saha¹, Alireza Arabameri², Anik Saha¹, Thomas Blaschke³, Phuong Thao Thi Ngo⁴, Viet Ha Nhu⁵, Shahab S Band⁶

Affiliations

¹ Department of Geography, University of Gour Banga, West Bengal 732103, India.
² Department of Geomorphology, Tarbiat Modares University, Tehran 14115-111, Iran. Electronic address: alireza.ameri91@yahoo.com.
³ Department of Geoinformatics - Z_GIS, University of Salzburg, 5020 Salzburg, Austria. Electronic address: thomas.blachke@sbg.ac.at.
⁴ Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, Viet Nam. Electronic address: ngotphuongthao5@duytan.edu.vn.
⁵ Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. Electronic address: nhuvietha@tdtu.edu.vn.
⁶ Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC. Electronic address: shamshirbands@yuntech.edu.tw.

PMID: 33127137
DOI: 10.1016/j.scitotenv.2020.142928

Abstract

The present research examines the landslide susceptibility in Rudraprayag district of Uttarakhand, India using the conditional probability (CP) statistical technique, the boost regression tree (BRT) machine learning algorithm, and the CP-BRT ensemble approach to improve the accuracy of the BRT model. Using the four fold of data, the models' outcomes were cross-checked. The locations of existing landslides were detected by general field surveys and relevant records. 220 previous landslide locations were obtained, presented as an inventory map, and divided into four folds to calibrate and authenticate the models. For modelling the landslide susceptibility, twelve LCFs (landslide conditioning factors) were used. Two statistical methods, i.e. the mean absolute error (MAE) and the root mean square error (RMSE), one statistical test, i.e. the Freidman rank test, as well as the receiver operating characteristic (ROC), efficiency and precision were used for authenticating the produced landslide models. The results of the accuracy measures revealed that all models have good potential to recognize the landslide susceptibility in the Garhwal Himalayan region. Among these models, the ensemble model achieved a higher accuracy (precision: 0.829, efficiency: 0.833, AUC: 89.460, RMSE: 0.069 and MAE: 0.141) than the individual models. According to the outcome of the ensemble simulations, the BRT model's predictive accuracy was enhanced by integrating it with the statistical model (CP). The study showed that the areas of fallow land, plantation fields, and roadsides with elevations of more than 1500 m. with steep slopes of 24° to 87° and eroding hills are highly susceptible to landslides. The findings of this work could help in minimizing the landslides' risk in the Western Himalaya and its adjoining areas with similar landscapes and geological characteristics.

Keywords: Boost regression tree (BRT); Conditional probability (CP); Ensemble method; Landslide susceptibility; Rudraprayag.