Proposing an easy-to-use tool for estimating landslide dimensions using a data-driven approach. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Abraham, Minu Treesa; Satyam, Neelima; Pradhan, Biswajeet; Segoni, Samuele
• Is Part Of: All earth. Volume 34:Issue 1(2022)
• Page Start: 243
• Page End: 258
• Publication Date: 2022-12-31
• Subjects: landslides -- hazard -- random forest -- travel distance -- machine learning; Earth sciences -- Periodicals; Space sciences -- Periodicals; 550
• DOI: 10.1080/27669645.2022.2127549
• Languages: English
• ISSNs: 2766-9645

Abstract

ABSTRACT: The increase in population and urbanisation of hilly regions have increased the risk due to landslides. This manuscript presents a data-driven approach with a random forest algorithm to estimate the projected area, length, travel distance, and width of landslides, using elevation and slope information. The method is tested for two different study areas (Idukki and Wayanad), using three different combinations of inputs. The input features considered were elevation ( E ), tangential slope ( θ ), drop height ( H ), angle of reach ( α ) and the profile curvature ( c ). A total of 144 models were considered and were evaluated using mean-absolute-error ( M A E ) and root-mean-square-error (RMSE) values. The results indicate that, by using E and θ alone, the R M S E value in estimating the length values for flow-like landslides in Wayanad was reduced from 472.74 m to 204.64 m. Out of the 48 combinations considered, M A E values have increased in seven cases and R M S E values in eight cases only. The pre-trained models are saved and used to develop an easy-to-use tool, which can bypass the complications associated with the existing statistical approaches. The tool can be used by untrained personnel for preliminary hazard assessment.