Proposing an easy-to-use tool for estimating landslide dimensions using a data-driven approach

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 ([Formula: see text]), tangential slope ([Formula: see text]), drop height ([Formula: see text]), angle of reach ([Formula: see text]) and the profile curvature ([Formula: see text]). A total of 144 models were considered and were evaluated using mean-absolute-error ([Formula: see text]) and root-mean-square-error (RMSE) values. The results indicate that, by using E and θ alone, the [Formula: see text] 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, [Formula: see text] values have increased in seven cases and [Formula: see text] 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.

Keywords

• landslides
• hazard
• random forest
• travel distance
• machine learning