Soil depth Prediction in a landslide prone tropical river basin under data-sparse conditions using machine-learning technique

Abstract

Western Ghats (WG) of India is experiencing frequent landslides during every Indian summer monsoon. Due to the unique blend of topography and tropical humid climate, accelerates chemical weathering, forming a layer of unconsolidated soil unconformably overlies the Precambrian crystalline rock. Lack of cohesion or bonding in these contrasting geologic materials, makes WG vulnerable to various forms of landslides during the peak of Indian summer monsoon. Hence detailed information about soil thickness has a predominant role in identifying the landslide prone area and understanding the landslides in WG. However, soil thickness maps are not available for WG area and steep rugged terrain makes it difficult to collect detailed soil thickness data. This study used a random forest (RF) machine-learning model to predict the soil depth with a limited number of sparse samples in the Panniar river basin of WG. The model was combined using 70 soil depth observations with eleven covariates such as normalized difference vegetation index, topographic wetness index, valley depth, solar radiance, elevation, slope length, slope angle, slope aspect, convergence index, profile curvature and plan curvature. The results show that the RF model has good predictive accuracy with coefficient of determination (R2) of 0.822 and root mean square error (RMSE) of 2.968, i.e., almost 80% of soil depth variation explained. The spatially predicted soil depth map clearly shows regional patterns with local details. Both geomorphological processes and vegetation contributed to shaping the soil depth in the study area. The resulting map can be used for understating the soil characteristics and  modelling  the landslide susceptibility in the study area.