Quantifying soil erosion and influential factors in Guwahati's urban watershed using statistical analysis, machine and deep learning

Soil erosion is a complex environmental issue influenced by rapid climate change, resource exploitation, and soil degradation etc. These factors have triggered global acceleration of soil erosion, primarily due to rapid transformation of topographical features and landscape composition. Guwahati, a thriving financial hub in northeast India, witnessing significant landscape change on both the banks of the Brahmaputra river therefore becomes disaster-prone zones. Hence, the objective of the present study is to identify soil erosion factors and assess its impact using statistical, machine learning, and deep learning techniques. It employs Revised Universal Soil Loss Equation (RUSLE) model for soil erosion estimation, furthermore analyzing physical attributes such as morphometrics, topography, drainage networks, and land use fragmentation indicators. Partial Least Squares Regression (PLSR), Random Forest (RF) sensitivity analysis, and Deep Neural Net-work (DNN) techniques are used in the study. The RUSLE model showed a significant range of soil erosion rates in the study area, spanning from 168.16 to 188.60 tonnes/hectare/year. Particularly, Silsako, Bharalu, North Guwahati, and Foreshore experiences the most severe soil loss. Amongst all influential factors contributing to soil erosion, the most important key parameters are rainfall, drainage density, landscape fragmentation components (such as cohesion index, edge density, and Shannon diversity index), along with stream frequency and basin relief, as indicated by the RF and DNN models. Furthermore, the PLSR analysis assigned linear weights to variables, highlighting the effectiveness of 14 out of 15 independent predictors derived from basin characteristics in accurately estimating soil erosion. This study provides important quantitative insights through rigorous scientific analysis, enabling well-informed decisions in urban water-shed management within the Brahmaputra region. Furthermore, it enhances understanding of the area's urgent needs, societal implications, and environmental conditions related to soil erosion.