Machine Learning Framework for Soil Erosion Risk Prediction

Abstract

Soil erosion is one of the most critical environmental challenges affecting agricultural productivity, watershed sustainability, ecosystem stability, and global food security. Accelerated erosion caused by rainfall intensity, surface runoff, inappropriate land-use practices, deforestation, rapid urbanization, and climate change has significantly degraded fertile topsoil across many regions of the world. Conventional soil erosion assessment techniques, including empirical equations, field surveys, and process-based simulation models, have provided valuable insights into erosion mechanisms; however, they often require extensive field measurements, considerable computational resources, and expert interpretation. These constraints inhibit their applicability for large-scale, real-time, and dynamic erosion risk appraisal. Various recent developments in machine learning (ML), artificial intelligence (AI), remote sensing technologies, Geographic Information Systems (GIS), and cloud-based geospatial analytics have opened new approaches for developing tools to accurately predict soil erosion at scale through the automation of existing understanding anchored on different heterogeneous datasets constituting the environment. We propose a global machine learning framework to predict soil erosion risk by synthesizing multisource environmental predictors-catalyzed remote sensing imagery, dryland and humid zone elevation models (DEMs), climatic signals, phenology expressions through vegetation indices, soil structures–including magnetic minerals-and land-use characteristics. The systematic data preprocessing, feature engineering, spatial harmonization of predictors (Lorenz et al 2019), machine learning modeling, and hyperparameter tuning approaches were then combined into the framework to facilitate model validation and ultimately visualize erosion susceptibility in space. In this research, various supervised machine learning algorithms such as Decision Trees (DT), Random Forests (RF/SF), Support Vector Machine(SVM/LM), Gradient Boosting Machine(GBM ) and Extreme Gradient Boosting(XGBoost ), Artificial Neural Networks(ANN)(Bardos et. al., 2020b; Murugaiyan & Vidhyashankar, 2018)) techniques are attempted to check the performance of these models in predicting Areas prone to erosion. As an important supplement, the proposed framework integrates feature importance analysis and uncertainty assessment to support explainable artificial intelligence methods for model transparency towards effectively assisting policymakers, environmental planners & practitioners in data-informed decision-making. The results show that combining NDVI, DEM-based terrain parameters, rainfall erosivity factors based on the Tanaka method (1988), soil texture properties derived from ordinary kriging interpolation of ground data collected in 2020, land cover classification through reclassification for evapotranspiration simulation, and wild hydrological slope units remarkably improves prediction performance relative to empirical approaches. Additionally, the use of ensemble learning algorithms enhances classification accuracy, model robustness, and generalization ability under different environmental conditions. The framework allows for early erosion hotspot identification, conservation prioritization in the management of watersheds, and implementation strategies related to sustainable agriculture. Thus, the suggested machine learning framework is a feasible and scalable decision-support method for minimizing soil degradation, encouraging environmental sustainability09and building climate resilience of land management systems.