Identifying the primary drivers of soil erosion through machine learning: A comparative analysis of three algorithms

ABSTRACT Machine learning is revolutionizing various fields by enabling sophisticated and efficient complex data analysis. This study leverages machine learning algorithms to address the critical issue of soil erosion in Uttar Pradesh, India. Soil erosion significantly impacts soil fertility, vital for the country's agricultural sustainability and economic stability. Effective soil erosion mitigation requires a detailed understanding of its contributing factors, which vary across different regions. In this research, we analyzed 15 factors influencing soil erosion using three machine learning algorithms: multiple linear regression, AdaBoost regression, and gradient boosting regression. Our findings revealed that slope is the most significant factor contributing to soil erosion. Among the algorithms, multiple linear regression demonstrated superior performance, providing the most accurate predictions with the lowest error rate. By harnessing the power of machine learning, this study underscores a transformative approach to environmental analysis and offers actionable insights for mitigating soil erosion. These findings can inform more effective soil conservation strategies, ultimately supporting sustainable agricultural practices and economic resilience in India.