Classification Of Irrigation Water Based On Machine Learning Approach

Abstract

This thesis pioneers the integration of advanced machine learning models into irrigation water classification. Starting from groundwater quality assessment through IWQI, and groundwater classification, the research evolves to leverage ML model interpretability for predictions. It marks a paradigm shift in water quality assessment methodologies, emphasizing potential efficiency gains. The application of machine learning assures accurate simulation of the Irrigation Water Quality Index (IWQI) and streamlined economic monitoring approach. This work carries substantial implications for water resource management, particularly benefiting farmers and decision-makers. The findings contribute to the advancement of sustainable water management practices, providing a transformative perspective at the intersection of machine learning and irrigation water quality assessment.