Climate-Resilient Plant Water Resource Management Using Explainable AI Technique

Abstract

Climate change intensifies global water insecurity through escalating hydrological extremes, deteriorating water quality, and aging infrastructure, necessitating transformative solutions. This systematic review evaluates the role of artificial intelligence (AI) in advancing climate-resilient water management. Key findings reveal that AI models—particularly long short-term memory (LSTM) and hybrid physics-informed neural networks—achieve superior accuracy in hydrological forecasting (Nash–Sutcliffe efficiency>0.90), enabling reliable predictions of water availability, droughts, and floods. AI-driven optimization enhances water distribution efficiency by 15–30% in case studies, while IoT-integrated systems reduce agricultural water waste by 20–40%. However, critical challenges persist: (1) data inequity, with 70% of AI applications concentrated in temperate, data-rich regions, neglecting arid and low-income areas; (2) model interpretability gaps, as “black-box” algorithms hinder stakeholder trust; and (3) policy-technical misalignment, where siloed governance stifles scalable AI adoption. The review underscores the urgency of hybrid AI-physics frameworks to balance accuracy with explainability, decentralized data ecosystems to empower marginalized communities, and ethical governance protocols to address algorithmic bias and equity. Future research should prioritize integrating multi-source data to enhance model transparency, to ensure sustainable and inclusive water management strategies. By bridging technological innovation with systemic resilience, AI emerges as a critical tool in mitigating climate impacts and securing global water security.