AI-Driven Dynamic Water Resource Prediction and Optimization Algorithms for Precision Irrigation

Addressing the escalating global scarcity of water resources and the pressing need for efficient irrigation in agricultural production, this research introduces a novel AI-powered framework for dynamic water resource prediction and optimization tailored for precision irrigation [1]. This framework innovatively integrates advanced machine learning techniques to achieve accurate, real-time forecasting of farmland water demand and subsequently optimizes irrigation strategies based on these predictions. Distinct from conventional approaches relying on static or simplified models, this study innovatively fuses multi-source heterogeneous information, including meteorological data, soil moisture levels, and crop growth stages, to construct a deep learning-based time-series forecasting model[2]. This model effectively captures the non-linear dynamic characteristics of water resource requirements. Furthermore, we designed a reinforcement learning-based irrigation decision optimization algorithm capable of dynamically adjusting irrigation parameters based on the predictive outcomes and real-time environmental feedback. This approach aims to minimize water consumption while concurrently maximizing crop yield. Experimental results demonstrate that the proposed algorithm significantly surpasses traditional methods in water resource prediction accuracy. Moreover, in practical irrigation scenarios, it effectively enhances water utilization efficiency, boosts crop productivity, and reduces energy expenditure, thereby providing robust technological support for sustainable agricultural development. This research exhibits significant innovation in both algorithm design and system integration, effectively overcoming the limitations of existing precision irrigation technologies regarding dynamic environmental adaptability and intelligent decision-making capabilities.