AI-Integrated Water Distribution Model for Lift Irrigation System

The existing lift irrigation schemes face significant challenges such as high operational costs, inefficient water distribution, delays in system upgrades, and limited resource optimization. Traditional methods of ten lack predictive capabilities, resulting in water wastage, energy inefficiency, and poor scheduling of maintenance activities. Moreover, the absence of advanced planning tools hampers timely project execution and cost control. To address these gaps, there is a need for an AI-integrated predictive model that can enhance water distribution efficiency while optimizing costs and resources. By incorporating construction management techniques like Critical Path Method (CPM) and Value Engineering, the proposed research aims to improve project scheduling, resource allocation, and cost-effectiveness. A pilot-scale implementation will validate the model's performance over traditional systems.