Evaluation of metaheuristic optimization algorithms for optimal allocation of surface water and groundwater resources for crop production

Extensive variability in current climatic conditions necessitates the need for optimal planning of water resources to manage socio-economic and environmental requirements efficiently. The optimal allocation of surface water and groundwater is essential to maximize crop net return due to uncertainty in seasonal rainfall, groundwater, and surface water availability in each region. This study demonstrated a multi-objective model to maximize the crop net return and efficient management of water resources. The multi-objective model comprised three objective functions maximizing the crop net return, minimizing the water deficit, and maximizing the aquifer recharge. The model's practicability was analyzed through the case study of the Pennar-Palar-Cauvery link canal command in India. Three meta-heuristic approaches, particle swarm optimization (PSO), genetic algorithm (GA), and marine predators algorithm (MPA), were employed to solve the presented model. And their performance was evaluated using hypervolume and coverage metrics, indicating MPA's superiority in obtaining well-distributed Pareto-optimal solutions. Thus, decision-makers can choose the best feasible solution based on current resource availability and preferences. The model was executed considering different cropping area deviations (5-25%) in the existing cropping pattern and allowance of groundwater mining from 0% to 70%. The obtained cropping pattern utilizing MPA at 25% cropping deviation achieved an increased net return of 1327.05 million INR.