Fuzzy particle swarm optimization for conjunctive use of groundwater and reclaimed wastewater under uncertainty

Water scarcity is a significant global challenge. Water resources planning and management are vital to all sectors, primarily for the agricultural sector identified as a significant consumer. Conjunctive water use management is ideal for decreasing water scarcity, especially in arid and semiarid regions. More importantly, considering uncertainties in water management is critical to planning and management issues. This study develops a fuzzy optimization model aiming at a qualitative-quantitative conjunctive reallocation of reclaimed wastewater and groundwater resources. Three objectives, including maximizing economic benefit, minimizing fertilizer consumption, and groundwater withdrawal, were assessed in Iran's Varamin Irrigation Network. This research uses fuzzy and non-fuzzy optimization models for a quantitative-qualitative conjunctive use of reclaimed wastewater and groundwater to meet multi-objective functions. These single and multi-objective fuzzy and non-fuzzy optimization models are compared. The particle swarm optimization (PSO) and multi-objective PSO (MOPSO) algorithms optimized single and multi-objective problems. A fuzzy optimization model is superior to the non-fuzzy optimization model regarding optimizing multi-objective functions. The results indicated that the net benefit increased up to 16% without increasing the cultivated area in the fuzzy MOPSO model. Moreover, groundwater withdrawal was reduced by using a larger share of reclaimed wastewater in total water consumption. This increased the use of potential nitrate in reclaimed wastewater and reduced fertilizer consumption. This study highlights achieving socio-economic and environmental goals under uncertain circumstances using agricultural water strategies through reclaimed wastewater. Also, a general improvement in water consumption efficiency was achieved through the optimal cropping pattern.