Multi-Objective Optimization of Irrigation Water Quality for Forage Maize Using the NSGA-II Algorithm Integrated with a Full Interactions Regression Model and Response Surface Analysis in Treated Municipal Wastewater Application

Shakeri, Narjes; Mirnaseri, Mohammad; Khoshravesh, Mojtaba; Delir Hasannia, Reza

doi:10.22059/ijswr.2026.413567.670130

Multi-Objective Optimization of Irrigation Water Quality for Forage Maize Using the NSGA-II Algorithm Integrated with a Full Interactions Regression Model and Response Surface Analysis in Treated Municipal Wastewater Application

Articles in Press

Document Type : Research Paper

Authors

Narjes Shakeri ¹

Mohammad Mirnaseri ²

Mojtaba Khoshravesh ¹

Reza delir hasannia ³

¹ Department of Water Engineering, Faculty of Agricultural Engineering, University of Agricultural Sciences and Natural Resources, Sari, Iran.

² Assistant Professor of Water Engineering Department, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University.

³ Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.

10.22059/ijswr.2026.413567.670130

Abstract

The utilization of treated municipal wastewater for forage maize irrigation represents an unavoidable strategy for mitigating water scarcity crises. However, this practice may entail the risk of heavy metal accumulation in plant tissues. The objective of this study was to determine the optimal blending ratio of well water and wastewater through a multi-objective optimization framework to simultaneously achieve maximum forage maize yield and minimum concentrations of nickel (Ni), zinc (Zn), cadmium (Cd), and lead (Pb). Three-year field data (2020–2022) were obtained from a lysimetric experiment conducted in Babolsar, Iran, comprising five blending treatments (0 to 100% wastewater). In the initial phase, a full interactions regression model was developed, attaining an average adjusted coefficient of determination of 0.9968 between six water quality parameters (EC, Na⁺, SAR, Ca²⁺, Mg²⁺, and N) and the five objectives. Subsequently, the NSGA-II multi-objective genetic algorithm, configured with a population size of 500 and 1,000 generations, generated a Pareto front consisting of 500 non-dominated solutions. Combined sensitivity analysis identified sodium concentration as the most influential variable governing the yield-toxicity trade-off, exhibiting a sensitivity coefficient of -0.232. In contrast, the sodium adsorption ratio (SAR) ranked second in importance with a coefficient of +0.207. This solution achieved a grain yield of 24.54 ton/ha (0.33% higher than the pure wastewater treatment) while substantially reducing lead concentration from 46.85 to 14.19 µg/g and cadmium concentration from 8.39 to 0.96 µg/g (corresponding to 70% and 89% reductions, respectively).

Keywords

Wastewater Irrigation

Multi-Objective Optimization

Pareto Front

Heavy Metals

Water Resources Management

Subjects