Assessing Agricultural Reuse Potential of Treated Wastewater: A Hybrid Machine Learning Approach

Estimating the quality of treated wastewater is a complex, nonlinear challenge that traditional statistical methods struggle to address. This study introduces a hybrid machine learning approach to predict key effluent parameters from an advanced biological wastewater treatment plant and assesses the reuse potential of treated wastewater for irrigation. Three artificial intelligence (AI) models, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Logic-Mamdani (FLM), were applied to three years of daily inlet and outlet water quality data. Fuzzy Logic was employed to predict the usability potential of treated wastewater, with ANFIS categorizing quality parameters and ANN-based high-performance models (low MSE, 74-99% R2) applied in the fuzzy inference system. The qualitative reuse potential of treated wastewater for agricultural irrigation ranged from 69% to 72% based on the best-performing model. It was estimated that treated wastewater could irrigate approximately 35% of a 20,000-hectare agricultural area. By integrating machine learning models, this research enhances the accuracy and interpretability of wastewater quality predictions, providing a reliable framework for sustainable water resource management. The findings support the optimization of wastewater treatment processes and highlight AI's role in advancing water reuse strategies in agriculture, ultimately contributing to improved irrigation efficiency and environmental conservation.