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.

This study compared the performances of subsurface drip irrigation (SDI) to sprinkler irrigation (SI) of cool-season turf mix (CS) and warm-season (WS) turfgrass species while investigating their response to different irrigation scheduling strategies. Moreover, the suitability of crop water stress index (CWSI) for monitoring water stress and scheduling irrigation was tested. Irrigation was applied up to the field capacity when 30%, 50% and 70% of total available water was consumed. All parameters, including color, quality, fresh yield, dry matter yield, irrigation water productivity, water productivity, vegetation height and mowing, differed significantly for different irrigation methods and water supply strategies for both species. The best visual turf was maintained under non-limiting soil moisture conditions (30%) in all main and sub-treatments. At this irrigation threshold, maximum and minimum amounts of irrigation water were applied in SI CS treatment (523.5 mm) and SDI WS treatment (298.6 mm), respectively. Warm-season turf required up to 40% less water than cool-season turf mix. In the water-scarce regions, 50% treatment for cool-season and 70% treatment for warm-season can be suggested for acceptable visual quality, lower water consumption and less frequent clipping. The mean CWSI before irrigation, representing irrigation threshold, ranged between 0.22–0.70.

The main objective of this study was to determine canopy-air temperature differential, which can be used to quantify crop water stress index (CWSI) for grapevine (Vitis vinifera L.) grown with drip irrigation during the spring cultivation period of 2005. Specifically, crop response to water loss, atmospheric temperature and air vapor pressure deficit (VPD) in the soil–plant–atmosphere continuum were examined to detect crop water stress. The effects of three irrigation programs (when depleted 30, 50 and 70% of the available water holding capacity within 0.90 m soil profile depth) on two grape cultivar (semillon and razaki) yields, and resulting CWSI were investigated. The non–irrigation treatment (NI) was also used for determination of fully stressed baseline. The lower (non-stressed) and upper (stressed) baselines were determined from measurements of canopy temperatures, ambient air temperatures and vapor pressure deficit values and the CWSI was calculated with three irrigation levels using the empirical approach. The CWSI value was useful for evaluating crop water stress in grapevine and should be useful for timing irrigation and predicting yield

Reservoir evaporation is a vital component of the hydrological cycle and presents considerable challenges for sustainable water management, especially in arid and semi-arid regions. This study assesses the effectiveness of two Artificial Intelligence (AI) methods: Multilayer Perceptron (MLP) and Adaptive Neuro-Fuzzy Inference System (ANFIS), a combination ANN with fuzzy logic, in estimating daily evaporation from a large reservoir in a semi-arid region. Using eight years of hydrometeorological data from a nearby station, the study employed the ReliefF algorithm as a feature selection method for relevant input variables. The dataset was divided into training, validation, and testing subsets with 5% and 10% validation ratios, using four train–test splits of 70:30, 75:25, 80:20, and 85:15. Various training algorithms (e.g., Levenberg–Marquardt) and membership functions (e.g., generalized bell-shaped functions) were tested for both models. MLP consistently outperformed ANFIS on the test sets, showing higher R² and lower RMSE values. In the best-performing 70:30 split, MLP achieved an R² of 0.8069 and RMSE of 0.0923, compared to ANFIS with an R² of 0.3192 and RMSE of 0.2254. The findings highlight the AI-based approaches’ potential to support improved evaporation forecasting and integration into decision support tools for water resource planning amid changing climatic conditions.