Agriculture is the most important sector with regard to water resources management due to its social, economic, hydrological, and environmental aspects, and many scholars and researchers have been driven to investigate the dynamic interrelationships among hydrological, environmental, and socioeconomic factors affecting agriculture. The system dynamics (SD) approach has become widely used because of its merits and benefits as a tool to deal with complex, dynamic problems and systems with many aspects and components that are involved and must be understood to ensure sound decisions regarding water and hydrological systems. Although agricultural water management needs to be studied as a main part of water management, socioeconomic management, and environmental management requiring the use of SD, this review shows that SD is currently used to a limited extent in terms of agricultural water management. This paper sheds light on the studies and investigations on the use of SD in the water sector and highlights the strengths of SD in order to encourage researchers to use this promising method to manage such a vital resource. Accordingly, this review seeks to include a comprehensive and up-to-date survey of existing publications and scholarly papers on the use of SD modeling as an effective technique for dealing with different problems associated with planning, management, and analysis of hydrology and water resources systems. Recent trends in the integration of SD with other modeling systems, such as artificial intelligence systems, are discussed along with the limitations and challenges facing application. This article makes a new contribution by giving a foundation of references and studies for scholars, researchers, and academics which encourages future investigation in employing the SD approach to hydrology and water resources management and planning, especially with agricultural water.

This study investigates the application of artificial neural network (ANN) for predicting solar still production (MD). Agricultural drainage water (ADW) was desalinated using a solar still. Important meteorological variables: ambient air temperature, relative humidity, wind speed, and solar radiation, together with the operational variables of flow rate, temperature, and total dissolved solids of feedwater, were considered as input parameters for ANN modeling. The output parameter was MD. The results revealed that the ANN model with five neurons and hyperbolic tangent transfer function was the most appropriate for MD prediction based on the minimum measures of error. The optimal ANN model had a 7–5–1 architecture. The ANN model was also compared to multiple linear regression (MLR). The results indicated that, compared to the MLR model, the ANN model provided better prediction results in all modeling stages. The average of the coefficient of determination between the ANN results and the experimental data was more than 0.96. Consequently, the ANN model was shown to have acceptable generalization capability and accuracy. The relative errors of forecasted MD values for the ANN model were mostly in the vicinity of ±10%. These results indicate that ANN can be successfully used in the MD prediction of a solar still desalinating ADW. One major output/contribution of this research involves assessment of the ANN modeling technique during ADW solar desalination, which adds a new perspective to the system analysis, design, and modeling for the potential productivity of a solar still to produce water during the ADW desalination process.

Three artificial neural network learning algorithms were utilized to forecast the productivity (MD) of a solar still operating in a hyper-arid environment. The learning algorithms were the Levenberg–Marquardt (LM), the conjugate gradient backpropagation with Fletcher–Reeves restarts, and the resilient backpropagation. The Julian day, ambient air temperature, relative humidity, wind speed, solar radiation, temperature of feed water, temperature of brine water, total dissolved solids (TDS) of feed water, and TDS of brine water were used in the input layer of the developed neural network model. The MD was located in the output layer. The developed model for each algorithm was trained, tested, and validated with experimental data obtained from field experimental work. Findings revealed the developed model could be utilized to predict the MD with excellent accuracy. The LM algorithm (with a minimum root mean squared error and a maximum overall index of model performance) was found to be the best in the training, testing, and validation stages. Relative errors in the predicted MD values of the developed model using the LM algorithm were mostly in the vicinity of ±10%. These results indicated that the LM algorithm is the most ideal and accurate algorithm for the prediction of the MD with the developed model.

The aim of this study was to investigate the performance of a solar desalination system using three different types of feedwater to reach near zero liquid discharge (ZLD) under hyper arid environment. Solar still was used to desalinate seawater (SW), groundwater (GW), and agricultural drainage water (DW). The influence of meteorological parameters on the performance of the solar still was investigated. The system productivity (MD), operational recovery ratio (ORR), and thermal efficiency (η_th) were determined during the desalination process. Some physicochemical properties of the feed, brine, and distilled water were measured such as total dissolved solids (TDS), electrical conductivity (EC), pH, density (ρ), and color. The performance was in direct proportion to solar radiation and ultraviolet index. The average ranges of the MD, ORR, and η_th were 0.56 L/m²/h, 36.77, and 52.38%, respectively, for all waters. The final recovery ratios were 68, 93, and 95.6% for SW, GW, and DW, respectively. Statistical empirical models were found to predict the system performance with a range of R² was 88–96%. One major output of this research is the assessment of the solar still system during the desalination process for near-ZLD, which adds a new perspective to system design, analysis, and modeling of the possible use of solar energy in ZLD desalination process. On the other hand, more studies are required for the continuous pilot production system, modeling for commercial production, in addition to full economic evaluation of the system.