Total dissolved solids (TDS) and electrical conductivity (EC) are important parameters in determining water quality for drinking and agricultural water, since they are directly associated to the concentration of salt in water and, hence, high values of these parameters cause low water quality indices. In addition, they play a significant role in hydrous life, effective water resources management and health studies. Thus, it is of critical importance to identify the optimum modeling method that would be capable to capture the behavior of these parameters. The aim of this study was to assess the ability of using three different models of artificial intelligence techniques: Adaptive neural based fuzzy inference system (ANFIS), artificial neural networks (ANNs) and Multiple Regression Model (MLR) to predict and estimate TDS and EC in Abu-Ziriq marsh south of Iraq. As so, eighty four monthly TDS and EC values collected from 2009 to 2018 were used in the evaluation. The collected data was randomly split into 75% for training and 25% for testing. The most effective input parameters to model TDS and EC were determined based on cross-correlation test. The three performance criteria: correlation coefficient (CC), root mean square error (RMSE) and Nash–Sutcliffe efficiency coefficient (NSE) were used to evaluate the performance of the developed models. It was found that nitrate (NO₃), calcium (Ca⁺²), magnesium (Mg⁺²), total hardness (T.H), sulfate (SO₄) and chloride (Cl⁻¹) are the most influential inputs on TDS. While calcium (Ca⁺²), magnesium (Mg⁺²), total hardness (T.H), sulfate (SO₄) and chloride (Cl⁻¹) are the most effective on EC. The comparison of the results showed that the three models can satisfactorily estimate the total dissolved solids and electrical conductivity, but ANFIS model outperformed the ANN and MLR models in the three performance criteria: RMSE, CC and NSE during the calibration and validation periods in modeling the two water quality parameters. ANFIS is recommended to be used as a predictive model for TDS and EC in the Iraqi marshes.

The low water quantity has a significant impact on the ecosystem and the food chain of living organisms, thus causing a loss of biodiversity and a lack of natural food sources. Abu-Ziriq Marsh, located in the south of Iraq, is chosen as the case study for the application of the proposed methodology. The aim of this study was to assess the ability of using three different models of Artificial Intelligence (AI) techniques: Adaptive Neural-based Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Multiple Regression Model (MLR) to predict and estimate the discharge of Abu-Ziriq Marsh by depending on flow release from upstream Al-Badaa regulator. Daily discharge of Al-Badaa regulator(Q_B ) and Abu-Ziriq Marsh(Q_z ) were used in this study. The water quantity data, consisting of 720 records of daily data between the years 2017 and 2018, were used for training and testing the models. The training and testing data were randomly partitioned into 515 (70.5 %) and 215 (29.5 %) datasets, respectively. The performance of all models was assessed through the correlation coefficient (R), root mean square error (RMSE) and Nash–Sutcliffe efficiency coefficient (NSE). Results of RMSE, R and NSE for the calibration (validation) of ANFIS model were 4.11 (4.17), 0.87 (0.83) and 0.76 (0.70), respectively. The evaluation of the results indicates that ANFIS model is superior to other models. The identified ANFIS models can be used as tools for the computation of water quantity parameter(Q_z ) in Iraqi Marshes.