Modelling water quantity parameters using Artificial Intelligence techniques, A case study Abu-Ziriq Marsh in south of Iraq.

Abstract

Abstract 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(QB ) and Abu-Ziriq Marsh(Qz ) 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(Qz ) in Iraqi Marshes.