Application of Computational Model Based Probabilistic Neural Network for Surface Water Quality Prediction

Mohammed Falah Allawi, Sinan Q. Salih, Murizah Kassim (), Majeed Mattar Ramal, Abdulrahman S. Mohammed and Zaher Mundher Yaseen ()
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Mohammed Falah Allawi: Dams and Water Resources Engineering Department, College of Engineering, University of Anbar, Ramadi 31001, Iraq
Sinan Q. Salih: Department of Communication Technology Engineering, College of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad 00964, Iraq
Murizah Kassim: Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam 40450, Malaysia
Majeed Mattar Ramal: Dams and Water Resources Engineering Department, College of Engineering, University of Anbar, Ramadi 31001, Iraq
Abdulrahman S. Mohammed: Dams and Water Resources Engineering Department, College of Engineering, University of Anbar, Ramadi 31001, Iraq
Zaher Mundher Yaseen: Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

Mathematics, 2022, vol. 10, issue 21, 1-18

Abstract: Applications of artificial intelligence (AI) models have been massively explored for various engineering and sciences domains over the past two decades. Their capacity in modeling complex problems confirmed and motivated researchers to explore their merit in different disciplines. The use of two AI-models (probabilistic neural network and multilayer perceptron neural network) for the estimation of two different water quality indicators (namely dissolved oxygen (DO) and five days biochemical oxygen demand (BOD 5 )) were reported in this study. The WQ parameters estimation based on four input modelling scenarios was adopted. Monthly water quality parameters data for the duration from January 2006 to December 2015 were used as the input data for the building of the prediction model. The proposed modelling was established utilizing many physical and chemical variables, such as turbidity, calcium (Ca), pH, temperature (T), total dissolved solids (TDS), Sulfate (SO 4 ), total suspended solids (TSS), and alkalinity as the input variables. The proposed models were evaluated for performance using different statistical metrics and the evaluation results showed that the performance of the proposed models in terms of the estimation accuracy increases with the addition of more input variables in some cases. The performances of PNN model were superior to MLPNN model with estimation both DO and BOD parameters. The study concluded that the PNN model is a good tool for estimating the WQ parameters. The optimal evaluation indicators for PNN in predicting BOD are (R 2 = 0.93, RMSE = 0.231 and MAE = 0.197). The best performance indicators for PNN in predicting Do are (R 2 = 0.94, RMSE = 0.222 and MAE = 0.175).

Keywords: surface water quality; machine learning; Iraq region; input combinations; data engineering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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