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Applying ANN Based PSO Algorithm for the Prediction of DO and PO4 in Al-Hillah River

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An evaluation of water quality parameters is necessary to improve the assessment of water resources management and maintenance. Monitoring water parameters is an important index of water quality and pollution control and management. A novel technique as artificial networks has proved its capacity and tendency for simulating and modeling numerous operations in water engineering. This paper has suggested models for predicting DO and PO4 at Al-Hillah River by using ANN-based PSO approach. This study defines the use of MLP, multilayer perceptron, feed foreword backpropagation-based particle swarm optimization. The study has revealed that ANN models can provide a very high correlation with the actual values in predicting DO and PO4. Sixteen river water quality parameters have been examined and used. The models' performance has been evaluated by R, correlation coefficient, mean absolute error, as well as mean square error. The outcomes have displayed that the MLP has been very effective in the prediction process of PO4 and DO.
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ANN; PSO; DO; PO4; Prediction

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