Prediction Of Water Quality Parameter By Applying Evolutionary Algorithm (EA) In Taiwan, China

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Muhammad Shukri Bin Nor Azmi
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The research examined the ability of Artificial Nueral Network (ANN) to predict Nitrate (NO3) in the Taiwan River. Regular monitoring of fluvial water quality criteria by the Department of the Environment (DOE) for the span of 28 years from 1986 to 2014, every month. The following four water quality systems have been selected in the proposed Artificial Neural Network (ANN), namely ammonium (NH3), nitrogen dioxide (NO2), dissolve oxygen (DO) and phosphate (PO4), for the proposed MLPNNN modelling. Two examples have been added, the first being the establishment of a NO3 station for four input-based model and second model (Scen 2) Applications to develop a NO3 prediction model based on prior station fourth inputs and NO3 expected (upstream). The second scenario was to establish the NO3 prediction model. To order to meet the verification requirements, the model needs tests when performance results and the values observed are similar enough. Therefore, the verifications of MLP-NN on the basis of field data gathering for the period 1986–2014 are presented to analyze the performance of the model planned. In order to identify the impact of input parameters on the model, the responsive analysis was implemented on the model. Dissolving oxygen (DO) and phosphate (PO4) have been found to be the most important inputs. On the other hand, the most important parameter was ammonium ions, whilst the lowest contribution to the proposed modell was Nitrogen dioxide (NO2). Three statistical indices were used in evaluating the model's success: efficiency coefficient (CE), mid square error (MSE) and correlation coefficient (CC). Scenario 1 indicates a comparatively low correlation in the test data collection among observed and expected values. In addition, for the test set of 0.98, 0.96 and 0.97 for one station after the adoption of scenario 2 high correlation coefficients were obtained between the observed and expected values. It seemed that Scenario 2's findings were higher than Scenario 1, and all stations were improved significantly from 4 to 8 percent
FYP Sem 2 2019/2020
Water Quality Parameter , Water Quality Prediction , Evolutionary Algorithm