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

FYP Sem 2 2019/2020

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Main Author: Muhammad Shukri Bin Nor Azmi
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Published: 2023
Subjects:
Water Quality Parameter
Water Quality Prediction
Evolutionary Algorithm
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spelling my.uniten.dspace-214042023-05-04T14:34:36Z Prediction Of Water Quality Parameter By Applying Evolutionary Algorithm (EA) In Taiwan, China Muhammad Shukri Bin Nor Azmi Water Quality Parameter Water Quality Prediction Evolutionary Algorithm FYP Sem 2 2019/2020 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 2023-05-03T16:48:04Z 2023-05-03T16:48:04Z 2020-02 https://irepository.uniten.edu.my/handle/123456789/21404 application/pdf
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Water Quality Parameter
Water Quality Prediction
Evolutionary Algorithm
spellingShingle Water Quality Parameter
Water Quality Prediction
Evolutionary Algorithm
Muhammad Shukri Bin Nor Azmi
Prediction Of Water Quality Parameter By Applying Evolutionary Algorithm (EA) In Taiwan, China
description FYP Sem 2 2019/2020
format
author Muhammad Shukri Bin Nor Azmi
author_facet Muhammad Shukri Bin Nor Azmi
author_sort Muhammad Shukri Bin Nor Azmi
title Prediction Of Water Quality Parameter By Applying Evolutionary Algorithm (EA) In Taiwan, China
title_short Prediction Of Water Quality Parameter By Applying Evolutionary Algorithm (EA) In Taiwan, China
title_full Prediction Of Water Quality Parameter By Applying Evolutionary Algorithm (EA) In Taiwan, China
title_fullStr Prediction Of Water Quality Parameter By Applying Evolutionary Algorithm (EA) In Taiwan, China
title_full_unstemmed Prediction Of Water Quality Parameter By Applying Evolutionary Algorithm (EA) In Taiwan, China
title_sort prediction of water quality parameter by applying evolutionary algorithm (ea) in taiwan, china
publishDate 2023
_version_ 1806424247614046208
score 13.214268

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