Prediction of the level of Water Quality Index using Artificial Neural Network techniques in Melaka River Basin

Artificial Neural Network (ANN) techniques were used to develop and validate water quality by predicting the Water Quality Index (WQI) in Melaka River Basin, Malaysia. Nine sampling stations were monitored in total. ANN techniques were applied for testing and developing the water quality predictio...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي: Ang, Kean Hua
التنسيق: مقال
اللغة:English
منشور في: Penerbit Universiti Kebangsaan Malaysia 2020
الوصول للمادة أونلاين:http://journalarticle.ukm.my/15701/1/49_01_08.pdf
http://journalarticle.ukm.my/15701/
http://www.mabjournal.com/index.php?option=com_content&view=article&id=981&catid=59:current-view&Itemid=56
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spelling my-ukm.journal.157012020-11-16T23:39:23Z http://journalarticle.ukm.my/15701/ Prediction of the level of Water Quality Index using Artificial Neural Network techniques in Melaka River Basin Ang, Kean Hua Artificial Neural Network (ANN) techniques were used to develop and validate water quality by predicting the Water Quality Index (WQI) in Melaka River Basin, Malaysia. Nine sampling stations were monitored in total. ANN techniques were applied for testing and developing the water quality prediction based on two sets of data. In the first data set, the independent water quality of six variables was used as input into ANN for trained, test and validated samples. In the second data set, a combination between Multiple Linear Regression (MLR) and ANN indicating only Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Suspended Solid (SS), and Ammoniacal-Nitrogen (AN) are accounted for training, testing and validating in modeling the water quality. Generally, MLR is used to exclude the lowest value invariance of independent variables, while rejecting the Dissolved Oxygen (DO) and pH. Based on the result of the correlation coefficient, the second set data (0.89) is marginally better than the first set data (0.87). These circumstances stated that predictions for WQI using ANN are acceptable, and the result is better when the variables of DO and pH are eliminated. Penerbit Universiti Kebangsaan Malaysia 2020 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/15701/1/49_01_08.pdf Ang, Kean Hua (2020) Prediction of the level of Water Quality Index using Artificial Neural Network techniques in Melaka River Basin. Malaysian Applied Biology, 49 (1). pp. 69-74. ISSN 0126-8643 http://www.mabjournal.com/index.php?option=com_content&view=article&id=981&catid=59:current-view&Itemid=56
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description Artificial Neural Network (ANN) techniques were used to develop and validate water quality by predicting the Water Quality Index (WQI) in Melaka River Basin, Malaysia. Nine sampling stations were monitored in total. ANN techniques were applied for testing and developing the water quality prediction based on two sets of data. In the first data set, the independent water quality of six variables was used as input into ANN for trained, test and validated samples. In the second data set, a combination between Multiple Linear Regression (MLR) and ANN indicating only Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Suspended Solid (SS), and Ammoniacal-Nitrogen (AN) are accounted for training, testing and validating in modeling the water quality. Generally, MLR is used to exclude the lowest value invariance of independent variables, while rejecting the Dissolved Oxygen (DO) and pH. Based on the result of the correlation coefficient, the second set data (0.89) is marginally better than the first set data (0.87). These circumstances stated that predictions for WQI using ANN are acceptable, and the result is better when the variables of DO and pH are eliminated.
format Article
author Ang, Kean Hua
spellingShingle Ang, Kean Hua
Prediction of the level of Water Quality Index using Artificial Neural Network techniques in Melaka River Basin
author_facet Ang, Kean Hua
author_sort Ang, Kean Hua
title Prediction of the level of Water Quality Index using Artificial Neural Network techniques in Melaka River Basin
title_short Prediction of the level of Water Quality Index using Artificial Neural Network techniques in Melaka River Basin
title_full Prediction of the level of Water Quality Index using Artificial Neural Network techniques in Melaka River Basin
title_fullStr Prediction of the level of Water Quality Index using Artificial Neural Network techniques in Melaka River Basin
title_full_unstemmed Prediction of the level of Water Quality Index using Artificial Neural Network techniques in Melaka River Basin
title_sort prediction of the level of water quality index using artificial neural network techniques in melaka river basin
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2020
url http://journalarticle.ukm.my/15701/1/49_01_08.pdf
http://journalarticle.ukm.my/15701/
http://www.mabjournal.com/index.php?option=com_content&view=article&id=981&catid=59:current-view&Itemid=56
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