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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| التنسيق: | مقال |
| اللغة: | English |
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Penerbit Universiti Kebangsaan Malaysia
2020
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| 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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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 |
| _version_ |
1684654290154553344 |
| score |
13.251813 |