PL EN
Predicting Water Quality Parameters in a Complex River System
Isman Kurniawan 1, 2  
,   Gasim Hayder 3, 4  
,   Hauwa Mohammed Mustafa 5, 6  
 
Więcej
Ukryj
1
School of Computing, Telkom University, Bandung, Indonesia 40257
2
Research Centre of Human Centric Engineering, Telkom University, Bandung, Indonesia 40257
3
Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor Darul Ehsan, Malaysia
4
Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor Darul Ehsan, Malaysia
5
College of Graduate Studies, Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor Darul Ehsan, Malaysia
6
Department of Chemistry, Kaduna State University (KASU), Tafawa Balewa Way, Kaduna, Nigeria
AUTOR DO KORESPONDENCJI
Hauwa Mohammed Mustafa   

College of Graduate Studies, Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor Darul Ehsan, Malaysia. Department of Chemistry, Kaduna State University (KASU), Tafawa Balewa Way, Kaduna, Nigeria
Data publikacji: 01-01-2021
 
J. Ecol. Eng. 2021; 22(1):250–257
 
SŁOWA KLUCZOWE
DZIEDZINY
 
STRESZCZENIE
This research applied a machine learning technique for predicting the water quality parameters of Kelantan River using the historical data collected from various stations. Support Vector Machine (SVM) was used to develop the prediction model. Six water quality parameters (dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and suspended solids (SS)) were predicted. The dataset was obtained from the measurement of 14 stations of Kelantan River from September 2005 to December 2017 with a total sample of 148 monthly data. We defined 3 schemes of prediction to investigate the contribution of the attribute number and the model performance. The outcome of the study demonstrated that the prediction of the suspended solid parameter gave the best performance, which was indicated by the highest values of the R2 score. Meanwhile, the prediction of the COD parameter gave the lowest score of R2 score, indicating the difficulty of the dataset to be modelled by SVM. The analysis of the contribution of attribute number shows that the prediction of the four parameters (DO, BOD, NH3-N, and SS) is directly proportional to the performance of the model. Similarly, the best prediction of the pH parameter is obtained from the utilization of the least number of attributes found in scheme 1. Keywords: machine learning, water quality parameters, turbidity, suspended solids, Kelantan River.