- Tytuł:
- Predicting Water Quality Parameters in a Complex River System
- Autorzy:
-
Kurniawan, Isman
Hayder, Gasim
Mustafa, Hauwa Mohammed - Powiązania:
- https://bibliotekanauki.pl/articles/1839534.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polskie Towarzystwo Inżynierii Ekologicznej
- Tematy:
-
machine learning
water quality parameters
turbidity
suspended solids
Kelantan river - Opis:
- 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.
- Źródło:
-
Journal of Ecological Engineering; 2021, 22, 1; 250-257
2299-8993 - Pojawia się w:
- Journal of Ecological Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł