Development of prediction model for phosphate in reservoir water system based machine learning algorithms
Decision trees; Eutrophication; Forecasting; Learning systems; Neural networks; Phosphate fertilizers; Predictive analytics; Reservoirs (water); Stochastic systems; Support vector machines; Water pollution; Water quality; Water supply; Conventional modeling; Cross validation; Developed model; Non-po...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Ain Shams University
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-27314 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-273142023-05-29T17:42:34Z Development of prediction model for phosphate in reservoir water system based machine learning algorithms Latif S.D. Birima A.H. Ahmed A.N. Hatem D.M. Al-Ansari N. Fai C.M. El-Shafie A. 57216081524 23466519000 57214837520 57226012037 51664437800 57214146115 16068189400 Decision trees; Eutrophication; Forecasting; Learning systems; Neural networks; Phosphate fertilizers; Predictive analytics; Reservoirs (water); Stochastic systems; Support vector machines; Water pollution; Water quality; Water supply; Conventional modeling; Cross validation; Developed model; Non-point source pollution; Prediction model; Primary sources; Statistical indices; Water quality parameters; Learning algorithms Phosphate (PO4) is a major component of most fertilizers, and when erosion and runoff occur, large amounts of it enter the water bodies, causing several problems such as eutrophication. Feitsui reservoir, the primary source of water supply to Taipei, reported half of the reservoir's pollutants from nonpoint-source pollution. The value of the PO4 in the water body fluctuates in highly nonlinear and stochastic patterns. However, conventional modeling techniques are no longer sufficiently effective in predicting accurately such stochastic patterns in the concentrations of PO4 in water. Therefore, this study proposes different machine learning algorithms: the artificial neural network (ANN), support vector machine (SVM), random forest (RF), and boosted trees (BT) to predict the concentration of PO4. Monthly measured data between 1986 and 2014 were used to train and test the accuracy of these models. The performances of these models were examined using different statistical indices. Hyperparameters optimization such as cross-validation was performed to enhance the precision of the models. Five water quality parameters were used as input to the proposed models. Different input combinations were explored to optimize the precision. The findings revealed that ANN outperformed the other three models to capture the changes in the concentrations of PO4 with high precision where RMSE is equal to 1.199, MAE is equal to 0.858, and R2 is equal to 0.979, MSE is equal to 1.439, and finally, CC is equal to 0.9909. The developed model could be used as a reliable means for managing eutrophication problems. � 2021 THE AUTHORS Final 2023-05-29T09:42:33Z 2023-05-29T09:42:33Z 2022 Article 10.1016/j.asej.2021.06.009 2-s2.0-85110194763 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110194763&doi=10.1016%2fj.asej.2021.06.009&partnerID=40&md5=ea07a43edb74e5a5a8197a1a0aeafcd6 https://irepository.uniten.edu.my/handle/123456789/27314 13 1 101523 All Open Access, Gold, Green Ain Shams University Scopus |
| 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/ |
| description |
Decision trees; Eutrophication; Forecasting; Learning systems; Neural networks; Phosphate fertilizers; Predictive analytics; Reservoirs (water); Stochastic systems; Support vector machines; Water pollution; Water quality; Water supply; Conventional modeling; Cross validation; Developed model; Non-point source pollution; Prediction model; Primary sources; Statistical indices; Water quality parameters; Learning algorithms |
| author2 |
57216081524 |
| author_facet |
57216081524 Latif S.D. Birima A.H. Ahmed A.N. Hatem D.M. Al-Ansari N. Fai C.M. El-Shafie A. |
| format |
Article |
| author |
Latif S.D. Birima A.H. Ahmed A.N. Hatem D.M. Al-Ansari N. Fai C.M. El-Shafie A. |
| spellingShingle |
Latif S.D. Birima A.H. Ahmed A.N. Hatem D.M. Al-Ansari N. Fai C.M. El-Shafie A. Development of prediction model for phosphate in reservoir water system based machine learning algorithms |
| author_sort |
Latif S.D. |
| title |
Development of prediction model for phosphate in reservoir water system based machine learning algorithms |
| title_short |
Development of prediction model for phosphate in reservoir water system based machine learning algorithms |
| title_full |
Development of prediction model for phosphate in reservoir water system based machine learning algorithms |
| title_fullStr |
Development of prediction model for phosphate in reservoir water system based machine learning algorithms |
| title_full_unstemmed |
Development of prediction model for phosphate in reservoir water system based machine learning algorithms |
| title_sort |
development of prediction model for phosphate in reservoir water system based machine learning algorithms |
| publisher |
Ain Shams University |
| publishDate |
2023 |
| _version_ |
1806426045218291712 |
| score |
13.214268 |