Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting
The utilisation of modelling tools in hydrology has been effective in predicting future floods by analysing historical rainfall and inflow data, due to the association between climate change and flood frequency. This study utilised a historical dataset of monthly inflow and rainfall for the Terengga...
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IWA Publishing
2025
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| author | Abozweita O.A. Ahmed A.N. Mohd Sidek L.B. Basri H.B. Bin Zawawi M.H. Huang Y.F. El-Shafie A. |
| author2 | 57219806365 |
| author_facet | 57219806365 Abozweita O.A. Ahmed A.N. Mohd Sidek L.B. Basri H.B. Bin Zawawi M.H. Huang Y.F. El-Shafie A. |
| author_sort | Abozweita O.A. |
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| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The utilisation of modelling tools in hydrology has been effective in predicting future floods by analysing historical rainfall and inflow data, due to the association between climate change and flood frequency. This study utilised a historical dataset of monthly inflow and rainfall for the Terengganu River in Malaysia, and it is renowned for its hydrological patterns that exhibit a high level of unpredictability. The evaluation of the predictive precision and effectiveness of the Optimised Decision Tree ODT model, along with the RF and GBT models, in this study involved analysing several indicators. These indicators included the correlation coefficient, mean absolute error, percentage of relative error, root mean square error, Nash-Sutcliffe efficiency, and accuracy rate. The research results indicated that the ODT and RF models performed better than the GBT model in predicting monthly inflows. The ODT model, as well as the RF and GBT models, showed validation results with average accuracies of 94%, 91%, and 92%, respectively. The R2 values were 90.2%, 84.8%, and 96.0%, respectively, and the NES values ranged from 0.92 to 0.94. The results of this research have greater implications, extending beyond the forecasting of monthly inflow rates to encompass other hydro-meteorological variables that depend exclusively on historical input data. ? 2024 The Authors. |
| format | Article |
| id | my.uniten.dspace-36200 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | IWA Publishing |
| record_format | dspace |
| spelling | my.uniten.dspace-362002025-03-03T15:41:33Z Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting Abozweita O.A. Ahmed A.N. Mohd Sidek L.B. Basri H.B. Bin Zawawi M.H. Huang Y.F. El-Shafie A. 57219806365 57214837520 58617132200 57065823300 59456402500 55807263900 16068189400 The utilisation of modelling tools in hydrology has been effective in predicting future floods by analysing historical rainfall and inflow data, due to the association between climate change and flood frequency. This study utilised a historical dataset of monthly inflow and rainfall for the Terengganu River in Malaysia, and it is renowned for its hydrological patterns that exhibit a high level of unpredictability. The evaluation of the predictive precision and effectiveness of the Optimised Decision Tree ODT model, along with the RF and GBT models, in this study involved analysing several indicators. These indicators included the correlation coefficient, mean absolute error, percentage of relative error, root mean square error, Nash-Sutcliffe efficiency, and accuracy rate. The research results indicated that the ODT and RF models performed better than the GBT model in predicting monthly inflows. The ODT model, as well as the RF and GBT models, showed validation results with average accuracies of 94%, 91%, and 92%, respectively. The R2 values were 90.2%, 84.8%, and 96.0%, respectively, and the NES values ranged from 0.92 to 0.94. The results of this research have greater implications, extending beyond the forecasting of monthly inflow rates to encompass other hydro-meteorological variables that depend exclusively on historical input data. ? 2024 The Authors. Final 2025-03-03T07:41:33Z 2025-03-03T07:41:33Z 2024 Article 10.2166/hydro.2024.205 2-s2.0-85210912702 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210912702&doi=10.2166%2fhydro.2024.205&partnerID=40&md5=c07845428854934f262655e68bcad87b https://irepository.uniten.edu.my/handle/123456789/36200 26 11 2683 2703 IWA Publishing Scopus |
| spellingShingle | Abozweita O.A. Ahmed A.N. Mohd Sidek L.B. Basri H.B. Bin Zawawi M.H. Huang Y.F. El-Shafie A. Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting |
| title | Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting |
| title_full | Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting |
| title_fullStr | Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting |
| title_full_unstemmed | Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting |
| title_short | Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting |
| title_sort | enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting |
| url_provider | http://dspace.uniten.edu.my/ |