Eugene Zhen Xiang Soo and Ren Jie Chin and Lloyd Ling and Yuk Feng Huang and Jin Long Lee and Foo Wei Lee (2024) Streamflow simulation and forecasting using remote sensing and machine learning techniques. Ain Shams Engineering Journal. pp. 1-10.
![[img]](https://eprints.ums.edu.my/style/images/fileicons/text.png) |
Text
FULL TEXT.pdf Restricted to Registered users only Download (2MB) | Request a copy |
Abstract
The study investigates the integration of remote sensing data and machine learning (ML) techniques in streamflow simulation and forecasting in Klang River Basin. The motivation behind the research lies in the need for improved accuracy in streamflow prediction to support effective water resource management and flood control in Malaysia. Five ML models, including K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) were tested, with 25 different model configurations trained on datasets from Malaysia’s Department of Irrigation and Drainage (DID) and climate data from NASA’s Giovanni Portal. Among the models, RF − III showed the best performance with SMAPE, MdAPE and Lp values of 0.36 and 0.37 and 0.908, respectively. Further analysis underscores air temperature’s substantial impact on streamflow prediction, deviating significantly from the benchmark value. The findings contribute to advancements in streamflow forecasting, offering potential applications in flood management and water resource planning for the region.
| Item Type: | Article |
|---|---|
| Keyword: | Rainfall-runoff Machine learning Data-driven modelling Water resource planning |
| Subjects: | Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software Q Science > QC Physics > QC1-999 Physics > QC851-999 Meteorology. Climatology Including the earth's atmosphere |
| Department: | FACULTY > Faculty of Engineering |
| Depositing User: | ABDULLAH BIN SABUDIN - |
| Date Deposited: | 04 Dec 2024 15:37 |
| Last Modified: | 04 Dec 2024 15:37 |
| URI: | https://eprints.ums.edu.my/id/eprint/42112 |
Actions (login required)
 |
View Item |