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https://ptsldigital.ukm.my/jspui/handle/123456789/486993Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Othman Jaafar, Prof. Dr. | - |
| dc.contributor.author | Zaher Mundher Yaseen (P77769) | - |
| dc.date.accessioned | 2023-10-11T02:27:19Z | - |
| dc.date.available | 2023-10-11T02:27:19Z | - |
| dc.date.issued | 2017-05-26 | - |
| dc.identifier.other | ukmvital:120359 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/486993 | - |
| dc.description | Stream-flow forecasting can yield important information for hydrological applications including sustainable design of rural and urban water management systems, optimization of water resource allocations, water use, pricing and water quality assessment, and agriculture and irrigation operations. The motivation for exploring and developing expert predictive models is an ongoing endeavor for hydrological applications. This is owing to the fact that stream-flow process including daily and monthly time scales are characterised by highly non-linear and non-stationary pattern. In this research, the potential of a relatively new non-tuned data-driven method, namely the extreme learning machine (ELM) method, was scrutinized in order for developing accurate monthly and daily stream-flow forecasting model for Tigris and Kelantan rivers representing two different climatic zones Iraq (semi-arid environment) and Malaysia (tropical environment), respectively. The significance of the ELM algorithm is its modeless procedure which is one of the major drawback of the existing Artificial Intelligent (AI) models used for such application. Based on partial autocorrelation statistical functions on historical stream-flow data, a set of input combinations with lagged stream-flow values are employed to establish the best forecasting model. A comparative investigation is conducted to evaluate the performance of the ELM compared to other data-driven models: support vector regression (SVR) and generalized regression neural network (GRNN). The forecasting metrics defined as the coefficient of determination (r), Nash-Sutcliffe efficiency (Ens), Willmotts Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) computed between the observed and forecasted stream-flow data are employed to assess the ELM models effectiveness. The results revealed that the ELM model outperformed the SVR and the GRNN models across a number of statistical measures for both inspected rivers. In quantitative terms (e.g., Tigris River-semi-arid environment), superiority of ELM over SVR and GRNN models was exhibited by Ens = 0.578, 0.378 and 0.144, r = 0.799, 0.761 and 0.468 and WI = 0.853, 0.802 and 0.689, respectively and the ELM model attained lower RMSE and MAE values by about 17.65% and 21.29 (relative to SVR) and by about 29.78% and 30.90 (relative to GRNN). It was found also that the ELM algorithm was a useful alternative over the exist predictive models in the literature that can be implemented effectively for semi-arid and tropical zones. Based on the findings of this study, several recommendations were suggested for further exploration of the ELM model in hydrological forecasting problems.,Certification of Master's / Doctoral Thesis" is not available | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | - |
| dc.rights | UKM | - |
| dc.subject | Machine learning | - |
| dc.subject | Streamflow -- Forecasting | - |
| dc.subject | Stream measurements -- Iraq | - |
| dc.subject | Stream measurements -- Malaysia | - |
| dc.subject | Universiti Kebangsaan Malaysia -- Dissertations | - |
| dc.subject | Dissertations, Academic -- Malaysia | - |
| dc.title | Extreme learning machine computing model for stream-flow forecasting | - |
| dc.type | Theses | - |
| dc.format.pages | 167 | - |
| dc.identifier.callno | Q325.5.Y337 2017 3 tesis | - |
| dc.identifier.barcode | 005327(2021)(PL2) | - |
| Appears in Collections: | Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_120359+SOURCE1+SOURCE1.0.PDF Restricted Access | 1.92 MB | Adobe PDF |  View/Open |
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