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Items where Author is "Mohamad Hamzah, Firdaus"

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Number of items: 6.

Article

Hamzah, Fatimah Bibi and Mohamad Hamzah, Firdaus and Mohd Razali, Siti Fatin and El-Shafie, Ahmed (2022) Multiple imputations by chained equations for recovering missing daily streamflow observations: A case study of Langat River basin in Malaysia. Hydrological Sciences Journal-Journal des Sciences Hydrologiques, 67 (1). pp. 137-149. ISSN 0262-6667, DOI https://doi.org/10.1080/02626667.2021.2001471.

Allawi, Mohammed Falah and Jaafar, Othman and Mohamad Hamzah, Firdaus and Koting, Suhana and Mohd, Nuruol Syuhadaa and El-Shafie, Ahmed (2019) Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance. Knowledge-Based Systems, 163. pp. 907-926. ISSN 0950-7051, DOI https://doi.org/10.1016/j.knosys.2018.10.013.

Allawi, Mohammed Falah and Jaafar, Othman and Mohamad Hamzah, Firdaus and El-Shafie, Ahmed (2019) Novel reservoir system simulation procedure for gap minimization between water supply and demand. Journal of Cleaner Production, 206. pp. 928-943. ISSN 0959-6526, DOI https://doi.org/10.1016/j.jclepro.2018.09.237.

Allawi, Mohammed Falah and Jaafar, Othman and Mohamad Hamzah, Firdaus and Ehteram, Mohammad and Hossain, Md Shabbir and El-Shafie, Ahmed (2018) Operating a reservoir system based on the shark machine learning algorithm. Environmental Earth Sciences, 77 (10). p. 366. ISSN 1866-6280, DOI https://doi.org/10.1007/s12665-018-7546-8.

Allawi, Mohammed Falah and Jaafar, Othman and Mohamad Hamzah, Firdaus and Mohd, Nuruol Syuhadaa and Deo, Ravinesh C. and El-Shafie, Ahmed (2018) Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region. Theoretical and Applied Climatology, 134 (1-2). pp. 545-563. ISSN 0177-798X, DOI https://doi.org/10.1007/s00704-017-2292-5.

Allawi, Mohammed Falah and Jaafar, Othman and Ehteram, Mohammad and Mohamad Hamzah, Firdaus and El-Shafie, Ahmed (2018) Synchronizing Artificial Intelligence Models for Operating the Dam and Reservoir System. Water Resources Management, 32 (10). pp. 3373-3389. ISSN 0920-4741, DOI https://doi.org/10.1007/s11269-018-1996-3.

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