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Article

Development of a Temperature-Based Model Using Machine Learning Algorithms for the Projection of Evapotranspiration of Peninsular Malaysia

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Department of Water & Environmental Engineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
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Construction and Building Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport (AASTMT), B 2401 Smart Village, Giza 12577, Egypt
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State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
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Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China
*
Author to whom correspondence should be addressed.
Academic Editor: Yaoming Ma
Water 2022, 14(18), 2858; https://doi.org/10.3390/w14182858
Received: 10 August 2022 / Revised: 5 September 2022 / Accepted: 6 September 2022 / Published: 13 September 2022
(This article belongs to the Section Hydrology)
Reliable projections of evapotranspiration (ET) are important for agricultural and water resources development, planning, and management. However, ET projections using well established empirical models suffer from uncertainty due to their dependency on many climatic variables. This study aimed to develop temperature-based empirical ET models using Gene Expression Programming (GEP) for the reliable estimation and projection of ET in peninsular Malaysia within the context of global warming. The efficiency of the GEP-generated equation was compared to the existing methods. Finally, the GEP ET formulas were used to project ET from the downscaled and projected temperature of nine global climate models (GCMs) for four Representative Concentration Pathways (RCPs), namely, RCP 2.6, 4.5, 6.0, and 8.5, at ten locations of peninsular Malaysia. The results revealed improved performance of GEP models in all standard statistics. Downscaled temperatures revealed a rise in minimum and maximum temperatures in the range of 2.47–3.30 °C and 2.79–3.24 °C, respectively, during 2010–2099. The ET projections in peninsular Malaysia showed changes from −4.35 to 7.06% for RCP2.6, −1.99 to 16.76% for RCP4.5, −1.66 to 22.14% for RCP6.0 and −0.91 to 39.7% for RCP8.5 during 2010−2099. A higher rise in ET was projected over the northern peninsula than in the other parts. View Full-Text
Keywords: evapotranspiration projections; machine learning; climate change; temperature; GEP evapotranspiration projections; machine learning; climate change; temperature; GEP
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MDPI and ACS Style

Muhammad, M.K.I.; Shahid, S.; Hamed, M.M.; Harun, S.; Ismail, T.; Wang, X. Development of a Temperature-Based Model Using Machine Learning Algorithms for the Projection of Evapotranspiration of Peninsular Malaysia. Water 2022, 14, 2858. https://doi.org/10.3390/w14182858

AMA Style

Muhammad MKI, Shahid S, Hamed MM, Harun S, Ismail T, Wang X. Development of a Temperature-Based Model Using Machine Learning Algorithms for the Projection of Evapotranspiration of Peninsular Malaysia. Water. 2022; 14(18):2858. https://doi.org/10.3390/w14182858

Chicago/Turabian Style

Muhammad, Mohd Khairul Idlan, Shamsuddin Shahid, Mohammed Magdy Hamed, Sobri Harun, Tarmizi Ismail, and Xiaojun Wang. 2022. "Development of a Temperature-Based Model Using Machine Learning Algorithms for the Projection of Evapotranspiration of Peninsular Malaysia" Water 14, no. 18: 2858. https://doi.org/10.3390/w14182858

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