Development of a Temperature-Based Model Using Machine Learning Algorithms for the Projection of Evapotranspiration of Peninsular Malaysia
Abstract
:1. Introduction
2. Study Area
3. Data
4. Methodology
- Gene expression programming (GEP) was used to generate a temperature-based empirical equation for the estimation of ET for peninsular Malaysia
- The accuracy of the newly developed empirical model was assessed by comparing its performance with the existing temperature-based empirical methods
- GCMs were used to downscale and project temperatures at the study locations.
- The newly developed GEP ET model was used to project the ET of Peninsular Malaysia from the GCM projected temperature
4.1. Gene Expression Programming (GEP)
- Selection of fitness function or a set of fitness functions. The Nash–Sutcliff Efficiency (NSE) was considered for the fitness function.
- Selection of a set of terminals and a set of functions. The inputs were selected according to their influence on ET.
- Creation of chromosomes from the selected terminals and functions.
- Setting the chromosomal architecture.
- Selection of the linking function.
- Selection of genetic operators.
4.2. Temperature-Based ET Methods
4.3. Temperature Downscaling and Projections
5. Results & Discussion
5.1. Development of Temperature-Based ET Equations Using GEP
5.2. Performance Evaluation of GEP ET Equations
5.3. Projection of ET
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model Developing Institute | Model Name | Resolution (Lon × Lat) |
---|---|---|
Beijing Climate Center, China | BCC-CSM1.1 | 2.8° × 2.8° |
National Center for Atmospheric Research, USA | CCSM4 | 1.25° × 0.94° |
Met Office Hadley Centre, UK | HadGEM2-ES | 1.87° × 1.25° |
Atmosphere and Ocean Research Institute, The University of Tokyo, Japan | MIROC-ESM | 2.8° × 2.8° |
Bjerknes Centre for Climate Research, Norwegian Meteorological Institute, Norway | NorESM1-M | 2.5° × 1.9° |
Geophysical Fluid Dynamics Laboratory, USA | GFDL-CM3 | 2.5° × 2.0° |
Commonwealth Scientific and Industrial Research Organization, Australia | CSIRO-Mk3.6.0 | 1.86° × 1.87° |
Institut Pierre Simon Laplace, France | IPSL-CM5A-MR | 1.26° × 2.5° |
Meteorological Research Institute, Japan | MRI-CGCM3 | 1.12° × 1.12° |
No | Model | Input Parameter | Equation |
---|---|---|---|
1 | FAO Blaney-Criddle [53] | Tmean | |
2 | Linacre [54] | Tmean | |
3 | Kharrufa [55] | Tmean | |
4 | Hargreaves and Samani [56] | Tmean, Tmin, Tmax, Ra | |
5 | Trajkovic [57] | Tmean, Tmin, Tmax, Ra | |
6 | Ravazzani [58] | Tmean, Tmin, Tmax, Ra |
Station Name | Model |
---|---|
Alor Setar | [(0.708527 + Tmax + (Tmax/−0.570404)) × ((Tmax + Tmin) × (0.708527/Tmax))] + [0.271 × Tmin−4.310577] + [Tmax−((39.983448 + Tmin)/(6.337066 + Tmin))] |
Bayan Lepas | [−2.659455/(Tmin + ((Tmin + Tmax) × (−2.659455/Tmax)))] − [9.144561] + [((Tmax + 2.659455) × Tmax)/(Tmin + 1.002563 + 2 × Tmax)] |
Ipoh | [−9.89798−Tmin] + [(2 × Tmax−Tmin + 2.245544)/8.36405] + [Tmin + 7.706391] |
Kota Bharu | ((Tmax/4.688568) − (4.688568/Tmin) − 4.688568) + (−1.946625/((Tmin× −1.946625) + Tmax + 5.589417)) + (Tmax/(Tmin − ((Tmin + Tmax)/(Tmin^2)))) |
Kuala Terenggannu | (Tmax/5.405304) + ((2 × Tmax + 2 × Tmin)/((Tmax^0.13211) + (Tmax × 5.950409)))+ ((Tmin + Tmax)/(12.843872 − Tmax)) |
Kuantan | (((Tmin × −1.63205) + (Tmin/−1.63205))/((Tmin × Tmax)/(Tmin − 1.63205))) + (((−4.044677/(Tmin/Tmax)) + 1.682221 − Tmax)/−4.044677) − 4.384247 |
Melaka | ((−8.459076 + Tmax)/Tmax) + ((Tmax − Tmin)/((2 × Tmax)/(0.269501 + Tmax))) + (−8.391388/(Tmin − 18.419738)) |
Muadzam Shah | [((Tmin − Tmax)/(7.168945 − Tmax))/((7.168945/Tmax) − 7.411835)] + [((−6.097015/Tmin) − (4.724793 + Tmin))/((Tmax × 4.724793) + (−6.097015/Tmax))] + [((Tmin − Tmax)/(−9.070129 − Tmax))/(1.893555/Tmin)] |
Senai | [(−5.88443 − Tmin + Tmax)/(−3.50461 + 2Tmin)] + [−10.39233/(Tmin − 14.90024)] + [2Tmax2/Tmin2] |
Subang | [−4.079162/Tmin] + [(6.476776 − Tmin)/(Tmax/(Tmin − Tmax))] + [−14.214936/(−12.751648 + Tmin)] |
Station Name | NSE | MAE | RMSE | md |
---|---|---|---|---|
Alor Setar | 0.98 | 4.7 | 7.9 | 1.0 |
Senai | 0.94 | 5.4 | 7.6 | 0.9 |
Bayan Lepas | 0.92 | 6.6 | 8.9 | 0.9 |
Ipoh | 0.81 | 7.3 | 9.3 | 0.7 |
Muadzam Shah | 0.79 | 6.9 | 8.9 | 0.8 |
Subang | 0.76 | 6.1 | 7.6 | 0.7 |
Kuantan | 0.99 | 3.3 | 4.8 | 1.0 |
Melaka | 0.94 | 6.1 | 9.3 | 0.9 |
Kota Bharu | 0.87 | 10.1 | 12.1 | 0.8 |
Kuala Terenggannu | 0.95 | 5.8 | 10.3 | 0.9 |
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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
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 StyleMuhammad, 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
APA StyleMuhammad, M. K. I., Shahid, S., Hamed, M. M., Harun, S., Ismail, T., & Wang, X. (2022). Development of a Temperature-Based Model Using Machine Learning Algorithms for the Projection of Evapotranspiration of Peninsular Malaysia. Water, 14(18), 2858. https://doi.org/10.3390/w14182858