Abstract
Reference evapotranspiration (ETo) is one of the foremost elements of the hydrology cycle which is essential for water resources management and irrigation applications. The current study is emphasized on the implementation of evolutionary computing models (i.e., gene expression programming (GEP)) for the simulation daily ETo in different locations of Peninsular Malaysia. The ETo models are developed using various input combinations of meteorological variables including air temperature (mean, maximum, and minimum), relative humidity, solar radiation, and mean wind speed. The in situ measurements of the ET are used to validate the model’s performance. The performance of the proposed GEP model is also compared with five well-established empirical formulations (EFs) developed based on the related climatological variability. The attained results evidenced the potential of GEP-derived ETo models in terms of all the statistical measures used. The best GEP model attained when all the meteorological variables are incorporated. However, the study revealed that the use of only temperature information can provide substantial predictability compared to EFs at all the studied stations across Peninsular Malaysia. This confirms the applicability of GEP in simulating ETo with fewer meteorological variables. The major advantage of GEP compared to other black box artificial intelligence algorithms is that GEP provides a set of equations which can be used by practitioners for reliable estimation of ETo at field with a fewer meteorological variable and, thus, can have wide applicability in water resources management.
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Data availability
Data are available with corresponding author.
Abbreviations
- AI:
-
Artificial intelligence
- AIC:
-
Akaike information criterion
- ETo:
-
Reference evapotranspiration
- GEP:
-
Gene expression programming
- EFs:
-
empirical formulations
- PM:
-
Penman-Monteith
- ANN:
-
Artificial neural network
- SVM:
-
Support vector machine
- GP:
-
Genetic programming
- ANFIS:
-
Adaptive neuro fuzzy inference system
- HS:
-
Hargreaves-Samani
- MK:
-
Makkink
- TR:
-
Turc
- PT:
-
Priestley-Taylor
- Tmax:
-
Temperature maximum
- Tmean:
-
Temperature mean
- Tmin:
-
Temperature minimum
- RH:
-
Relative humidity
- SR:
-
Solar radiation
- WS:
-
Mean wind speed
- MMD:
-
Malaysia Meteorological Department
- GA:
-
Genetic algorithms
- T:
-
Air temperature
- Sh:
-
Sunshine
- ST:
-
Soil temperature
- RH:
-
Relative humidity
- VP:
-
Vapor pressure
- WD:
-
Wind direction
- RF:
-
Rainfall
- EPR:
-
Evolutionary polynomial regression
- GP:
-
Genetic programming
- NR:
-
Net radiation
- EC-LE:
-
Eddy-covariance-measured latent heat
- ANFIS-GP and ANFIS-SC:
-
Adaptive neuro-fuzzy inference system integrated with grid partition and subtractive clustering
- EC-AET:
-
Eddy covariance-measured actual evapotranspiration
- WA-SVR:
-
Wavelet-support vector regression
- SVR-FFA:
-
Support vector regression-firefly algorithm
- MLR:
-
Multiple linear regression
- MARS:
-
Multivariate adaptive regression spline
- BT:
-
Boosted regression tree
- RFM:
-
Random forest model
- MT:
-
Model tree
- ELM:
-
Extreme learning machine
- DE:
-
Differential evolution
- NN-GP:
-
Neuro-fuzzy-grid partitioning
- NN-SC:
-
Neuro-fuzzy-sub-clustering
- CART:
-
Classification and regression tree
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Modeling is performed using GeneXproTools software.
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Mohd Khairul Idlan Muhammad: modeling, conceptualization, writing up the manuscript. Shamsuddin Shahid: supervision, writing up the manuscript, conceptualization, data analysis. Tarmizi Ismail: supervision, writing up the manuscript, conceptualization, data analysis. Sobri Harun: supervision, conceptualization, writing up the manuscript, data analysis. Ozgur Kisi: validation, investigation, revision, manuscript editing. Zaher Mundher Yaseen: results analysis and discussion, writing up the manuscript, visualization, investigation.
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Muhammad, M.K.I., Shahid, S., Ismail, T. et al. The development of evolutionary computing model for simulating reference evapotranspiration over Peninsular Malaysia. Theor Appl Climatol 144, 1419–1434 (2021). https://doi.org/10.1007/s00704-021-03606-z
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DOI: https://doi.org/10.1007/s00704-021-03606-z