Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation
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- @Article{Ahmadi:2021:AWM,
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author = "Farshad Ahmadi and Saeid Mehdizadeh and
Babak Mohammadi and Quoc Bao Pham and Thi Ngoc Canh Doan and
Ngoc Duong Vo",
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title = "Application of an artificial intelligence technique
enhanced with intelligent water drops for monthly
reference evapotranspiration estimation",
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journal = "Agricultural Water Management",
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year = "2021",
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volume = "244",
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pages = "106622",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, empirical models, intelligent
water drops, reference evapotranspiration, support
vector regression",
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ISSN = "0378-3774",
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bibsource = "OAI-PMH server at oai.repec.org",
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oai = "oai:RePEc:eee:agiwat:v:244:y:2021:i:c:s0378377420321697",
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URL = "https://www.sciencedirect.com/science/article/pii/S0378377420321697",
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DOI = "doi:10.1016/j.agwat.2020.106622",
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abstract = "Reference evapotranspiration (ET0) is one of the most
important parameters, which is required in many fields
such as hydrological, agricultural, and climatological
studies. Therefore, its estimation via reliable and
accurate techniques is a necessity. The present study
aims to estimate the monthly ET0 time series of six
stations located in Iran. To achieve this objective,
gene expression programming (GEP) and support vector
regression (SVR) were used as standalone models. A
novel hybrid model was then introduced through coupling
the classical SVR with an optimisation algorithm,
namely intelligent water drops (IWD) (i.e.,
SVR{$-$}IWD). Two various types of scenarios were
considered, including the climatic data- and antecedent
ET0 data-based patterns. In the climatic data-based
models, the effective climatic parameters were
recognised by using two pre-processing techniques
consisting of {$\tau$} Kendall and entropy. It is
worthy to mention that developing the hybrid SVR-IWD
model as well as using the {$\tau$} Kendall and entropy
approaches to discern the most influential weather
parameters on ET0 are the innovations of current
research. The results illustrated that the applied
pre-processing methods introduced different climatic
inputs to feed the models. The overall results of
present study revealed that the proposed hybrid SVR-IWD
model outperformed the standalone SVR one under both
the considered scenarios when estimating the monthly
ET0. In addition to the mentioned models, two types of
empirical equations were also used including the
Hargreaves{$-$}Samani (H{$-$}S) and
Priestley{$-$}Taylor (P{$-$}T) in their original and
calibrated versions. It was concluded that the
calibrated versions showed superior performances
compared to their original ones.",
- }
Genetic Programming entries for
Farshad Ahmadi
Saeid Mehdizadeh
Babak Mohammadi
Quoc Bao Pham
Thi Ngoc Canh Doan
Ngoc Duong Vo
Citations