Generalized gene expression programming models for estimating reference evapotranspiration through cross-station assessment and exogenous data supply

Kazemi, Mohammad Hossein; Majnooni-Heris, Abolfazl; Kişi, Özgür; Shiri, Jalal

doi:10.1007/s11356-020-10916-8

Cited by 18 publications

(4 citation statements)

References 27 publications

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“…Solar radiation plays a vital role in the quantification of ETo, in addition to temperature. Numerous ETo modelling studies have documented the efficacy of solar radiation to improve the predictive performance of soft computing models (Fan et al 2018;Petković et al 2020;Üneş et al 2020;Kazemi et al 2021). Radiation-based empirical models commonly use temperature and solar radiation as the input parameters for estimation, which have always resulted in a lower outcome compared to the prediction performance of soft computing models employing the same input parameters for modelling (Antonopoulos and Antonopoulos 2017;Chia et al 2020a;Zhu et al 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Minimal input parameter-based ETo modelling research has acquired wide recognition in hydro-meteorological communities, especially in developing nations where weather stations and data collection methods are few (Adamala et al 2015;Debnath et al 2015;Roy 2021). Among these, certain recent studies have implemented the AI techniques such as artificial neural networks (ANN) and their derivatives (Ferreira et al 2019;Reis et al 2019;Ferreira and da Cunha 2020a;Sowmya et al 2020;Bellido-Jiménez et al 2021;Kaya et al 2021), adaptive neuro fuzzy inference systems (ANFIS) (Petković et al 2020;Üneş et al 2020), support vector machine (SVM) and their hybrid variants (Chia et al 2020a;Tikhamarine et al 2020;Ahmadi et al 2021), tree based soft computing methods (Fan et al 2018;Wu et al 2020), gene expression programming (GEP) (Kazemi et al 2021;Muhammad et al 2021), hybrid ML models (Mohammadi and Mehdizadeh 2020;Zhu et al 2020;Kisi et al 2021;Gong et al 2021), and ensemble of ML models (Wu et al 2021;Martín et al 2021) for the ETo modelling. Most of these studies have reported success by experimenting with different input parameters for modelling, such as only temperature (Bellido-Jiménez et al 2021), a combination of temperature and solar radiation (Chia et al 2020a), temperature and relative humidity (Ferreira and da Cunha 2020a), temperature and wind speed (Nagappan et al 2020) or a variety of other combinations to minimize meteorological data usage.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

2021

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Hydro-agrological research considers the reference evapotranspiration (ETo), driven by meteorological variables, crucial for achieving precise irrigation in precision agriculture. ETo modelling based on a single meteorological parameter would be beneficial in places where the collection of climatic parameters is challenging. The aim of this research is to develop a deep neural network (DNN) architecture that predicts daily ETo with a single input parameter selected based on the feature importance (FI) score generated by the machine learning techniques, random forest (RF), and extreme gradient boosting (XGBoost). This study also investigated the potential of SHapley Additive exPlanations to interpret and validate the outcomes of the feature selection methods by assessing the contributions of each feature to the ETo prediction. These methods recommended solar radiation as a significant parameter in the datasets of three California Irrigation Management System (CIMIS) weather stations located in distinct ETo zones. Three ETo models (DNN-Ret, XGB-Ret, and RF-Ret) were built using solar radiation as the sole input, and CIMIS ETo as the output. The performance evaluation of the developed models proved that DNN-Ret outperformed XGB-Ret and RF-Ret regardless of the dataset, with coefficients of determination (R 2 ) ranging from 0.914 to 0.954 in the local scenario, with an average decrease of 8–9.5% in mean absolute error and root mean squared error, and an improvement of 2.6–2.9% in Nash–Sutcliffe efficiency and 1.7–2% increase in R 2 . The overall result analysis highlighted the efficiency of DNN-Ret in the single input parameter based ETo modelling in diverse climatic zones.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

2021

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“…The contributions of these variables to ET o prediction will differ depending on the climatic zone. Solar radiation, for example, was found to be the most important contributing variable in the ET o estimation in the majority of climatic zones (Kazemi et al, 2021; Ravindran et al, 2021). Temperature and humidity also contribute considerably to ET o estimation than wind speed in CIMIS datasets (Ravindran et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…The ability of artificial intelligence (AI) to portray the non‐linear relationship between inputs and ET o without depicting the complex process behind it led to various machine learning (ML) based ET o modelling research, especially those that utilized minimum, easily available, meteorological parameters. Most of this research was executed in regions where the essential meteorological data is unavailable and explored traditional ML to deep learning (DL) architectures such as artificial neural networks (ANN) and its derivatives (Ferreira & da Cunha, 2020; Kaya et al, 2021; Nagappan et al, 2020; Sowmya et al, 2020), support vector machine (SVM) and its hybrid variants (Chia, Huang, & Koo, 2020; Tikhamarine et al, 2020), adaptive neuro fuzzy inference systems (Gonzalez del Cerro et al, 2021; Üneş et al, 2020), gene expression programming (Kazemi et al, 2021), tree based soft computing methods (Fan et al, 2018; Rashid Niaghi et al, 2021; Salam & Islam, 2020), hybrid ML models (Chen et al, 2020; Kisi et al, 2021; Muhammad et al, 2021; Zhu et al, 2020), and ensemble of ML models (Wu et al, 2021). These investigations demonstrated excellence as well as superiority over traditional empirical approaches, when temperature and radiation data were included in the input set.…”

Section: Introductionmentioning

confidence: 99%

An automated machine learning methodology for the improved prediction of reference evapotranspiration using minimal input parameters

Sowmya

Kumar

Ambat

et al. 2022

Hydrological Processes

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An accurate and minimal meteorological parameter‐based reference evapotranspiration (ETo) computational model is highly essential to design a cost‐effective and automated solution for precise water allocation in various agricultural and hydrological applications. Automated machine learning (AutoML), a substantially progressive machine learning (ML) paradigm, expedites the data science applications in these domains by automating and alleviating the tedious tasks of developing ML pipelines, particularly in non‐linear hydrological process modelling. This paper proposes an AutoML solution for daily EToprediction in a limited input parameter scenario, for the first time in EToestimation research. Two AutoML frameworks, AutoGluon‐Tabular (AGT) and H2O AutoML were implemented using daily meteorological data of a humid tropical climatic region of Kerala, India and evaluated its performance with the radiation‐based empirical methods and traditional ML methods. The developed AutoML models imitated the input parameter combinations of radiation‐based empirical models such as Priestley‐Taylor, Makkink, Turc and Ritchie, with the Penman‐Monteith EToserving as the target for modelling process. An improvement of mean absolute error from 0.119 to 0.078 mm/day, mean squared error from 0.035 to 0.017 mm/day, root mean squared error from 0.186 to 0.129 mm/day, root mean squared logarithmic error from 0.078 to 0.026 mm/day, and coefficient of determination (R2) from 0.971 to 0.985 observed in the AutoML model, AGT relative to the empirical model, Turc describes its enhanced performance capability. For demonstrating the superiority of AutoML to traditional ML techniques, another set of AutoML models were also developed based on California Irrigation Management Information System datasets and evaluated them with existing study results in literature. The overall result analysis demonstrated the superiority of AGT model in EToprediction in all the weather stations considered in this study. The results of the study confirm that AutoML techniques can be applied to any hydrological dataset and provide an effective automated solution for hydrological process modelling.

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A comparative study of remote sensing and gene expression programming for estimation of evapotranspiration in four distinctive climates

Barzkar

Shahabi

Niazmradi

et al. 2021

Stoch Environ Res Risk Assess

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Generalized gene expression programming models for estimating reference evapotranspiration through cross-station assessment and exogenous data supply

Cited by 18 publications

References 27 publications

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

An automated machine learning methodology for the improved prediction of reference evapotranspiration using minimal input parameters

A comparative study of remote sensing and gene expression programming for estimation of evapotranspiration in four distinctive climates

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