Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

doi:10.1016/j.compag.2017.05.002

Cited by 195 publications

(81 citation statements)

References 37 publications

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“…Mehdizadeh et al [38] tested several methods, namely gene expression programming, support vector machine, multivariate adaptive regression splines for ETo modeling. With selected two variables combinations (relative humidity and temperature), they had slightly higher RMSE and lower R 2 in comparison with the RNN models used in this study.…”

Section: Model Training and Tesing Evaluationmentioning

confidence: 99%

Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management

et al. 2020

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Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorological sites (North Cape, Summerside, Harrington, and Saint Peters) were selected across Prince Edward Island (PEI), Canada to form a PEI dataset from mean values of the four sites’ climatic variables for capturing climatic variability from all parts of the province. Based on subset regression analysis, the highest contributing climatic variables, namely maximum air temperature and relative humidity, were selected as input variables for RNNs’ training (2011–2015) and testing (2016–2017) runs. The results suggested that the LSTM and bidirectional LSTM are suitable methods to accurately (R2 > 0.90) estimate ETo for all sites except Harrington. Testing period (2016–2017) root mean square errors were recorded in range of 0.38–0.58 mm/day for all sites. No major differences were observed in accuracy of LSTM and bidirectional LSTM. Another objective of this study was to highlight the potential gap between ETO and rainfall for assessing agriculture sustainability in Prince Edward Island. Analyses of the data highlighted that the cumulative ETo surpassed the cumulative rainfall potentially affecting yield of major crops in the island. Therefore, agriculture sustainability requires viable options such as supplemental irrigation to replenish the crop water requirements as and when needed.

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Section: Model Training and Tesing Evaluationmentioning

confidence: 99%

Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management

et al. 2020

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“…They reported better performance of the GEP model with four inputs compared to the empirical models. Mehdizadeh, Behmanesh, and Khalili (2017) investigated the performance of SVM [Poly and radial basis function (RBF)], GEP, multivariate adaptive regression spline (MARS) and the empirical models to determine the ET o at 44 meteorological stations in Iran. The study reported that the MARS and SVM-RBF models performed better than the SVM-Poly and GEP models.…”

Section: State Of the Art: Evolutionary Computing (Ec) Models For Et mentioning

confidence: 99%

Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions

Wang

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Shahid

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Evapotranspiration is one of the most important components of the hydrological cycle as it accounts for more than two-thirds of the global precipitation losses. Indeed, the accurate prediction of reference evapotranspiration (ET o) is highly significant for many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation of ET o is a major concern in hydrology. ET o can be estimated using different approaches, including field measurement, empirical formulation and mathematical equations. Most recently, advanced machine learning models have been developed for the estimation of ET o. Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ET o. The current research is devoted to providing a new milestone in the implementation of the EC algorithm for the modeling of ET o. A comprehensive review is conducted to recognize the feasibility of EC models and their potential in simulating ET o in a wide range of environments. Evaluation and assessment of the models are also presented based on the review. Finally, several possible future research directions are proposed for the investigations of ET o using EC.

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“…The calibration was carried out by means of simple linear regression with the ET o estimated by the PM method with full data as benchmark, as suggested by Allen et al (1998) The multivariate adaptive regression splines (MARS) is a nonparametric regression analysis capable of determining the relations between input and output variables without any assumption, modeling the nonlinearities and interactions, besides of automatically choose the predictors variables of real importance. In MARS base functions are set at different intervals of the independent variables, the initial and final points of these intervals are called knots (MEHDIZADEH et al, 2017). The operation of the base functions occurs according to the following equations.…”

Section: Methodsmentioning

confidence: 99%

“…These almost always perform better than traditional methods (SHIRI et al, 2014;FENG et al, 2017;MEHDIZADEH et al, 2017). According to Mehdizadeh et al (2017) these techniques are valid in modeling of complex and nonlinear problems, such as ET o , Among the soft computing methods, the multivariate adaptive regression splines (MARS) is a regression analysis that presents the possibility to use the developed model in the form of an algebraic equation, unlike models such as neural networks, which require the implementation of a specific software for its use. This method is a nonparametric regression initially proposed by Friedman (1991).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reference evapotranspiration estimated from air temperature using the MARS regression technique

Ferreira¹,

Duarte²,

Araújo³

et al. 2018

Biosci. J.

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ABSTRACT:The reference evapotranspiration (ET o ) is an important component for determining the water requirements of the crops. In order to estimate this variable accurately, the Food and Agriculture Organization (FAO) proposed the Penman-Monteith equation, however, this demands a large number of meteorological data, which restricts its use. In this context, this study compares the performance of the Penman-Monteith equation using only measured air temperature (PMT) and the Hargreaves-Samani (HS) equation with the performance of the multivariate adaptive regression splines (MARS) technique for the daily ET o estimation with only air temperature data. For the study, daily meteorological data from 2002 to 2016 were used. The data were collected from weather stations located in Florianópolis-SC, Manaus-AM and Petrolina-PE, being these selected in order to capture different climatic conditions. MARS models were developed for each weather station and the PMT e HS equations were locally calibrated. The performances of the original and calibrated equations and MARS models were evaluated based on the statistical indices root mean square error, mean absolute error, mean bias error and coefficient of determination. The ET o estimated by the Penman-Monteith method with full data was used as reference for the development of the MARS models, calibration of the equations and for the performance evaluation of the models under study. The calibration of the HS and PMT equations promoted better performances in relation to the original equations, improving the methods accuracy. The MARS technique presented good performance, outperforming the original and calibrated PMT and HS equations, with lower error values and higher coefficient of determination, and can be considered as an alternative to empirical methods.

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Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration

Cited by 195 publications

References 37 publications

Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management

Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management

Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions

Reference evapotranspiration estimated from air temperature using the MARS regression technique

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