Evaporation modelling using different machine learning techniques

Wang, Lunche; Kisi, Özgür; Hu, Bo; Bilal, Muhammad; Zounemat‐Kermani, Mohammad; Li, Hui

doi:10.1002/joc.5064

Cited by 72 publications

(27 citation statements)

References 47 publications

(71 reference statements)

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“…Results proved the high ability of the FG model in pan evaporation estimation. Wang, Kisi, Hu, et al (2017) predicted pan evaporation using MLPNN, GRNN, ANFIS with grid partition (ANFIS-GP), MARS, MLR, FG, LSSVM, and SS models in China. The results showed that the AI models provided the most accuracy than the SS and MLR techniques.…”

Section: Introductionmentioning

confidence: 99%

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Malik

Kumar

Kim

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and 'M5Tree' were assessed to simulate the pan evaporation in monthly scale (EP m ) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe's Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Malik

Kumar

Kim

et al. 2020

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

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“…These authors have, for other regions, estimated evapotranspiration from the same meteorological data required by the Penman-Monteith, as well as from scarce data. Methods such as neural networks, regression trees, support vector machines and many others present, for the most part, a heuristic characteristic coupled with a high ability to generalize and model patterns (Wang et al, 2017b(Wang et al, , 2017c.…”

Section: Introductionmentioning

confidence: 99%

Heuristic methods applied in reference evapotranspiration modeling

et al. 2018

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The importance of the precise estimation of evapotranspiration is directly related to sustainable water usage. Since agriculture represents 70% of Brazil’s water consumption, adequate and efficient application of water may reduce the conflicts over the use of water among the multiple users. Considering the importance of accurate estimation of evapotranspiration, the objective of the present study was to model and compare the reference evapotranspiration from different heuristic methodologies. The standard Penman-Monteith method was used as reference for evapotranspiration, however, to evaluate the heuristic methodologies with scarce data, two widely known methods had their performances assessed in relation to Penman-Monteith. The methods used to estimate evapotranspiration from scarce data were Priestley-Taylor and Thornthwaite. The computational techniques Stepwise Regression (SWR), Random Forest (RF), Cubist (CB), Bayesian Regularized Neural Network (BRNN) and Support Vector Machines (SVM) were used to estimate evapotranspiration with scarce and full meteorological data. The results show the robustness of the heuristic methods in the prediction of the evapotranspiration. The performance criteria of machine learning methods for full weather data varied from 0.14 to 0.22 mm d-1 for mean absolute error (MAE), from 0.21 to 0.29 mm d-1 for root mean squared error (RMSE) and from 0.95 to 0.99 coefficient of determination (r²). The computational techniques proved superior performance to established methods in literature, even in scenarios of scarce variables. The BRNN presented the best performance overall.

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“…In fact, GA is an oversimplification of biological evolution [51]. GP, introduced by [52,53], solves the problem of fixed length solutions (as stated for GA) by creating nonlinear entities. Each entity (parse tree) has a distinguished shape and size.…”

Section: Methodsmentioning

confidence: 99%

A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates

et al. 2019

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In the current research, gene expression programming (GEP) was applied to model reference evapotranspiration (ETo) in 18 regions of Iran with limited meteorological data. Initially, a genetic algorithm (GA) was employed to detect the most important variables for estimating ETo among mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), sunshine (n), and wind speed (WS). The results indicated that a coupled model containing the Tmean and WS can predict ETo accurately (RMSE = 0.3263 mm day −1 ) for arid, semiarid, and Mediterranean climates. Therefore, this model was adjusted using the GEP for all 18 synoptic stations. Under very humid climates, it is recommended to use a temperature-based GEP model versus wind speed-based GEP model. The optimal and lowest performance of the GEP belonged to Shahrekord (SK), RMSE = 0.0650 mm day −1 , and Kerman (KE), RMSE = 0.4177 mm day −1 , respectively. This research shows that the GEP is a robust tool to model ETo in semiarid and Mediterranean climates (R 2 > 0.80). However, GEP is recommended to be used cautiously under very humid climates and some of arid regions (R 2 < 0.50) due to its poor performance under such extreme conditions. Atmosphere 2019, 10, 311 2 of 15 sediment [9], predicting velocity in compound channels [10], characterizing risks in water supply systems [11], and modeling evaporation [12].Although the GEP has been developed in water resources studies, the application of this technique for ETo modeling is limited. Some of the successful applications of the GEP to estimate ETo can be listed as follows.Parasuraman et al. [3] modeled ETo using only ground temperature and net radiation. Irmak and Kamble [13] investigated evapotranspiration data assimilation with the GA and soil, water, atmosphere, and plant (SWAP) model for on-demand irrigation. The data assimilation methodology obtained from the present research can be considered a practical tool at the field scale for scheduling the irrigation estimated by remote sensing-based evapotranspiration. The results showed that the GA was effectively able to determine the terms included in the fitness function, and parameters were predicted reasonably, especially if only four variables were included. Traore and Guven [14] developed regional-specific numerical models of ETo using the GEP in Sahel. Statistically, the GEP was an effectual modeling tool for the successful computation of ETo under the study area. The results indicated that, using the GEP model, it would be possible to formulate an accurate and applicable numerical equation for each region by irrigation systems for the manual computation of ETo under the study area, where sufficient meteorological variable is often missing. Shiri [15] claimed that GEP outperforms empirical model to calculate ETo in hyper arid regions over Iran. Traore et al. [16] found that GEP is a robust tool to model ETo in in Jiangsu province, China. Mattar [17] demonstrated that GEP is a more accurate method than empirical equations to ...

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Evaporation modelling using different machine learning techniques

Cited by 72 publications

References 47 publications

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Heuristic methods applied in reference evapotranspiration modeling

A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates

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