A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland

Fu, Tonglin; Li, Xinrong; Jia, Ruibao; Li, Feng

doi:10.1016/j.jhydrol.2021.126881

Cited by 27 publications

(30 citation statements)

References 34 publications

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“…More recently, Pan et al (2020) used an ensemble of remote sensing, ML and land surface modeling to simulate the ET at global level, while Mosre and Suárez (2021) report the use of ML with in situ remote sensing data to determine the actual ET in arid cold regions. ML algorithms have been also used to predict CO 2 (Guevara-Escobar et al 2020), latent heat flux (Zhao et al 2019;Dou and Yang 2018;Yin et al 2021;Fu et al 2021;Mosre and Suárez 2021), reference evapotranspiration (Anurag et al 2021;Borges et al 2020), and to evaluate terrestrial evapotranspiration at global scale (Pan et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops

et al. 2022

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Background The evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data. Results Our results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: the R2 value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that the R2 values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF. Conclusion ML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems.

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Section: Introductionmentioning

confidence: 99%

Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops

et al. 2022

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“…Accurately estimating ET is a critical prerequisite in environmental management [4][5][6] , especially in desert regions with large areas of artificial sand-binding vegetation, where the sustainability of artificial sand-binding vegetation is determined by the water balance between ET and precipitation 5,7 . In addition, climate change, especially changes in warming and precipitation patterns, will inevitably have a profound impact on the sustainability of artificial vegetation 7,8 . Different from the natural vegetation, artificial sand-binding vegetation is established with speciall purpose and function, the accurate estimation of ET can provide a reference for understanding the water balance and determining the composition, structure, spatial distribution, and scale of artificial sand-binding vegetation in desert regions 9,10 .…”

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confidence: 99%

“…the latent heat of vaporization, solar radiation, relative humidity, air temperature, etc.) in desert regions 4,6,[2][3][4][5][6][7][8][9][10][11][12] . Therefore, constructing the other types of data-driven models to obtain accurate estimating results is highly desirable.Recently, the machine learning (ML) models, including back-propagation neural networks (BPNN) 13 , multilayer perceptron (MLP) 2 , Multilayer artificial neural networks (MLNN) 6 , support vector machine (SVM) 7,12 , extreme learning machine (ELM) 6 , Model tree (MT) 14,15 , random forest (RF) 6 , wavelet neural networks (WNN) 16 , radial basis function (RBF) 17 , etc., have been dramatically employed to estimate evaporation or ET due to its capability of automatically learning features and not requiring any assumptions.…”

mentioning

confidence: 99%

“…in desert regions 4,6,[2][3][4][5][6][7][8][9][10][11][12] . Therefore, constructing the other types of data-driven models to obtain accurate estimating results is highly desirable.Recently, the machine learning (ML) models, including back-propagation neural networks (BPNN) 13 , multilayer perceptron (MLP) 2 , Multilayer artificial neural networks (MLNN) 6 , support vector machine (SVM) 7,12 , extreme learning machine (ELM) 6 , Model tree (MT) 14,15 , random forest (RF) 6 , wavelet neural networks (WNN) 16 , radial basis function (RBF) 17 , etc., have been dramatically employed to estimate evaporation or ET due to its capability of automatically learning features and not requiring any assumptions. As ML models have the defects that the hyperparameters are difficult to adjust by themselves, which significantly decrease the computing accuracy.…”

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confidence: 99%

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Hybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation

2022

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The sustainability of artificial sand-binding vegetation is determined by the water balance between evapotranspiration (ET) and precipitation in desert regions. Consequently, accurately estimating ET is a critical prerequisite for determing the types and spatial distribution of artificial vegetation in different sandy areas. For this purpose, a novel hybrid estimation model was proposed to estimate monthly ET by coupling the deep learning long short term memory (LSTM) with variational mode decomposition (VMD) and whale optimization algorithm (WOA) (i.e., VMD-WOA-LSTM) to estimate the monthly ET in the southeast margins of Tengger Desert. The superiority of LSTM was selected due to its capability of automatically extracting the nonlinear and nonstationary features from sequential data, WOA was employed to optimize the hyperparameters of LSTM, and VMD was used to extract the intrinsic traits of ET time series. The estimating results of VMD-WOA-LSTM has been compared with actual ET and estimation of other hybrid models in terms of standard performance metrics. The results reveale that VMD-WOA-LSTM provide more accurate and reliable estimating results than that of LSTM, the support vector machine (SVM), and the variants of those models. Therefore, VMD-WOA-LSTM could be recommended as an essential auxiliary method to estimate ET in desert regions.

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Predicting Evapotranspiration Using Support Vector Machine Model and Hybrid Gamma Test

Ehteram

Seifi

Banadkooki

2023

Application of Machine Learning Models in Agricultural and Meteorological Sciences

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A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland

Cited by 27 publications

References 34 publications

Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops

Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops

Hybrid the long short-term memory with whale optimization algorithm and variational mode decomposition for monthly evapotranspiration estimation

Predicting Evapotranspiration Using Support Vector Machine Model and Hybrid Gamma Test

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