Evaluation of Data-driven Hybrid Machine Learning Algorithms for Modelling Daily Reference Evapotranspiration

Kushwaha, Nand Lal; Rajput, Jitendra; Sena, D.R.; Elbeltagi, Ahmed; Singh, Danny; Mani, Indra

doi:10.1080/07055900.2022.2087589

Cited by 25 publications

(12 citation statements)

References 51 publications

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“…In this paper, the evaluation metrics, including RMSE 22 , the normalized mean squared error (NMSE) 12 , the mean absolute error (MAE) 9 , 13 , 22 , the mean absolute percentage error (MAPE) 14 , 22 , and Nash–Sutcliffe coefficient of efficiency (NSCE) 12 , 13 were employed to assess the model performance. The definition of those evaluation indexes are as follows: where and denoted as the desired and actual outputs.…”

Section: Methodsmentioning

confidence: 99%

“…In general, the direct measurements method (e.g., Class A pan, Lysimeter group) is largely restricted due to the limitation of experimental conditions in dryland 14 – 16 , and the physically-based methods (e.g., Dalton model, FAO-56 Penman–Monteith method, etc.) have the drawbacks that the estimated results are very sensitive to the errors of parameters 17 , 18 , and the key meteorological factors(e.g., relative humidity, latent heat of evaporation, radiation) are sometimes difficult to be measured in the arid sand land 19 , 20 .…”

Section: Introductionmentioning

confidence: 99%

“…The aims of this study were (1) to provide a novel approach for monthly pan Ep estimation with limited meteorological variables; (2) to obtain more robust and precise estimating results by coupling LSTM with heuristic algorithms and data preprocessing technique; (3) to find the optimal and minimum meteorological parameters to be observed in the study area. Compared to previous studies 14 – 22 , 36 – 38 , the proposed models simultaneously account for data preprocessing and hyper-parameters optimization of deep learning models, and can be recommended as an effective method to estimate Ep with limited meteorological information in dryland.…”

Section: Introductionmentioning

confidence: 99%

“…As a main component of water balance, it plays an extremely important role in the global hydrological cycle [3][4][5] . Accurate estimation of evaporation by using is a significant issue in ecological management [6][7][8][9][10][11] , especially in arid sand land, where the stability and sustainability of the artificially re-vegetated belts depend on the effective utilization of the limited available water resources 12,13 .In general, the direct measurements method (e.g., Class A pan, Lysimeter group) is largely restricted due to the limitation of experimental conditions in dryland [14][15][16] , and the physically-based methods (e.g., Dalton model, FAO-56 Penman-Monteith method, etc.) have the drawbacks that the estimated results are very sensitive to the errors of parameters 17,18 , and the key meteorological factors(e.g., relative humidity, latent heat of evaporation,…”

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confidence: 99%

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Estimating the monthly pan evaporation with limited climatic data in dryland based on the extended long short-term memory model enhanced with meta-heuristic algorithms

2023

Sci Rep

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Accurate estimation of evaporation is of great significance for understanding regional drought, and managing and applying limited water resources in dryland. However, the application of the traditional estimation approaches is limited due to the lack of required meteorological parameters or experimental conditions. In this study, a novel hybrid model was proposed to estimate the monthly pan Ep in dryland by integrating long short-term memory (LSTM) with grey wolf optimizer (GWO) algorithm and Kendall-τ correlation coefficient, where the GWO algorithm was employed to find the optimal hyper-parameters of LSTM, and Kendall-τ correlation coefficient was used to determine the input combination of meteorological variables. The model performance was compared to the performance of other methods based on the evaluation metrics, including root mean squared error (RMSE), the normalized mean squared error (NMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE), and Nash–Sutcliffe coefficient of efficiency (NSCE). The results indicated that the optimal input meteorological parameters of the hybrid Kendall-τ-GWO-LSTM models are the monthly average temperature, the minimum air temperature, the maximum air temperature, the minimum values of RMSE, NMSE, MAE, and MAPE are 38.28, 0.20, 26.62, and 19.96%, and the maximum NSCE is 0.89, suggesting that the hybrid Kendall-τ-GWO-LSTM exhibit better model performance than the other hybrid models. Thus, the hybrid Kendall-τ-GWO-LSTM model was highly recommended for estimating pan Ep with limited meteorological information in dryland. The present investigation provides a novel method to estimate the monthly pan Ep with limited meteorological variables in dryland by coupling a deep learning model with meta-heuristic algorithms and the data preprocessing techniques.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Estimating the monthly pan evaporation with limited climatic data in dryland based on the extended long short-term memory model enhanced with meta-heuristic algorithms

2023

Sci Rep

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“…As such, many of empirical models have been configured for predicting evaporation rates from metrological variables like Thornthwaite equations, Priestley-Taylor and Penman-Monteith. However, the stochasticity features in addition to the nonlinearity and non-stationary of the meteorological variables employed in building a predictive model necessitate developing rigorous and reliable intelligent models that could be capable to eliminate the stochasticity inherited in the evaporation-meteorological variables relationship (Elbeltagi et al 2022;Kisi et al 2017a;Kushwaha et al 2022a;Salih et al 2020;Khan et al 2018;Naganna et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment

et al. 2022

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Exact estimation of evaporation rates is very important in a proper planning and efficient operation of water resources projects and agricultural activities. Evaporation is affected by many driving forces characterized by nonlinearity, non-stationary, and stochasticity. Such factors clearly hinder setting up rigorous predictive models. This study evaluates the predictability of coupling the additive regression model (AR) with four ensemble machine-learning algorithms—random Subspace (RSS), M5 pruned (M5P), reduced error pruning tree (REPTree), and bagging for estimating pan evaporation rates. Meteorological data encompass maximum temperature, minimum temperature, mean temperature, relative humidity, and wind speed from three different agroclimatic stations in Iraq (i.e., Baghdad, Mosul, and Basrah) were utilized as predictor parameters. The regression model in addition to the sensitivity analysis was employed to identify the best-input combinations for the evaluated methods. It was demonstrated that the AR-M5P estimated the evaporation with higher accuracy than others when combining wind speed, relative humidity, and the minimum and mean temperatures as input parameters. The AR-M5P model provided the best performance indicators, i.e., MAE = 33.82, RMSE = 45.05, RAE = 24.75, RRSE = 28.50, and r = 0.972 for Baghdad; MAE = 25.82, RMSE = 35.95, RAE = 23.75, RRSE = 29.64, and r = 0.956 for Mosul station, respectively. The outcomes of this study proved the superior performance of the hybridized methods in addressing such intricate hydrological relationships and hence could be employed for other environmental problems.

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An In‐Depth Review on Machine Learning Infusion in an Agricultural Production System

Dash,

Priyadarshini,

Priyadarshini

2024

How Machine Learning Is Innovating Today's World

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Evaluation of Data-driven Hybrid Machine Learning Algorithms for Modelling Daily Reference Evapotranspiration

Cited by 25 publications

References 51 publications

Estimating the monthly pan evaporation with limited climatic data in dryland based on the extended long short-term memory model enhanced with meta-heuristic algorithms

Estimating the monthly pan evaporation with limited climatic data in dryland based on the extended long short-term memory model enhanced with meta-heuristic algorithms

Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment

An In‐Depth Review on Machine Learning Infusion in an Agricultural Production System

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