Reference evapotranspiration prediction using high-order response surface method

Keshtegar, Behrooz; Abdullah, Shafika Sultan; Huang, Yuk Feng; Saggi, Mandeep Kaur; Khedher, Khaled Mohamed; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1007/s00704-022-03954-4

Cited by 9 publications

(4 citation statements)

References 48 publications

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“…R n = R ns − R nl (9) Radiation that falls on earth surface R ns is the fraction of solar radiation R s that is not reflected by the earth surface expressed by Eq. 10.…”

Section: Sensors Used In the Prototypementioning

confidence: 99%

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Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT

Bashir

Rehman

et al. 2022

IEEE Access

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Accurate estimation of Reference Evapotranspiration (ET 0 ) is important for efficient management and conservation of irrigation water. Existing methods of ET 0 rate determination are complex for application at the farmer level. Apart from standard methods of ET 0 determination, many data-driven soft computing approaches were also proposed to determine the ET 0 with limited data set. We proposed a temperature and humidity-based ML approach for ET 0 rate determination on directly sensed environmental conditions of the crop field. Crop field environmental conditions for (ET 0 ) rate determination are sensed by the proposed Internet of Things (IoT) architecture. Crop field environmental conditions from the year 2015 to 2021 in Pakistan are used for the training and testing of the proposed model. Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Artificial Neural Network (ANN) based models are compared for performance. Crop fields directly sensed temperature and humidity pass to the model to train and predict the ET 0 -rate of crop fields. The 10-fold cross-validation technique is applied for the evaluation of the proposed approach. The accuracy of the proposed solution for the ET 0 rate is compared against the Food and Agriculture Organization (FAO) recommended Penman-Monteith method for ET 0 rate determination. As concerned of the ML-based models the KNN model is more accurate as compared to SVM,GNB and ANN models with 92% accuracy. The KNN model of ET 0 is more efficient in reducing the Root Mean Squared Errors (RMSE) by 16% and Mean Absolute Errors (MAE) by 3% against the state of the art approach.INDEX TERMS Internet of Things (IoT), Reference Evapotranspiration (ET 0 ), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), K-Nearest Neighbour (KNN), Artificial Neural Network (ANN), Penman-Monteith

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“…R n = R ns − R nl (9) Radiation that falls on earth surface R ns is the fraction of solar radiation R s that is not reflected by the earth surface expressed by Eq. 10.…”

Section: Sensors Used In the Prototypementioning

confidence: 99%

“…ET 0 adjusts the amount of irrigation water according to the meteorological conditions. ET 0 is the most important issue in water resource management and planning [9]. Irrigation water schedule according to the ET 0 rate supports the conservation of irrigation water while maintaining yield [10].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT

Bashir

Rehman

et al. 2022

IEEE Access

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“…Keshtegar et al [21] used high-order response surface method to compute ET o . They included daily weather information which include the maximum temperature, maximum humidity, wind speed, solar radiation, and vapor pressure deficit, obtained from three observation stations in Burkina Faso, West Africa.…”

Section: Reference Evapotranspiration Problematic and Known Solutionsmentioning

confidence: 99%

Hybrid Neural Network Based Models for Evapotranspiration Prediction Over Limited Weather Parameters

et al. 2023

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Evapotranspiration can be used to estimate the amount of water required by agriculture projects and green spaces, playing a key role in water management policies that combat the hydrological drought, which assumes a structural character in many countries. In this context, this work presents a study on reference evapotranspiration (ET o ) estimation models, having as input limited meteorological parameters, namely: temperature, humidity, and wind. Since solar radiation (SR) is an important parameter in the determination of ET o , SR estimation models are also developed. These ET o and SR estimation models compare the use of Artificial Neural Networks (ANN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and hybrid neural network models such as LSTM-ANN, RNN-ANN and GRU-ANN. Two main approaches were taken for ET o estimation: (i) directly use those algorithms to estimate ET o , and (ii) estimate solar radiation first and then use that estimation together with other meteorological parameters in a method that predicts ET o . For the later case, two variants were implemented: the use of the estimated solar radiation as (ii.1) a feature of the neural network regressors, and (ii.2) the use of FAO-56PM method to compute ET o , which has solar radiation as one of the input parameters. Using experimental data collected from a weather station (WS) located in Vale do Lobo (Portugal), the later approach achieved the best result with a coefficient of determination (R 2 ) of 0.977. The developed model were then applied to data from eleven stations located in Colorado (USA), with very distinct climatic conditions, showing similar (R 2 > 0.95) results to the ones for which the models were initially designed, proving a good generalization. As a final notice, the reduced-set features were carefully selected so that they are compatible with free online weather forecast services.

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“…Keshtegar et al [44] propose regression modeling for daily ET for efficient irrigation water scheduling. The authors propose a hybrid approach for radial-based function and the M5 model tree.…”

Section: Literature Reviewmentioning

confidence: 99%

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

et al. 2022

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Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, especially for saline soils. Moreover, existing studies focus on the use of the Internet of Things (IoT) and machine learning-enabled smart and precision irrigation water recommendation systems along with the ET estimation by limited parameters. The ETs for saline soils are also equally important for the reclamation of saline soils, which is ignored by the existing literature. The study proposed IoT and machine leaching-based architecture of context-aware monthly ETs estimations for saline soil reclamation with the effective leaching process. The IoT-enabled crop field contexts in terms of crop field temperature, soil salinity, and irrigation water salinity are used as input features to the Long Short-Term Memory (LSTM) and ensembled LSTM models for monthly ETs predictions. The performance of the proposed solution is observed in terms of the accuracy of the machine learning models along with the comparison against the FAO-56 PM-based standard method. The implementation of the proposed solution reveals that the ensembled LSTM-based approach for ETs is more accurate as compared to the LSTM model with accuracies of 92 and 90% for the training and validation datasets, respectively. The predictions made by the ensembled LSTM are more in line with the FAO-56 PM-based method with a Pearson correlation of 0.916 as compared to LSTM models. The implementation of the proposed solution in real-time environments reveals that the proposed solution is more effective in reducing the soil salinity as compared to the traditional method.

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Reference evapotranspiration prediction using high-order response surface method

Cited by 9 publications

References 48 publications

Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT

Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT

Hybrid Neural Network Based Models for Evapotranspiration Prediction Over Limited Weather Parameters

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

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Reference evapotranspiration prediction using high-order response surface method

Cited by 9 publications

References 48 publications

Machine Learning Based Prediction of Reference Evapotranspiration (ET0) Using IoT

Machine Learning Based Prediction of Reference Evapotranspiration (ET0) Using IoT

Hybrid Neural Network Based Models for Evapotranspiration Prediction Over Limited Weather Parameters

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

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Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT

Machine Learning Based Prediction of Reference Evapotranspiration (ET₀) Using IoT