Prediction of reference evapotranspiration for irrigation scheduling using machine learning

Nagappan, Manikumari; Gopalakrishnan, Vinodhini; Alagappan, Murugappan

doi:10.1080/02626667.2020.1830996

Cited by 27 publications

(12 citation statements)

References 37 publications

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“…Özgür and Yamaç ( 2020 ) subsequently improved this model by using the SeLU activation function in their DNN architecture, yielding the best performance metrics of RMSE = 0.2073 mm/day and R 2 = 0.9934 at the Aksaray weather station, Turkey. Recently, Nagappan et al ( 2020 ) developed a one-dimensional CNN ETo estimation model with only three input parameters extracted from PCA with R 2 = 0.979 and RMSE = 0.21 mm/day as the performance metrics. These values fall within the performance ranges of the previously mentioned full input parameter investigations (R 2 = 0.95–0.99 and RMSE = 0.19–0.27 mm/day).…”

Section: Discussionmentioning

confidence: 99%

“…Based on the findings obtained from ANFIS, multilinear regression (MLR) and SVM, Üneş et al ( 2020 ) selected temperature and solar radiation as the inputs for ETo modelling. Certain related researches have attempted genetic algorithm (Jovic et al 2018 ), PCA (Nagappan et al 2020 ), maximum information coefficient techniques (Chen et al 2020a ), subset regression analysis (Afzaal et al 2020 ), and gamma test (Patil and Deka 2017 ) to analyze the effect of input parameters on ETo. These investigations were conducted in various climatic zones and most of them have chosen solar radiation as one of the most influential parameters on ETo.…”

Section: Introductionmentioning

confidence: 99%

“…Among these, certain recent studies have implemented the AI techniques such as artificial neural networks (ANN) and their derivatives (Ferreira et al 2019;Reis et al 2019;Ferreira and da Cunha 2020a;Sowmya et al 2020;Bellido-Jiménez et al 2021;Kaya et al 2021), adaptive neuro fuzzy inference systems (ANFIS) (Petković et al 2020;Üneş et al 2020), support vector machine (SVM) and their hybrid variants (Chia et al 2020a;Tikhamarine et al 2020;Ahmadi et al 2021), tree based soft computing methods (Fan et al 2018;Wu et al 2020), gene expression programming (GEP) (Kazemi et al 2021;Muhammad et al 2021), hybrid ML models (Mohammadi and Mehdizadeh 2020;Zhu et al 2020;Kisi et al 2021;Gong et al 2021), and ensemble of ML models (Wu et al 2021;Martín et al 2021) for the ETo modelling. Most of these studies have reported success by experimenting with different input parameters for modelling, such as only temperature (Bellido-Jiménez et al 2021), a combination of temperature and solar radiation (Chia et al 2020a), temperature and relative humidity (Ferreira and da Cunha 2020a), temperature and wind speed (Nagappan et al 2020) or a variety of other combinations to minimize meteorological data usage. Furthermore, when temperature and radiation data were included to the input set, these investigations demonstrated excellence and superiority compared to the equivalent traditional empirical techniques (Chia et al 2020a;Üneş et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Özgür and Yamaç (2020) tested the power of DNN in ETo estimation by using SeLU activation function. A reduced feature one-dimensional convolution neural network (CNN) model for ETo prediction was developed by Nagappan et al (2020) and succeeded with promising results. Sowmya et al (2020) developed various DNN variants for ETo prediction using different feature combinations, and analyzed their predictive performances.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

2021

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Hydro-agrological research considers the reference evapotranspiration (ETo), driven by meteorological variables, crucial for achieving precise irrigation in precision agriculture. ETo modelling based on a single meteorological parameter would be beneficial in places where the collection of climatic parameters is challenging. The aim of this research is to develop a deep neural network (DNN) architecture that predicts daily ETo with a single input parameter selected based on the feature importance (FI) score generated by the machine learning techniques, random forest (RF), and extreme gradient boosting (XGBoost). This study also investigated the potential of SHapley Additive exPlanations to interpret and validate the outcomes of the feature selection methods by assessing the contributions of each feature to the ETo prediction. These methods recommended solar radiation as a significant parameter in the datasets of three California Irrigation Management System (CIMIS) weather stations located in distinct ETo zones. Three ETo models (DNN-Ret, XGB-Ret, and RF-Ret) were built using solar radiation as the sole input, and CIMIS ETo as the output. The performance evaluation of the developed models proved that DNN-Ret outperformed XGB-Ret and RF-Ret regardless of the dataset, with coefficients of determination (R 2 ) ranging from 0.914 to 0.954 in the local scenario, with an average decrease of 8–9.5% in mean absolute error and root mean squared error, and an improvement of 2.6–2.9% in Nash–Sutcliffe efficiency and 1.7–2% increase in R 2 . The overall result analysis highlighted the efficiency of DNN-Ret in the single input parameter based ETo modelling in diverse climatic zones.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

2021

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“…They claimed that the PSO-ELM model offered the best accuracy among other applied models such as ANN and random forest (RF) models. Nagappan et al (2020) attempted to predict ET0 for irrigation scheduling using machine learning methods like deep learning neural network (DLNN) and RBNN. It was found that the DLNN model acted better in the prediction process.…”

Section: Introductionmentioning

confidence: 99%

Modelling Reference Evapotranspiration using a Novel Regression-based Method: Radial basis M5 Model Tree

Kişi

Keshtegar

Zounemat‐Kermani

et al. 2021

Preprint

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In the current study, an ability of a novel regression-based method is evaluated in modelling daily reference evapotranspiration (ET0), which is an important issue in water resources management plans and helps farmers in irrigation planning. The method was developed by hybridizing radial basis function and M5 model tree and called as radial basis M5 model tree (RM5Tree). The radial-based kernel function was used to control the input variables in modelling process of M5 model tree. The new model results were compared with traditional M5 model tree (M5Tree), response surface method (RSM) and two neural networks (multi-layer perceptron neural networks, MLPNN & radial basis function neural network, RBFNN) with respect to several statistical indices. Daily climatic data (relative humidity, RH, solar radiation, SR, wind speed, air temperature, T) recorded at three stations in Turkey, Mediterranean Region, were used. The effect of each weather data on ET0 was also investigated by utilizing three different input scenarios with various combinations of input variables. On the whole, the RM5Tree provided the best results (NSE > 0.997) followed by the MLPNN (NSE > 0.990), and M5Tree (NSE > 0.945) in modelling daily ET0. The SR was observed as the most effective input parameter on ET0 which was followed by the T and RH. However, the findings of the third modelling scenario revealed that taking into account of all variables would considerably increase models’ accuracies for the three stations.

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Reference evapotranspiration prediction using machine learning models: An empirical study from minimal climate data

Shaloo,

Kumar,

Bisht

et al. 2023

Agronomy Journal

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The scarcity of climatic data is the biggest challenge for developing countries and the development of models for reference evapotranspiration (ET0) estimation with limited datasets is crucial. Therefore, the current investigation assessed the efficacy of four machine learning (ML) models viz., Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Neural Networks (NN) to predict ET0 based on minimal climate data in comparison with the standard FAO‐56 Penman‐Monteith (PM) method. The data on daily climate parameters were collected for the period 2000−2021, including maximum and minimum temperature (Tmax, Tmin), mean relative humidity (RH), wind speed (WS) and sunshine hours (SSH). The performance of the developed models considering different input combinations was evaluated by using several statistical performance measures. The results showed that the SVM model performed better than the other ML models during training (R2 = 0.985; mean absolute error [MAE] = 0.170 mm/d; mean square error [MSE] = 0.052 mm/d; root mean square error [RMSE] = 0.229 mm/d; mean absolute percentage error [MAPE] = 5.72%,) and testing stages (R2 = 0.985; MAE = 0.168 mm/d; MSE = 0.050 mm/d; RMSE = 0.224 mm/d; MAPE = 5.91%) under full dataset scenario. The best performance of the models to estimate was with Tmax, RH. Ws, SSH and Tmin. The results of the current study are substantial as it offers an approach to estimate ET0 in semi‐arid data scarce region.This article is protected by copyright. All rights reserved

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Prediction of reference evapotranspiration for irrigation scheduling using machine learning

Cited by 27 publications

References 37 publications

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter

Modelling Reference Evapotranspiration using a Novel Regression-based Method: Radial basis M5 Model Tree

Reference evapotranspiration prediction using machine learning models: An empirical study from minimal climate data

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