Ensemble learning-based estimation of reference evapotranspiration (ETo)


Tausif M. Dilshad S. Umer Q. Iqbal M.W. Latif Z. Lee C. Bashir R.N.
December 2023Elsevier B.V.

Internet of Things (Netherlands)
2023#24

Reference Evapotranspiration (ETo) is crucial and influential in irrigation water management. Precise ETo rate estimation is vital for successful agriculture water management. There are numerous techniques for ETo rate simulation, but machine learning (ML) and deep learning (DL) approaches are currently popular. This study proposes an ensemble learning-based model for ETo rate estimation. The proposed model leverages minimum meteorological parameters, i.e., minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (RH), and mean wind speed (WS) as input features. The proposed model employs Random Forest Bagging and Gradient Boosting models as bagging and boosting ensemble techniques for the accurate ETo rate estimation. The 10-fold cross-validation method is leveraged for the evaluation of the proposed model. The performance results of the proposed model are compared with the baseline model of ETo estimation, i.e., the Food and Agriculture Organization Penman–Monteith (FAO-56 PM) and off-the-shelf deep learning models. The performance results indicate that Random Forest Bagging is significant as it yields Gradient Boosting and baseline models with 93.15% f-measure and reduces Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) by 17% and 10%, respectively.

Bagging , Boosting , Convolutional neural network , Ensemble learning , IoT , Long short-term memory , Reference evapotranspiration

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Department of Computer Science, The Superior University, Lahore, 54000, Pakistan
Department of Computer Sciences, COMSATS University Islamabad, Vehari, 61000, Pakistan
Department of Computer Science, Hanyang University, Seoul, 04763, South Korea
Department of Computer Science, School of Engineering and Digital Sciences (SEDS), Nazarbayev University, Astana, 010000, Kazakhstan

Department of Computer Science
Department of Computer Sciences
Department of Computer Science
Department of Computer Science

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