Modelling of daily reference evapotranspiration using deep neural network in different climates

TL;DR

This paper compares artificial neural network and deep neural network models for estimating daily reference evapotranspiration across different climates, demonstrating the superior performance of a proposed DNN model with SeLU activation.

Contribution

The study introduces a deep neural network model with SeLU activation for more accurate ET o estimation and compares it with existing models across various climates.

Findings

Proposed DNN with SeLU achieves R² of 0.9934 in Aksaray.

DNN models outperform previous ANN and DNN models.

Model is recommended for use in diverse climate zones.

Abstract

Precise and reliable estimation of reference evapotranspiration (ET o ) is an essential for the irrigation and water resources management. ET o is difficult to predict due to its complex processes. This complexity can be solved using machine learning methods. This study investigates the performance of artificial neural network (ANN) and deep neural network (DNN) models for estimating daily ET o . Previously proposed ANN and DNN methods have been realized, and their performances have been compared. Six input data including maximum air temperature (T max ), minimum air temperature (T min ), solar radiation (R n ), maximum relative humidity (RH max ), minimum relative humidity (RH min ) and wind speed (U 2 ) are used from 4 meteorological stations (Adana, Aksaray, Isparta and Ni\u{g}de) during 1999-2018 in Turkey. The results have shown that our proposed DNN models achieves satisfactory accuracy for daily ET o estimation compared to previous ANN and DNN models. The best performance has been observed with the proposed model of DNN with SeLU activation function (P-DNN-SeLU) in Aksaray with coefficient of determination (R 2 ) of 0.9934, root mean square error (RMSE) of 0.2073 and mean absolute error (MAE) of 0.1590, respectively. Therefore, the P-DNN-SeLU model could be recommended for estimation of ET o in other climate zones of the world.