Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method

Abstract

Reference crop evapotranspiration (ETo) is an important component of the hydrological cycle that is used for water resource planning, irrigation, and agricultural management, as well as in other hydrological processes. The aim of this study was to estimate the ETo based on limited meteorological data using an artificial neural network (ANN) method. The daily data of minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean), solar radiation (SR), humidity (H), wind speed (WS), sunshine hours (Ssh), maximum global radiation (gradmax), minimum global radiation (gradmin), day length, and ETo data were obtained over the long-term period from 1969 to 2019. The analysed data were divided into two parts from 1969 to 2007 and from 2008 to 2019 for model training and testing, respectively. The optimal ANN for forecasting ETo included Tmax, Tmin, H, and SR at hidden layers (4, 3); gradmin, SR, and WS at (6, 4); SR, day length, Ssh, and Tmean at (3, 2); all collected parameters at hidden layer (5, 4). The results showed different alternative methods for estimation of ETo in case of a lack of climate data with high performance. Models using ANN can help promote the decision-making for water managers, designers, and development planners.

Artificial Neural Networks for the Prediction of the Reference Evapotranspiration of the Peloponnese Peninsula, Greece

“…In that study, August and December had been selected as typical months of summer and winter, respectively. FAO PM has been widely used as a reference method to estimate ETo in studies investigating ANNs, either as the only reference method [10,70,81] or combined with direct methods [82]. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) ANN models were examined in this study.…”

Selection of Optimal Palmer Predictors for Increasing the Predictability of the Danube Discharge: New Findings Based on Information Theory and Partial Wavelet Coherence Analysis

“…Predictors can be obtained from meteorological and hydrological data through simple extrapolations if they have linear structures, or using an artificial neural network method, as shown in the work of Elbeltagi et al, 2022 [ 92 ], for nonlinear structures.…”

Monthly Stream Flows Estimation in the Karasu River of Euphrates Basin With Artificial Neural Networks Approach

“…are examples of work done in such areas. ANN is used to model the R-R relationship (Young and Liu, 2015;Vyas et al 2016;Kumar et al 2016;Dounia et al, 2016;Asadi et al 2019), to predict rainfall (Lee et al, 1998;Mirabbasi et al 2019), to predict river flow (Guimaraes Santos and Silva, 2014;Shi et al, 2016;Zemzami and Benaabidate, 2016;Wagena et al 2020;Adnan et al 2021), to predict reference evapotranspiration (Aytek, 2008;Qasem et al 2019;Tikhamarine et al 2019;Elbeltagi et al 2022), to predict discharge and waterlevel (Khan et al, 2016;Nacar et al 2018;Anilan et al 2020;Damla et al 2020;Temiz et al 2021), to predict snowmelt-runoff (Yilmaz, 2011), ANNs have also been regarded as a powerful tool for use in a variety of underground water problems (Malik et al 2021;Wunsch et al 2021). ANNs can be used for other purposes is unit hydrograph derivation (Lange, 1998), flood frequency analysis (Campolo, 2003;Dawson, et al, 2006;, drought analysis (Shin and Salas, 2000;Ochoa-Rivera, 2008;Banadkooki et al 2021;Ozan Evkaya and Sevinç Kurnaz, 2021), suspended sediment data estimation (Jimeno-Sáez, 2018;Khan et al 2019;Meshram et al 2020), Modelling the infiltration process Sihag et al 2021;Singh et al 2021), estimation of hydroelectric generation (Uzlu et al, 2014;…”