Modeling of Evapotranspiration (ETo) in a Medium Urban Park within a Megacity by Using Artificial Neural Network (ANN) Model

Abstract

Evapotranspiration (ETo) is considered a main component of the hydrological cycle. This study was carried out on a medium-size park within a highly urbanized area, close to the center of Melbourne city. The purpose of the study is to calculate the reference evapotranspiration (ETo), particularly at a specified spot in a corner of the park. The hand-held device used to collect data gave consistent results and reduced the need for assumptions. The Penman-Montieth equation was used to calculate the reserved ETo. To build an ETo model, Artificial Neural Network (ANN) was adopted to predict ETo. Three models were built to select the best model, based on the least Root Mean Square Error (RMSE) and the highest coefficient of determination (R2). Results showed a contrast between the observed and predicted magnitudes of ETo. Both of the observed and predicted magnitudes for ETo are higher than most recent studies. Data from the specified location shows a difference in ETo magnitudes relative to the fixed meteorological stations. This study supports that climate change causes increasing magnitudes of reference evapotranspiration ETo.