Machine Intelligence-Based Reference Evapotranspiration Modelling: An application of Neural Networks

Abstract

After inventing Artificial Neural Networks, a deep learning algorithm, simulation of hydrology and water resource-related problems become more efficient. The investigation aimed to discover an efficient Artificial Neural Networks (ANN) model for obtaining weekly reference evapotranspiration (ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ) in the Tirupati region. Air temperature (T), Sunshine hours (S), Wind speed (W) and Relative Humidity (RH) are among the climate variables commonly utilized to evaluate the ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> . Multiple and partial correlation analyses were performed between the ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> calculated by the Penman-Monteith (PM) method (PMET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ) and these variables by deleting one variable each time to determine the most impacting variable, RH, W, S, and T were found to be impacting variables in the order of lowest to highest. As a result, the most desirable ANN model (ANN ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ) was created using all the variables as inputs and eliminating one of the least influential variables each time to assess ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> . The ANN models are developed and validated using climatic data from 1992 to 2001. The model's ability was evaluated using numerical indicators and scatter & comparison plots by matching the PM ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> to the ANN ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> . The numerical indexes are employed to validate the usefulness of the generated models. The ANN (1-5-1) considering one input variable (T), ANN (2-5-1) considering two input variables (T & S), ANN (3-4-1) considering three input variables (T, S, & W), and ANN (4-3- 1) considering four input variables (T, S, W, & RH), were found to have 83.53%, 89.85%, 94.21%, and 99.30% efficiency during the validation, respectively. Therefore, the ANN models may accurately predict the weekly ET <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> in the research area and elsewhere in climatological situations similar to the study area.