Modelling reference evapotranspiration using gene expression programming and artificial neural network at Pantnagar, India

Abstract

Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource planning. Its quantification is helpful in irrigation scheduling, water balance studies, water allocation, etc. Modelling of reference evapotranspiration (ET0) using both gene expression programming (GEP) and artificial neural network (ANN) techniques was done using the daily meteorological data of the Pantnagar region, India, from 2010 to 2019. A total of 15 combinations of inputs were used in developing the ET0 models. The model with the least number of inputs consisted of maximum and minimum air temperatures, whereas the model with the highest number of inputs consisted of maximum air temperature, minimum air temperature, mean relative humidity, number of sunshine hours, wind speed at 2 m height and extra-terrestrial radiation as inputs and with ET0 as the output for all the models. All the GEP models were developed for a single functional set and pre-defined genetic operator values, while the best structure in each ANN model was found based on the performance during the testing phase. It was found that ANN models were superior to GEP models for the estimation purpose. It was evident from the reduction in RMSE values ranging from 2 % to 56 % during training and testing phases in all the ANN models compared with GEP models. The ANN models showed an increase of about 0.96 % to 9.72 % of R2 value compared to the respective GEP models. The comparative study of these models with multiple linear regression (MLR) depicted that the ANN and GEP models were superior to MLR models.

Keywords

• Artificial Neural Networks
• Evolutionary algorithms
• Gene Expression Programming
• Machine Learning
• Regression Analysis
• Reference evapotranspiration models