Efficient prediction of evaporation using ensemble feature selection techniques

For the timely planning and management of water resources, evaporation prediction must be estimated properly, especially in regions that are prone to drought and where evaporation directly affects the pest population. Changes in meteorological variables such as temperature, relative humidity, solar radiation, rainfall have a great impact on the evaporation process. In order to forecast the variable, ensemble feature selection techniques along with various machine learning techniques were investigated. Meteorological weekly weather data were collected from the ICRISAT location over a period from 1974 to 2021. The reliability of these developed models was based on statistical approaches namely Mean Absolute Error, Root Mean Square Error, Coefficient of Determination, Nash–Sutcliffe Efficiency coefficient, and Willmott’s Index of agreement along with several graphical aids. The results indicate that lasso regression outperforms all other machine learning approaches and the results are validated using current data (2020-2021). For a better understanding of the results, these validated results were also compared with results obtained from the established linear regression method and artificial neural network. It was further found that lasso regression shows an improved performance (R2 = 0.929) over linear regression (R2 = 0.871) and artificial neural network (R2 = 0.889).