Daily evapotranspiration prediction using gradient boost regression model for irrigation planning

Appropriate irrigation planning with reference evapotranspiration (ET_o) plays an important role in managing the water resources for agriculture. This paper presents machine learning models for predicting ET_o and thereby aiding in irrigation planning. Daily weather data which include minimum and maximum temperature, relative humidity, solar radiation, soil temperature and wind speed were used to forecast ET_o. The datasets were trained, validated and tested using multiple linear regression, random forest and gradient boost regression (GBR) algorithms. The results of the machine learning algorithms with and without the proposed preprocess techniques were compared. Performance evaluations of these models were done by comparing the root mean square error, mean absolute error and coefficient of determination. It was found that the preprocessed GBR model edges past the other two models in predicting the ET_o. The paper also discusses the mechanism of irrigation planning with the help of ET_o, crop evapotranspiration (ET_c) and irrigation system efficiency.