Application of Random Forest Regression with Hyper-parameters Tuning to Estimate Reference Evapotranspiration

Estimation of reference evapotranspiration (ETo) is a complex and non-linear problem that is used for the quantification of crop water requirements. In this study, random forest regression based models are developed to predict the ETo of Bhopal city, Madhya Pradesh, India. The meteorological data is collected from IMD, Pune for the periods of the years 2015-16. Based on the correlation among meteorological variables with observed ETo, four different random forest regression models are created. Moreover, the effects of three important hyper-parameters of random forest, such as the number of trees in the forest, depth of the tree, and the number of samples at a leaf node are evaluated to estimate ETo using the proposed models. These hyper-parameters are applied in three different ways to the models such as one hyper-parameter parameter at a time, and combination of hyper-parameters using grid search, and random search approaches. In this study, the result indicates that a random forest regression based model with maximal meteorological input variables exhibits great predictive power in small execution time than minimal input variables. This study also reveals that the model that optimises the hyper-parameters using a grid search approach shows equal predictive power but takes much execution time whereas random search based optimization exhibits the same level of predictive capability in less computation time. Stakeholders can utilize random forest regression models with sufficient meteorological data to estimate crop water requirements, and enhance the food production.

@article{Jain2022,
  title     = {Application of Random Forest Regression with Hyper-parameters Tuning to Estimate Reference Evapotranspiration},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Satendra Kumar Jain and Anil Kumar Gupta},
  doi       = {10.14569/IJACSA.2022.0130585},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130585}
}