Estimation of soil moisture using decision tree regression

Abstract

Soil moisture (SM) is a significant factor in the climate system. The accurate determination of SM has high importance in food production to satisfy the increasing demand for food and the chemical processes of soil. This paper applies decision tree regression to estimate SM considering different parameters including air temperature, time, relative humidity, and soil temperature. The presented method holds a mighty advantage to determine SM since the stimulant of the decision tree regression is an algorithm that generates a decision tree from given instances. Besides, usage of decision tree regression provides an opportunity to save time. Numerical results show that the presented method offers a high coefficient of determination value (R2), low mean squared error (MSE), and mean absolute error (MAE). The depth of the decision tree equals to five by providing higher fitness values than other depth levels. The best fitness values in the training stage are 0.00019, 0.007, and 0.842 for MSE, MAE, and R2, respectively. In conclusion of the paper, applied decision tree regression can handle the data of SM estimation in satisfying fitness criterion.