A Stacked Machine Learning Algorithm for Multi-Step Ahead Prediction of Soil Moisture

Abstract

A trustworthy assessment of soil moisture content plays a significant role in irrigation planning and in controlling various natural disasters such as floods, landslides, and droughts. Various machine learning models (MLMs) have been used to increase the accuracy of soil moisture content prediction. The present investigation aims to apply MLMs with novel structures for the estimation of daily volumetric soil water content, based on the stacking of the multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR). Two groups of input variables were considered: the first (Model A) consisted of various meteorological variables (i.e., daily precipitation, air temperature, humidity, and wind speed), and the second (Model B) included only daily precipitation. The stacked model (SM) had the best performance (R2 = 0.962) in the prediction of daily volumetric soil water content for both categories of input variables when compared with the MLP (R2 = 0.957), RF (R2 = 0.956) and SVR (R2 = 0.951) models. Overall, the SM, which, in general, allows the weaknesses of the individual basic algorithms to be overcome while still maintaining a limited number of parameters and short calculation times, can lead to more accurate predictions of soil water content than those provided by more commonly employed MLMs.