METHOD FOR ESTIMATING REGRESSION MODEL BY MACHINE LEARNING TO REPRODUCE SOIL MOISTURE CONDITION DUE TO HEAVY RAIN

Abstract

Soil moisture condition inside the slope affects the occurrences of slope failures. The previous study revealed that the estimated regression model using a recurrent neural network was available to reproduce the field measurement data of volumetric water content at the time of heavy rain. However, the method of giving input data and the machine learning algorithms should be more discussed. This study discussed the method for estimating the regression model to reproduce the soil moisture condition by changing input data and machine learning algorithms. As a result, the estimated regression model by using antecedent precipi-tation indices as input data and RNN as the machine learning algorithm led to predict the variation of the volumetric water content with sufficient accuracy even on slopes with different soil types.