APPLICABILITY OF RECURRENT NEURAL NETWORK TO PREDICT FIELD MEASUREMENT DATA OF VOLUMETRIC WATER CONTENT

Abstract

It is important to identify the model that can simulate the field measurement data of soil moisture conditions to predict the occurrence of landslide disasters due to heavy rain. This study verified the applicability of the recurrent neural network to predict the field measurement data of volumetric water content. The recurrent neural network model was estimated by using the training data at the time of weak rain, and the estimated model simulated the test data at the time of heavy rain with enough accuracy. The simulation results led to the conclusion that the recurrent neural network was an effective method to predict the field measurement data of volumetric water content.