ARTIFICIAL NEURAL NETWORK FOR PREDICTION OF LAND SUBSIDENCE IN MUDSLIDES REGION THROUGH INSAR AND RAIN DATA

Abstract

Abstract. Mudslides are powerful and fast-moving mass movements that pose significant risks to human lives, infrastructure, and natural environments. They are commonly triggered by intense rainfall and their impact is particularly severe in mountainous regions. Synthetic Aperture Radar (SAR) technology can be used to calculate the subsidence of the territory over time by means of a temporal series of SAR images through the Persistent Scatterer Interferometry (PSI) technique. In some research Interferometric SAR (InSAR PSI) data were used to train Long Short-Term Memory (LSTM) based Artificial Neural Network (ANN) to provide movements forecasting. This paper proposes a new LSTM based ANN to forecast future territory movements considering both the past InSAR PSI data, the rain forecasting of the next acquisition and the past cumulative amount of rain since the movements of mudslides are strictly dependent to the quantity of rainfall accumulated in the terrain. The results of the proposed ANN are shown in terms of Mean Square Error (MSE) and Mean Absolute error (MAE) by comparing them with a LSTM-based ANN trained with only the InSAR PSI data.