MONITORING LAND SUBSIDENCE IN EGYPT’S NORTHERN WEST COAST USING INTERFEROMETRIC SYNTHETIC APERTURE RADAR

The study aims to map variation in ground levels based on ordinary spirit levelling (SL) measurements. New machine learning techniques were developed and compared in the current study to estimate the leveling through SL measurements. The results show the developed LSTM model outperforms CNN, RNN, and BI-LSTM in modeling ground leveling in the training and testing stages. The accuracy of mapping ground levelling through the developed LSTM model is close to 99 % in terms of model error.