DEEP NEURAL NETWORK-BASED DATA RECONSTRUCTION FOR LANDSLIDE DETECTION

Landslides could cause huge threats to lives and cause property damages. In the landslide prediction system, environmental information can be collected through sensors to detect the possibility of landslide occurrences. However, the data collected may be lost due to sensor failures, external interferences or other environmental factors, which may affect the accuracy of landslide predictions. In order to solve the problem of missing data, we propose a data reconstruction method based on rainfall intensity and soil moisture, which reconstructs missing data based on temporal relationships. It is based on the data trend in the past period of time. A Long Short-Term Memory (LSTM) deep neural network is trained to predict the data value in missing time slots. We use the predicted data to compensate for the missing data so as the elevate the accuracy not only of data, but also landslide predictions. Our method is compared with other reconstruction methods. The proposed LSTM model exhibit a smaller RMSE than the Linear Extrapolation (LE) method. Even if 90% of random data is lost, the RMSE results for the data reconstruction by LE and LSTM are, respectively, 0.033 and 0.036 for rainfall data and 0.029 and 0.032 for soil moisture data.