CNN-LSTM-ATTENTION DEEP LEARNING MODEL FOR MAPPING LANDSLIDE SUSCEPTIBILITY IN KERALA, INDIA

Abstract

Abstract. As a typical type of natural disaster, landslides may result in injuries to humans, threats to property security, and economic loss. As such, it is important to understand or predict the probability of landslide occurrence at potentially risky sites. Using typically machine learning (ML) to estimate landslide susceptibility based on a landslide inventory and a set of factors that impact the occurrence of landslides is a common practice. However, in landslide susceptibility assessment, existing DL-based neural network methods use a fully connected layer to optimize the selection of factors, which limits their efficiency in extracting features of those contributing factors. In response to those problems, this study proposed a CNN-LSTM model with an attention mechanism (AM) to avoid the complex optimization of input factors while the same or even better prediction accuracy can be achieved. To compare our method with the existing ones, the historical landslide inventory and the remote sensing data of Kerala, India were used to produce the input variables needed in the methods considered. The results show that our method produced more accurate results, compared to those existing neural network methods (e.g. CNN, LSTM and CNN-LSTM).