Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer

Abstract

Deep-seated landslides have caused substantial damage to both human life and infrastructure in the past. Developing an early warning system for this type of disaster is crucial to reduce its impact on society. This research contributes to developing predictive early warning systems for deep-seated landslide displacement by employing advanced computational models for environmental risk management. The novel framework evaluates machine learning, time series deep learning, and convolutional neural networks (CNNs), identifying the most effective models to be enhanced by the Age of Exploration-Inspired Optimizer (AEIO) algorithm. Our approach demonstrates exceptional forecasting capabilities by utilizing 8 years of comprehensive data - including displacement, groundwater levels, and meteorological information from the Lushan (mountainous) region in Taiwan. The AEIO-MobileNet model precisely predicts imminent deep-seated landslide displacement with a mean absolute percentage error (MAPE) of 2.81 %. These advancements significantly enhance geohazard informatics by providing reliable and efficient tools for landslide risk assessment and management. They help safeguard road networks, construction projects, and infrastructure in vulnerable slope areas.