Enhanced Landslide Detection Using Deep Learning with Recurrent Neural Networks for Temporal Monitoring

Landslides represent an important group of geohazards causing extensive damage to structures and posing a thoughtful risk to general sustainability. The appropriate and precise withstanding of landslide actions is essential for successfully reducing the related hazards. This review announces an unconventional landslide warning structure that leverages an urbane learning planning, specifically a Recurrent Neural Network (RNN). The system is established through training on ancient landslide datasets shared with real-time sensor information, which incorporates critical features such as soil moisture content, temperature, humidity, and ground vibrations. The RNN model expertly identifies temporal forms thus facilitating precise probability assessments about the incidence of landslide actions. The efficacy of the executed RNN approach is assessed using previously experimental datasets to confirm its predictive accuracy and general applicability. Assessing presentation integrates metrics with accuracy, recall, precision, and F1 score to measure its success. The outcomes of the assessment expose that the Recurrent Neural Network shows enhanced value in the identification of landslides, accomplishing an outstanding accuracy rate of 89%. The outcomes document a precision statistic of 82%, an F1-score at 87%, and a recall rate verified at 94%.