An Improved LSTM Time-Series Approach for Forecasting Debris Flow Events

Abstract

Debris flows occur abruptly and possess strong destructive power, making them one of the most hazardous geomorphological processes capable of inflicting heavy losses in a short period of time. In view of the difficulty in obtaining timely and accurate predictions using existing warning techniques, this study introduces a novel time-series forecasting strategy for debris-flow hazards, built upon an enhanced LSTM model. Field surveys were first conducted to assemble the initial dataset, after which the data were carefully filtered and standardized. Principal Component Analysis was applied to reorganize and condense the numerous environmental and geological variables, allowing eight dominant factors related to debris-flow development to be extracted. Based on these refined inputs, an LSTM-driven prediction architecture was developed. The model was trained and validated using the prepared dataset, and its performance was benchmarked against several traditional forecasting methods. Experi­mental outcomes reveal that the proposed model maintains low prediction errors—below 0.06 for training, 0.12 for testing, and 0.19 for validation. These results highlight the method’s superior predictive capability and robustness compared with conventional approaches. The framework presented here has the potential to significantly enhance debris-flow risk assessment and offers practical support for emergency management and early-warning operations.