Radar‐Based Deep Learning for Debris Flow Identification Amid the Environmental Disturbances

Abstract Microwave radar, utilizing the differences in Doppler frequencies from moving target echoes, offers remote sensing capabilities and continuous all‐weather monitoring for geological disasters. However, intelligent identification of debris flow signals using such radar remains unexplored. Therefore, we implemented 12 deep learning models coupled with a voting strategy to develop classification models for identifying the debris flow, using 24,000 samples across eight categories of targets obtained from field experiments. Each model demonstrated significant proficiency in classification, achieving a remarkable highest accuracy of 95.46% for the multi‐object classification. Among the individual models, the vgg16 model with a simple and deep architecture excelled in debris flow identification, exhibiting a high precision and a low false alarm rate. The voting strategy further improved the reliability of individual deep learning model. We propose that employing radar‐based deep learning techniques combined with extensive field data represents a crucial advancement in the monitoring and early warning of debris flow.