Flood-Prone Road Recognition: Enhancing Resilience Through Identification Analysis

Floods pose significant threats to public safety and infrastructure, making the timely detection of flood-prone areas crucial for effective disaster management. This research presents an approach for flood-prone road detection leveraging a hybrid approach of combining convolutional neural network (CNN) and long short-term memory (LSTM) architectures. The proposed model harnesses the feature extraction abilities of CNNs and the sequential learning ability of LSTMs to analyze satellite imagery and weather data. The dataset utilized for model training and evaluation includes annotated images indicating flood-prone roads. Extensive experiments demonstrate the efficacy of hybrid CNN-LSTM model in accurately identifying vulnerable areas prone to flooding. Visualizations of model predictions highlight its capability to discern subtle patterns indicative of flood susceptibility. The findings of this research contribute to the advancement of flood detection systems, offering a promising solution for preemptive identification of flood-prone roads and enhancing the preparedness and response capabilities of disaster management authorities.