Development of a multi-modal fully guided attention gate (MM-FGAG) framework for spatio-temporal flood detection

Floods are one of the most frequent hydrometeorological disasters in Indonesia, causing severe social, economic, and environmental impacts. The object of this research is spatio-temporal flood detection in Simpang Empat, Asahan Regency, North Sumatra, an area that faces annual flooding due to high rainfall, low-lying topography, and land-use changes. Conventional detection approaches based on either spatial or temporal data often fail to capture complex interactions, thereby limiting predictive accuracy. To address this problem, this study developed a multi-modal fully guided attention gate (MM-FGAG) framework that integrates Sentinel-2 multispectral imagery, SRTM elevation, CHIRPS rainfall, and ERA5 atmospheric variables. The model employs CNN-based spatial priors to guide temporal attention in LSTM, ensuring that predictions focus on the most flood-relevant regions and time periods. Experimental results show that MM-FGAG achieved 91.72% accuracy, 92.05% precision, 90.29% recall, and an AUC of 0.945, significantly outperforming CNN, LSTM, and CNN-LSTM baselines. This improvement is explained by explicit spatial-to-temporal guidance, which enhances predictive accuracy while also increasing interpretability through attention maps. Distinctive features of the framework include multimodal integration, guided attention, and the ability to generate flood risk maps with more than 90% agreement with observed data. These findings confirm that MM-FGAG is robust, adaptive, and capable of producing accurate and explainable predictions. The framework shows strong potential for use in flood early warning systems and disaster risk management, providing timely information for evacuation planning and resource allocation in vulnerable regions.