Figure - available from: Stochastic Environmental Research and Risk Assessment
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The detail about Convolutional Block Attention Module. Panel A: The overview of the CBAM. Panel B: The channel sub-module utilizes both max poling outputs and average pooling outputs with a shared network to tell “what to pay attention to”. Panel C: The spatial sub-module utilizes same outputs that are pooled along and forward them to a convolutional layer to tell “where to pay attention to”
Source publication
Urban flood risk management has been a hot issue worldwide due to the increased frequency and severity of floods occurring in cities. In this paper, an innovative modelling approach based on the Bayesian convolutional neural network (BCNN) was proposed to simulate the urban flood inundation, and to provide a reliable prediction of specific water de...
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Floods pose significant risks worldwide, impacting lives, infrastructure, and economies. The Susurluk basin, covering 24,319 km² in Türkiye, is highly vulnerable to flooding. This study updates the flood management plan for the basin, integrating hydrological modeling, GIS-based flood mapping, and early warning system evaluations in alignment with...
