An Explainable Deep Neural Network with Frequency-Aware Channel and Spatial Refinement for Flood Prediction in Sustainable Cities
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| Format: | Preprint |
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2025
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| _version_ | 1866912579496968192 |
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| author | Dar, Shahid Shafi Kaurav, Bharat Jain, Arnav Raghaw, Chandravardhan Singh Rehman, Mohammad Zia Ur Kumar, Nagendra |
| author_facet | Dar, Shahid Shafi Kaurav, Bharat Jain, Arnav Raghaw, Chandravardhan Singh Rehman, Mohammad Zia Ur Kumar, Nagendra |
| contents | In an era of escalating climate change, urban flooding has emerged as a critical challenge for sustainable cities, threatening lives, infrastructure, and ecosystems. Traditional flood detection methods are constrained by their reliance on unimodal data and static rule-based systems, which fail to capture the dynamic, non-linear relationships inherent in flood events. Furthermore, existing attention mechanisms and ensemble learning approaches exhibit limitations in hierarchical refinement, cross-modal feature integration, and adaptability to noisy or unstructured environments, resulting in suboptimal flood classification performance. To address these challenges, we present XFloodNet, a novel framework that redefines urban flood classification through advanced deep-learning techniques. XFloodNet integrates three novel components: (1) a Hierarchical Cross-Modal Gated Attention mechanism that dynamically aligns visual and textual features, enabling precise multi-granularity interactions and resolving contextual ambiguities; (2) a Heterogeneous Convolutional Adaptive Multi-Scale Attention module, which leverages frequency-enhanced channel attention and frequency-modulated spatial attention to extract and prioritize discriminative flood-related features across spectral and spatial domains; and (3) a Cascading Convolutional Transformer Feature Refinement technique that harmonizes hierarchical features through adaptive scaling and cascading operations, ensuring robust and noise-resistant flood detection. We evaluate our proposed method on three benchmark datasets, such as Chennai Floods, Rhine18 Floods, and Harz17 Floods, XFloodNet achieves state-of-the-art F1-scores of 93.33%, 82.24%, and 88.60%, respectively, surpassing existing methods by significant margins. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_08003 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Explainable Deep Neural Network with Frequency-Aware Channel and Spatial Refinement for Flood Prediction in Sustainable Cities Dar, Shahid Shafi Kaurav, Bharat Jain, Arnav Raghaw, Chandravardhan Singh Rehman, Mohammad Zia Ur Kumar, Nagendra Computer Vision and Pattern Recognition In an era of escalating climate change, urban flooding has emerged as a critical challenge for sustainable cities, threatening lives, infrastructure, and ecosystems. Traditional flood detection methods are constrained by their reliance on unimodal data and static rule-based systems, which fail to capture the dynamic, non-linear relationships inherent in flood events. Furthermore, existing attention mechanisms and ensemble learning approaches exhibit limitations in hierarchical refinement, cross-modal feature integration, and adaptability to noisy or unstructured environments, resulting in suboptimal flood classification performance. To address these challenges, we present XFloodNet, a novel framework that redefines urban flood classification through advanced deep-learning techniques. XFloodNet integrates three novel components: (1) a Hierarchical Cross-Modal Gated Attention mechanism that dynamically aligns visual and textual features, enabling precise multi-granularity interactions and resolving contextual ambiguities; (2) a Heterogeneous Convolutional Adaptive Multi-Scale Attention module, which leverages frequency-enhanced channel attention and frequency-modulated spatial attention to extract and prioritize discriminative flood-related features across spectral and spatial domains; and (3) a Cascading Convolutional Transformer Feature Refinement technique that harmonizes hierarchical features through adaptive scaling and cascading operations, ensuring robust and noise-resistant flood detection. We evaluate our proposed method on three benchmark datasets, such as Chennai Floods, Rhine18 Floods, and Harz17 Floods, XFloodNet achieves state-of-the-art F1-scores of 93.33%, 82.24%, and 88.60%, respectively, surpassing existing methods by significant margins. |
| title | An Explainable Deep Neural Network with Frequency-Aware Channel and Spatial Refinement for Flood Prediction in Sustainable Cities |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.08003 |