Global Flood Prediction: a Multimodal Machine Learning Approach

• URL: http://arxiv.org/abs/2301.12548v1
• Date: Sun, 29 Jan 2023 21:39:39 GMT
• Title: Global Flood Prediction: a Multimodal Machine Learning Approach
• Authors: Cynthia Zeng, Dimitris Bertsimas
• Abstract summary: This work presents a novel multimodal machine learning approach for multi-year global flood risk prediction. Our framework employs state-of-the-art processing techniques to extract embeddings from each data modality. Experiments demonstrate that a multimodal approach, that is combining text and statistical data, outperforms a single-modality approach.
• Score: 3.7565501074323224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Flooding is one of the most destructive and costly natural disasters, and climate changes would further increase risks globally. This work presents a novel multimodal machine learning approach for multi-year global flood risk prediction, combining geographical information and historical natural disaster dataset. Our multimodal framework employs state-of-the-art processing techniques to extract embeddings from each data modality, including text-based geographical data and tabular-based time-series data. Experiments demonstrate that a multimodal approach, that is combining text and statistical data, outperforms a single-modality approach. Our most advanced architecture, employing embeddings extracted using transfer learning upon DistilBert model, achieves 75\%-77\% ROCAUC score in predicting the next 1-5 year flooding event in historically flooded locations. This work demonstrates the potentials of using machine learning for long-term planning in natural disaster management.

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