Abstract
Flooding is among the most destructive natural disasters in the world. The destruction that floods cause has led to an urgency in developing accurate prediction models. One aspect of flood prediction which has yet to benefit from machine learning techniques is in the prediction of flood extent. However, due to the many factors that can cause flooding, developing predictive models that can generalise to other potential flooding locations has proven to be a difficult task. This paper shows that a Multi-Input ConvLSTM can exploit several flood conditioning factors to effectively model flood extent while generalising well to other flood locations under certain conditions. Furthermore, this study compares the sub-components of the system to demonstrate their efficacy when applied to various flood types.
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Muckley, L., Garforth, J. (2021). Multi-Input ConvLSTM for Flood Extent Prediction. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12666. Springer, Cham. https://doi.org/10.1007/978-3-030-68780-9_8
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DOI: https://doi.org/10.1007/978-3-030-68780-9_8
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