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Long Short-term Dynamic Graph Neural Networks: for short-term intense rainfall forecasting

Published: 06 March 2023 Publication History

Abstract

In practice, accurate and timely forecasting of short-term intense rainfall is critical, but the problem is extremely difficult because to its complicated spatial-temporal association. Although several spatial-temporal series forecasting methods have been used to rainfall prediction, these models continue to suffer from inadequate modeling of data’s complicated intrinsic connection. We provide a new short-term intense rainfall prediction model that use two graph generators to model data correlations under distinct semantics, followed by a graph convolution module for information integration to fully extract data spatial-temporal information. Finally, a variant of recurrent neural network is employed to extract the temporal dependence. The experimental results on both datasets show that the model can model the spatial and temporal dependence across the data more effectively than the baseline model, and further improve the model’s predictive performance for short-term intense rainfall.

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      MLNLP '22: Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing
      December 2022
      406 pages
      ISBN:9781450399067
      DOI:10.1145/3578741
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 06 March 2023

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      Author Tags

      1. Graph Convolution
      2. Short-term intense rainfall
      3. spatial-temporal correlation

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