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Short-Term Prediction Model of Water Level Based on ATT-ConvLSTM

Authors:

Rui Liu,

Ying ChenAuthors Info & Claims

DSDE '22: Proceedings of the 2022 5th International Conference on Data Storage and Data Engineering

Pages 85 - 90

https://doi.org/10.1145/3528114.3528128

Published: 24 June 2022 Publication History

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Abstract

At present, under extreme weather conditions, flood events caused by river overflow occur frequently, which poses a great threat to people's life and property safety. In order to prevent the disaster caused by flood and take preventive measures in advance, this paper constructs a hybrid short-term water level prediction model ATT-ConvLSTM which combined with attention mechanism and ConvLSTM. The model uses the water level, flow, temperature and rainfall data in the historical hydrological data set of Arnoia River to predict the short-term change of water level. The experimental results show that the prediction model proposed in this paper achieves good results in Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and R-Squared (R2).

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Malik HFeng JShao PAbduljabbar Z(2024)Improving flood forecasting using time-distributed CNN-LSTM model: a time-distributed spatiotemporal methodEarth Science Informatics10.1007/s12145-024-01354-y17:4(3455-3474)Online publication date: 10-Jun-2024
https://doi.org/10.1007/s12145-024-01354-y
Girija RDeepa NGera V(2024)An Artificial Intelligence-Based Portable Rainwater Harvesting KitRecent Advances in Industrial and Systems Engineering10.1007/978-981-97-4700-9_39(415-424)Online publication date: 27-Aug-2024
https://doi.org/10.1007/978-981-97-4700-9_39

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DSDE '22: Proceedings of the 2022 5th International Conference on Data Storage and Data Engineering

February 2022

124 pages

ISBN:9781450395724

DOI:10.1145/3528114

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 ACM 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: 24 June 2022

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DSDE 2022: 2022 the 5th International Conference on Data Storage and Data Engineering

February 25 - 27, 2022

Sanya, China

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Malik HFeng JShao PAbduljabbar Z(2024)Improving flood forecasting using time-distributed CNN-LSTM model: a time-distributed spatiotemporal methodEarth Science Informatics10.1007/s12145-024-01354-y17:4(3455-3474)Online publication date: 10-Jun-2024
https://doi.org/10.1007/s12145-024-01354-y
Girija RDeepa NGera V(2024)An Artificial Intelligence-Based Portable Rainwater Harvesting KitRecent Advances in Industrial and Systems Engineering10.1007/978-981-97-4700-9_39(415-424)Online publication date: 27-Aug-2024
https://doi.org/10.1007/978-981-97-4700-9_39

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