Complexity to Forecast Flood: Problem Definition and Spatiotemporal Attention LSTM Solution
Joint Authors
Zhu, Yuelong
Wang, Sifeng
Wu, Yirui
Ding, Yukai
Feng, Jun
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-26
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
With significant development of sensors and Internet of things, researchers nowadays can easily know what happens in physical space by acquiring time-varying values of various factors.
Essentially, growing data category and size greatly contribute to solve problems happened in physical space.
In this paper, we aim to solve a complex problem that affects both cities and villages, i.e., flood.
To reduce impacts induced by floods, hydrological factors acquired from physical space and data-driven models in cyber space have been adopted to accurately forecast floods.
Considering the significance of modeling attention capability among hydrology factors, we believe extraction of discriminative hydrology factors not only reflect natural rules in physical space, but also optimally model iterations of factors to forecast run-off values in cyber space.
Therefore, we propose a novel data-driven model named as STA-LSTM by integrating Long Short-Term Memory (LSTM) structure and spatiotemporal attention module, which is capable of forecasting floods for small- and medium-sized rivers.
The proposed spatiotemporal attention module firstly explores spatial relationship between input hydrological factors from different locations and run-off outputs, which assigns time-varying weights to various factors.
Afterwards, the proposed attention module allocates temporal-dependent weights to hidden output of each LSTM cell, which describes significance of state output for final forecasting results.
Taking Lech and Changhua river basins as cases of physical space, several groups of comparative experiments show that STA-LSTM is capable to optimize complexity of mathematically modeling floods in cyber space.
American Psychological Association (APA)
Wu, Yirui& Ding, Yukai& Zhu, Yuelong& Feng, Jun& Wang, Sifeng. 2020. Complexity to Forecast Flood: Problem Definition and Spatiotemporal Attention LSTM Solution. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1143889
Modern Language Association (MLA)
Wu, Yirui…[et al.]. Complexity to Forecast Flood: Problem Definition and Spatiotemporal Attention LSTM Solution. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1143889
American Medical Association (AMA)
Wu, Yirui& Ding, Yukai& Zhu, Yuelong& Feng, Jun& Wang, Sifeng. Complexity to Forecast Flood: Problem Definition and Spatiotemporal Attention LSTM Solution. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1143889
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143889