Rainfall peak prediction using deep learning†
Seunghawk Kim1 · Gwangseob Kim2 · Kyeong Eun Lee3
13Department of Statistics, Kyungpook National University
2Department of Civil Engineering
2Department of Civil Engineering
Correspondence to: † This paper was conducted with the support of the Ministry of Public Administration and Security’s climate change response AI-based storm and flood risk prediction technology development project. (2022-MOIS61-002). This paper is a revised version of Seunghwak Kim’s Master’s Thesis.
1 Graduate student, Department of Statistics, Kyungpook National University, Daegu 41566, Korea.
2 Professor, Department of Civil Engineering, Kyungpook National University, Daegu 41566, Korea.
3 Associate professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: artlee@knu.ac.kr
1 Graduate student, Department of Statistics, Kyungpook National University, Daegu 41566, Korea.
2 Professor, Department of Civil Engineering, Kyungpook National University, Daegu 41566, Korea.
3 Associate professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: artlee@knu.ac.kr
Received June 26, 2023; Revised July 16, 2023; Accepted July 20, 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- Due to the regional impact of climate change, the frequency and intensity of heavy rainfall events in South Korea have been increasing, while urbanization has contributed to a rise in internal urban flooding damages. Despite similar rainfall amounts, the characteristics of flood damages vary depending on the changes in the temporal distribution of rainfall. Therefore, a precise prediction of urban flooding requires a thorough understanding and prediction of the temporal distribution characteristics of rainfall. For this purpose, we utilized 48 years of data (from 1974 to 2021) from the end-point meteorological observation data provided by the meteorological data portal. We selected 60 regions and employed convolutional neural networks, long short-term memory networks, ConvLSTM, and EfficientnetV2 models to predict the rainfall peak locations within a 6-hour period for 11 urban areas prone to significant urban flooding. The analysis revealed that EfficientnetV2 achieved the highest accuracy (84.58%).
- Keywords : Deep Learning, EfficientnetV2, Flooding, Rainfall Peak
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