Using Convolutional Neural Networks to Build a Lightweight Flood Height Prediction Model with Grad-Cam for the Selection of Key Grid Cells in Radar Echo Maps

Chen, Yi-Chung; Chang, Tzu-Yin; Chow, Heng-Yi; Li, Siang-Lan; Ou, Chin-Yu

doi:10.3390/w14020155

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Using Convolutional Neural Networks to Build a Lightweight Flood Height Prediction Model with Grad-Cam for the Selection of Key Grid Cells in Radar Echo Maps

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Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Douliu City 640301, Taiwan

National Science and Technology Center for Disaster Reduction, New Taipei City 23143, Taiwan

Author to whom correspondence should be addressed.

Academic Editor: Renato Morbidelli

Water 2022, 14(2), 155; https://doi.org/10.3390/w14020155

Received: 3 December 2021 / Revised: 31 December 2021 / Accepted: 5 January 2022 / Published: 7 January 2022

(This article belongs to the Special Issue Mitigation Techniques for Water-Induced Natural Disasters: The State of the Art)

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Abstract

Recent climate change has brought extremely heavy rains and widescale flooding to many areas around the globe. However, previous flood prediction methods usually require a lot of computation to obtain the prediction results and impose a heavy burden on the unit cost of the prediction. This paper proposes the use of a deep learning model (DLM) to overcome these problems. We alleviated the high computational overhead of this approach by developing a novel framework for the construction of lightweight DLMs. The proposed scheme involves training a convolutional neural network (CNN) by using a radar echo map in conjunction with historical flood records at target sites and using Grad-Cam to extract key grid cells from these maps (representing regions with the greatest impact on flooding) for use as inputs in another DLM. Finally, we used real radar echo maps of five locations and the flood heights record to verify the validity of the method proposed in this paper. The experimental results show that our proposed lightweight model can achieve similar or even better prediction accuracy at all locations with only about 5~15% of the operation time and about 30~35% of the memory space of the CNN. View Full-Text

Keywords: lightweight model; radar echo maps; convolutional neural network; Grad-Cam

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Chen, Y.-C.; Chang, T.-Y.; Chow, H.-Y.; Li, S.-L.; Ou, C.-Y. Using Convolutional Neural Networks to Build a Lightweight Flood Height Prediction Model with Grad-Cam for the Selection of Key Grid Cells in Radar Echo Maps. Water 2022, 14, 155. https://doi.org/10.3390/w14020155

AMA Style

Chen Y-C, Chang T-Y, Chow H-Y, Li S-L, Ou C-Y. Using Convolutional Neural Networks to Build a Lightweight Flood Height Prediction Model with Grad-Cam for the Selection of Key Grid Cells in Radar Echo Maps. Water. 2022; 14(2):155. https://doi.org/10.3390/w14020155

Chicago/Turabian Style

Chen, Yi-Chung, Tzu-Yin Chang, Heng-Yi Chow, Siang-Lan Li, and Chin-Yu Ou. 2022. "Using Convolutional Neural Networks to Build a Lightweight Flood Height Prediction Model with Grad-Cam for the Selection of Key Grid Cells in Radar Echo Maps" Water 14, no. 2: 155. https://doi.org/10.3390/w14020155

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Using Convolutional Neural Networks to Build a Lightweight Flood Height Prediction Model with Grad-Cam for the Selection of Key Grid Cells in Radar Echo Maps

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