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Prediction of Heavy Rain Damage Using Deep Learning
 
 
Article

Transfer Learning with Convolutional Neural Networks for Rainfall Detection in Single Images

1
School of Engineering, University of Basilicata, 85100 Potenza, Italy
2
National Research Institute for Earth Science and Disaster Resilience-NIED, Tsukuba 305-0006, Japan
*
Authors to whom correspondence should be addressed.
Academic Editor: Scott Curtis
Water 2021, 13(5), 588; https://doi.org/10.3390/w13050588
Received: 23 January 2021 / Revised: 14 February 2021 / Accepted: 19 February 2021 / Published: 24 February 2021
Near real-time rainfall monitoring at local scale is essential for urban flood risk mitigation. Previous research on precipitation visual effects supports the idea of vision-based rain sensors, but tends to be device-specific. We aimed to use different available photographing devices to develop a dense network of low-cost sensors. Using Transfer Learning with a Convolutional Neural Network, the rainfall detection was performed on single images taken in heterogeneous conditions by static or moving cameras without adjusted parameters. The chosen images encompass unconstrained verisimilar settings of the sources: Image2Weather dataset, dash-cams in the Tokyo Metropolitan area and experiments in the NIED Large-scale Rainfall Simulator. The model reached a test accuracy of 85.28% and an F1 score of 0.86. The applicability to real-world scenarios was proven with the experimentation with a pre-existing surveillance camera in Matera (Italy), obtaining an accuracy of 85.13% and an F1 score of 0.85. This model can be easily integrated into warning systems to automatically monitor the onset and end of rain-related events, exploiting pre-existing devices with a parsimonious use of economic and computational resources. The limitation is intrinsic to the outputs (detection without measurement). Future work concerns the development of a CNN based on the proposed methodology to quantify the precipitation intensity. View Full-Text
Keywords: rain detection; transfer learning; convolutional neural networks; single image classification; rainfall; cameras rain detection; transfer learning; convolutional neural networks; single image classification; rainfall; cameras
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MDPI and ACS Style

Notarangelo, N.M.; Hirano, K.; Albano, R.; Sole, A. Transfer Learning with Convolutional Neural Networks for Rainfall Detection in Single Images. Water 2021, 13, 588. https://doi.org/10.3390/w13050588

AMA Style

Notarangelo NM, Hirano K, Albano R, Sole A. Transfer Learning with Convolutional Neural Networks for Rainfall Detection in Single Images. Water. 2021; 13(5):588. https://doi.org/10.3390/w13050588

Chicago/Turabian Style

Notarangelo, Nicla Maria, Kohin Hirano, Raffaele Albano, and Aurelia Sole. 2021. "Transfer Learning with Convolutional Neural Networks for Rainfall Detection in Single Images" Water 13, no. 5: 588. https://doi.org/10.3390/w13050588

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