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http://dx.doi.org/10.3741/JKWRA.2021.54.12.1233

Development of a method for urban flooding detection using unstructured data and deep learing  

Lee, Haneul (Program in Smart City Engineering, Inha University)
Kim, Hung Soo (Department of Civil Engineering, Inha University)
Kim, Soojun (Department of Civil Engineering, Inha University)
Kim, Donghyun (Program in Smart City Engineering, Inha University)
Kim, Jongsung (Program in Smart City Engineering, Inha University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.12, 2021 , pp. 1233-1242 More about this Journal
Abstract
In this study, a model was developed to determine whether flooding occurred using image data, which is unstructured data. CNN-based VGG16 and VGG19 were used to develop the flood classification model. In order to develop a model, images of flooded and non-flooded images were collected using web crawling method. Since the data collected using the web crawling method contains noise data, data irrelevant to this study was primarily deleted, and secondly, the image size was changed to 224×224 for model application. In addition, image augmentation was performed by changing the angle of the image for diversity of image. Finally, learning was performed using 2,500 images of flooding and 2,500 images of non-flooding. As a result of model evaluation, the average classification performance of the model was found to be 97%. In the future, if the model developed through the results of this study is mounted on the CCTV control center system, it is judged that the respons against flood damage can be done quickly.
Keywords
CNN; Image classification; Unstructured data; Urban flood; Web crawling;
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