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Urban Flood Prediction Using Deep Neural Network with Data Augmentation
Water ( IF 3.0 ) Pub Date : 2020-03-22 , DOI: 10.3390/w12030899
Hyun Il Kim , Kun Yeun Han

Data-driven models using an artificial neural network (ANN), deep learning (DL) and numerical models are applied in flood analysis of the urban watershed, which has a complex drainage system. In particular, data-driven models using neural networks can quickly present the results and be used for flood forecasting. However, not a lot of data with actual flood history and heavy rainfalls are available, it is difficult to conduct a preliminary analysis of flood in urban areas. In this study, a deep neural network (DNN) was used to predict the total accumulative overflow, and because of the insufficiency of observed rainfall data, 6 h of rainfall were surveyed nationwide in Korea. Statistical characteristics of each rainfall event were used as input data for the DNN. The target value of the DNN was the total accumulative overflow calculated from Storm Water Management Model (SWMM) simulations, and the methodology of data augmentation was applied to increase the input data. The SWMM is one-dimensional model for rainfall-runoff analysis. The data augmentation allowed enrichment of the training data for DNN. The data augmentation was applied ten times for each input combination, and the practicality of the data augmentation was determined by predicting the total accumulative overflow over the testing data and the observed rainfall. The prediction result of DNN was compared with the simulated result obtained using the SWMM model, and it was confirmed that the predictive performance was improved on applying data augmentation.

中文翻译:

使用深度神经网络和数据增强进行城市洪水预测

使用人工神经网络 (ANN)、深度学习 (DL) 和数值模型的数据驱动模型应用于具有复杂排水系统的城市流域的洪水分析。特别是,使用神经网络的数据驱动模型可以快速呈现结果并用于洪水预测。但是,由于没有大量具有实际洪水历史和强降雨的数据,很难对城市地区的洪水进行初步分析。本研究使用深度神经网络(DNN)预测总累积溢流,由于观测到的降雨数据不足,在韩国全国范围内调查了6小时的降雨量。每个降雨事件的统计特征被用作 DNN 的输入数据。DNN 的目标值是根据雨水管理模型 (SWMM) 模拟计算的总累积溢流,并应用数据增强方法来增加输入数据。SWMMH 是降雨径流分析的一维模型。数据增强允许丰富 DNN 的训练数据。对每个输入组合应用十次数据增强,通过预测测试数据和观测降雨的总累积溢出来确定数据增强的实用性。将 DNN 的预测结果与使用 SWMM 模型获得的模拟结果进行比较,证实应用数据增强后预测性能有所提高。并应用数据增强的方法来增加输入数据。SWMMH 是降雨径流分析的一维模型。数据增强允许丰富 DNN 的训练数据。对每个输入组合应用十次数据增强,通过预测测试数据和观测降雨的总累积溢出来确定数据增强的实用性。将 DNN 的预测结果与使用 SWMM 模型获得的模拟结果进行比较,证实应用数据增强后预测性能有所提高。并应用数据增强的方法来增加输入数据。SWMMH 是降雨径流分析的一维模型。数据增强允许丰富 DNN 的训练数据。对每个输入组合应用十次数据增强,通过预测测试数据和观测降雨的总累积溢出来确定数据增强的实用性。将 DNN 的预测结果与使用 SWMM 模型获得的模拟结果进行比较,证实应用数据增强后预测性能有所提高。对每个输入组合应用十次数据增强,通过预测测试数据和观测降雨的总累积溢出来确定数据增强的实用性。将 DNN 的预测结果与使用 SWMM 模型获得的模拟结果进行比较,证实应用数据增强后预测性能有所提高。对每个输入组合应用十次数据增强,通过预测测试数据和观测降雨的总累积溢出来确定数据增强的实用性。将 DNN 的预测结果与使用 SWMM 模型获得的模拟结果进行比较,证实应用数据增强后预测性能有所提高。
更新日期:2020-03-22
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