An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks
An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks
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DOI：
10.1029/2022ea002385
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发表时间：
2023-01
影响因子：
3.1
通讯作者：
L. Windheuser;R. Karanjit;R. Pally;S. Samadi;N. Hubig
中科院分区：
文献类型：
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作者：
L. Windheuser;R. Karanjit;R. Pally;S. Samadi;N. Hubig
在洪水图像中检测和分类不同类型的标签的自动化方法在本研究中具有重要的应用。两个美国地质调查局（USGS）衡量车站，即哥伦布和佐治亚州乔治亚州的Sweetwater Creek。策略性地位于监测站附近，大约每30 s刷新一次。然后，对训练的长期记忆（LSTM），密集的模型和CNN进行了训练，以预测洪水阶段时间序列数据（6、12，12，12， 24和48小时的结果表明，如果算法在网络前堆叠，则U-net CNN具有更高的图像分割精度。在Sweetwater Creek的0.0035英尺，对于时间序列的预测，在三种型号中，LSTM在两个历史上都更准确地预测了洪水阶段的估计。 （2015–2022）以及实时的预测，特别是在24和48个小时的时间标准中，我们广泛评估了针对当前状态的洪水阶段预测系统，部分是在现实的人群中。 。
The use of automated methods for detecting and classifying different types of labels in flood images have important applications in hydrologic prediction. In this research, we propose a fully automated end‐to‐end image detection system to predict flood stage data using deep neural networks across two US Geological Survey (USGS) gauging stations, that is, the Columbus and the Sweetwater Creek, Georgia, USA. The images were driven from the USGS live river web cameras, which were strategically located nearby the monitoring stations and refreshed roughly every 30 s. To estimate the flood stage, a U‐Net Convolutional Neural Network (U‐Net CNN) was first stacked on top of a segmentation model for noise and feature reduction that diminished the number of images needed for training. A Long Short‐Term Memory (LSTM), a dense model, and a CNN were then trained to predict the flood stage time series data in near real‐time (6, 12, 24, and 48 hr). The results revealed that the U‐Net CNN has a higher accuracy for image segmentation if the algorithm is stacked in front of the network. The absolute error with the U‐Net was 0.0654 feet at the Columbus while it was 0.0035 feet at the Sweetwater Creek, which were practically low for flood stage estimation. For time series prediction, among three models, the LSTM predicted the flood stage values more accurately during both historical (2015–2022) as well as real‐time forecasts, particularly for 24 and 48 hr timescales. We extensively evaluated the proposed flood stage prediction system against current state‐of‐the‐art methodologies partly crowd‐sourced and mined in real‐time.