一种基于河道网络传感器数据的洪水预警和态势感知混合深度学习模型
A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data
摘要：
本研究的目的是创建和测试一个混合深度学习模型FastGRNN-FCN（快速、准确、稳定和微小的门控递归神经网络-全卷积网络），用于使用通道网络传感器数据进行城市洪水预测和态势感知。该研究使用得克萨斯州哈里斯县作为试验台，并从三个历史洪水事件（例如，2016年纳税日洪水、2016年阵亡将士纪念日洪水和2017年哈维飓风洪水），用于训练和验证混合深度学习模型。洪水数据被分成一个多变量的时间序列，并作为模型的输入。每个输入包括九个变量，包括所研究的信道传感器及其在信道网络中的前趋和后继传感器的信息。采用查准率-查全率曲线和F-测度来确定最优模型参数集。通过对不同的权值和阈值的考察，经过100次迭代，得到了权值为1、临界阈值为0.59的最优模型。测试准确率和F-测度最终分别达到97.8%和0.792。然后，该模型在预测2019年休斯顿伊梅尔达洪水中进行了测试，结果显示与经验洪水非常匹配。结果表明，该模型能够准确预测洪水的时空传播和衰退，并提供了一个预测性的洪水预警工具，优先洪水响应和资源分配策略的应急响应官员。
英文摘要：
The objective of this study is to create and test a hybrid deep learning model, FastGRNN-FCN (Fast, Accurate, Stable and Tiny Gated Recurrent Neural Network-Fully Convolutional Network), for urban flood prediction and situation awareness using channel network sensors data. The study used Harris County, Texas as the testbed, and obtained channel sensor data from three historical flood events (e.g., 2016 Tax Day Flood, 2016 Memorial Day flood, and 2017 Hurricane Harvey Flood) for training and validating the hybrid deep learning model. The flood data are divided into a multivariate time series and used as the model input. Each input comprises nine variables, including information of the studied channel sensor and its predecessor and successor sensors in the channel network. Precision-recall curve and F-measure are used to identify the optimal set of model parameters. The optimal model with a weight of 1 and a critical threshold of 0.59 are obtained through one hundred iterations based on examining different weights and thresholds. The test accuracy and F-measure eventually reach 97.8% and 0.792, respectively. The model is then tested in predicting the 2019 Imelda flood in Houston and the results show an excellent match with the empirical flood. The results show that the model enables accurate prediction of the spatial-temporal flood propagation and recession and provides emergency response officials with a predictive flood warning tool for prioritizing the flood response and resource allocation strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
