The objective of this study is to create and test a hybrid deep learning (DL) 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 DL 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 100 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.

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This paper presents a Bayesian network model to assess the vulnerability of the flood control infrastructure and to simulate failure cascade based on the topological structure of flood control networks along with hydrological information gathered from sensors. Two measures are proposed to characterize the flood control network vulnerability and failure cascade: (a) node failure probability (NFP), which determines the failure likelihood of each network component under each scenario of rainfall event, and (b) failure cascade susceptibility, which captures the susceptibility of a network component to failure due to failure of other links. The proposed model was tested in both single watershed and multiple watershed scenarios in Harris County, Texas using historical data from three different flooding events, including Hurricane Harvey in 2017. The proposed model was able to identify the most vulnerable flood control network segments prone to flooding in the face of extreme rainfall. The framework and results furnish a new tool and insights to help decision-makers to prioritize infrastructure enhancement investments and actions. The proposed Bayesian network modeling framework also enables simulation of failure cascades in flood control infrastructures, and thus could be used for scenario planning as well as near-real-time inundation forecasting to inform emergency response planning and operation, and hence improve the flood resilience of urban areas. 668 wileyonlinelibrary.com/journal/mice Comput Aided Civ Inf. 2020;35:668-684.

With the rapid development of artificial intelligence technology and the rapid progress of big data technology, based on the theory of artificial intelligence fusion systems based on the lives of college students, and the use of advanced technology, university courses have also expanded in multiple directions. A reasonable and scientific development model of electronic technology design. Derived by using electronic data as a carrier to optimize the derivative tools of university courses and improve performance in many aspects such as learning and sports. Using the empirical formula of the Big Data Fourier algorithm, the manual verification was verified through the statistical comparison and analysis of the competitive auxiliary review system. The application of intelligent systems in the fusion system of university curriculum derivatives and artificial intelligence algorithms based on Internet big data play an active role in the fusion system of university curriculum derivatives.

This paper presents a deep learning model based on the integration of physical and social sensors data for predictive watershed flood monitoring. The data from flood sensors and 3-1-1 reports data are mapped and fused through a multi-variate time series approach. This data format is able to increase the data availability (partially due to sparsely installed physical sensors and fewer reported flood incidents in less urbanized areas) and capture both spatial and temporal interactions between different watersheds and historical events. We use Harris County, TX as the study site and obtained 7 historical flood events data for training, validating, and testing the flood prediction model. The model predicts the flood probability of each watershed in the next 24 hours. By comparing the flood prediction performance of three different datasets (i.e., flood sensor only, 3-1-1 reports only, and integrated dataset), we conclude that the integrated dataset achieves the best flood prediction performance with an accuracy of 0.825, Area Under the Receiver operating characteristics Curve (AURC) of 0.902, Area Under the Precision-Recall Curve (AUPRC) of 0.883, Area Under the F-measure Curve (AUFC) of 0.762, and Max. F-measure of 0.788.