# A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data

arXiv: https://arxiv.org/abs/2006.09201 · HTML: https://mlsanity.com/paper/2006.09201/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features](https://mlsanity.com/paper/2111.08450/) (2021)
- [ELEV-VISION-SAM: Integrated Vision Language and Foundation Model for Automated Estimation of Building Lowest Floor Elevation](https://mlsanity.com/paper/2404.12606/) (2024)
- [FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation](https://mlsanity.com/paper/2405.14232/) (2024)
- [Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery](https://mlsanity.com/paper/2506.06667/) (2025)
- [CrisiSense-RAG: Crisis Sensing Multimodal Retrieval-Augmented Generation for Rapid Disaster Impact Assessment](https://mlsanity.com/paper/2602.13239/) (2026)
- [A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping](https://mlsanity.com/paper/2506.13201/) (2025)
- [High-Resolution Flood Probability Mapping Using Generative Machine Learning with Large-Scale Synthetic Precipitation and Inundation Data](https://mlsanity.com/paper/2409.13936/) (2024)
- [ZeroFlood: Flood Hazard Mapping from Single-Modality SAR Using Geo-Foundation Models](https://mlsanity.com/paper/2510.23364/) (2025)
- [Towards Daily High-resolution Inundation Observations using Deep Learning and EO](https://mlsanity.com/paper/2208.09135/) (2022)
- [Training-free retrieval-augmented generation with reinforced reasoning for flood damage nowcasting](https://mlsanity.com/paper/2602.10312/) (2026)

## Abstract (arXiv)

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.