# Deep Learning Models for Flood Predictions in South Florida

arXiv: https://arxiv.org/abs/2306.15907 · HTML: https://mlsanity.com/paper/2306.15907/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [Conditional Time Series Forecasting with Convolutional Neural Networks](https://mlsanity.com/paper/1703.04691/) (2017)
- [Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data](https://mlsanity.com/paper/1602.05875/) (2016)
- [Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting](https://mlsanity.com/paper/2305.04876/) (2023)

## Built on by (most cited first, 6 of 6)

- [TimeX++: Learning Time-Series Explanations with Information Bottleneck](https://mlsanity.com/paper/2405.09308/) (2024)
- [FIDLAR: Forecast-Informed Deep Learning Architecture for Flood Mitigation](https://mlsanity.com/paper/2402.13371/) (2024)
- [The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation](https://mlsanity.com/paper/2310.19166/) (2023)
- [Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates](https://mlsanity.com/paper/2310.07631/) (2023)
- [Retrieval-Augmented Foundation Models for Water Level Prediction in the Everglades](https://mlsanity.com/paper/2508.04888/) (2025)
- [Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems](https://mlsanity.com/paper/2605.19366/) (2026)

## Abstract (arXiv)

Simulating and predicting the water level/stage in river systems is essential for flood warnings, hydraulic operations, and flood mitigations. Physics-based detailed hydrological and hydraulic computational tools, such as HEC-RAS, MIKE, and SWMM, can be used to simulate a complete watershed and compute the water stage at any point in the river system. However, these physics-based models are computationally intensive, especially for large watersheds and for longer simulations, since they use detailed grid representations of terrain elevation maps of the entire watershed and solve complex partial differential equations (PDEs) for each grid cell. To overcome this problem, we train several deep learning (DL) models for use as surrogate models to rapidly predict the water stage. A portion of the Miami River in South Florida was chosen as a case study for this paper. Extensive experiments show that the performance of various DL models (MLP, RNN, CNN, LSTM, and RCNN) is significantly better than that of the physics-based model, HEC-RAS, even during extreme precipitation conditions (i.e., tropical storms), and with speedups exceeding 500x. To predict the water stages more accurately, our DL models use both measured variables of the river system from the recent past and covariates for which predictions are typically available for the near future.