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Found 7 papers, 2 papers with code
FIDLAR: Forecast-Informed Deep Learning Architecture for Flood Mitigation
FIDLAR seamlessly integrates two neural network modules: one called the Flood Manager, which is responsible for generating water pre-release schedules, and another called the Flood Evaluator, which assesses these generated schedules.
The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation
Floods can cause horrific harm to life and property.
Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates
In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems.
Deep Learning Models for Flood Predictions in South Florida
To overcome this problem, we train several deep learning (DL) models for use as surrogate models to rapidly predict the water stage.
Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting
Accurate time series forecasting is a fundamental challenge in data science.
Time Series Forecasting (TSF) Using Various Deep Learning Models
In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future.
Cache Replacement as a MAB with Delayed Feedback and Decaying Costs
We derive an optimal learning rate for EXP4-DFDC that defines the balance between exploration and exploitation and proves theoretically that the expected regret of our algorithm is a vanishing quantity as a function of time.
