Flooding poses significant risks to communities in wetlands and densely urbanized regions, necessitating robust prevention ...

We present a knowledge‐guided machine learning framework for operational hydrologic forecasting at the catchment scale. Our approach, a Factorized Hierarchical Neural Network (FHNN), has two main ...

A comprehensive, modular Python automation system for HEC-RAS flood analysis workflows. This professional-grade tool demonstrates advanced hydraulic modeling capabilities, Python integration, and ...

Rainfall -based methods are widely used for flash flood monitoring and warning. However, the conventional approach of issuing an alert based on any single associated station exceeding a threshold ...

Abstract: Floods rank as the most harmful natural disaster because they lead to major fatalities and extensive damage to building infrastructure. The developed system uses machine learning to forecast ...

MANVILLE, N.J.—Richard Onderko said he will never forget the terrifying Saturday morning back in 1971 when the water rose so swiftly at his childhood home here that he and his brother had to be ...

Abstract: Public safety in disaster operations relies heavily on accurate predictions which help minimize flood damages. The Long Short-Term Memory (LSTM) neural network demonstrates exceptional ...