# Flood Prediction Using Classical and Quantum Machine Learning Models

arXiv: https://arxiv.org/abs/2407.01001 · HTML: https://mlsanity.com/paper/2407.01001/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [Enhancing Quantum Support Vector Machines through Variational Kernel Training](https://mlsanity.com/paper/2305.06063/) (2023)

## Built on by (most cited first, 2 of 2)

- [Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region](https://mlsanity.com/paper/2411.18141/) (2024)
- [Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification](https://mlsanity.com/paper/2604.09374/) (2026)

## Abstract (arXiv)

This study investigates the potential of quantum machine learning to improve flood forecasting we focus on daily flood events along Germany's Wupper River in 2023 our approach combines classical machine learning techniques with QML techniques this hybrid model leverages quantum properties like superposition and entanglement to achieve better accuracy and efficiency classical and QML models are compared based on training time accuracy and scalability results show that QML models offer competitive training times and improved prediction accuracy this research signifies a step towards utilizing quantum technologies for climate change adaptation we emphasize collaboration and continuous innovation to implement this model in real-world flood management ultimately enhancing global resilience against floods