Application of Machine Learning Models for Flood Forecasting
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: 25 February 2025 | Viewed by 109
Special Issue Editor
Interests: disaster mitigation; flood modeling; IoT; early warning systems; flood damage; emergency response
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Flooding is widely acknowledged as one of the most devastating natural disasters on Earth. Many researchers have dedicated significant efforts to studying topics related to flooding, with the goal of producing outcomes that can help alleviate the impact of this phenomenon. However, the frequency and severity of flooding events have increased due to climate change. While this has resulted in more flood events and damage, it has also led to the availability of more data that can aid researchers in improving flood-related studies. The recent advancements in machine learning models and their diverse applications have captured researchers' attention. One key advantage of machine learning models is their ability to make predictions based solely on the presence of past flood data, removing the need for extensive geographical parameters and observations for calibration and validation. Consequently, the application of machine learning models has become the latest trend in flood-related research. This Special Issue will delve into various machine learning models for flood simulations and their applications in disaster mitigation and prevention. The Special Issue aims to provide valuable information to readers from different backgrounds, such as academia and engineering, who are identifying breakthroughs in their research area or practical implementations for flood applications.
Dr. Tsunhua Yang
Guest Editor
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Keywords
- machine learning
- data science
- flood modeling
- disasters
- climate change
- forecasting
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