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Application of Machine Learning Models for Flood Forecasting
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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: 20 August 2025 | Viewed by 1595

Special Issue Editor

Dr. Tsunhua Yang

E-Mail Website
Guest Editor
Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu City 300093, Taiwan
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • data science
  • flood modeling
  • disasters
  • climate change
  • forecasting

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Published Papers (2 papers)

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Research

16 pages, 4331 KiB  
Article
Combination of Large Language Models and Portable Flood Sensors for Community Flood Response: A Preliminary Study
by Tsung-Hua Ou, Tsun-Hua Yang and Pei-Zen Chang
Water 2025, 17(7), 1055; https://doi.org/10.3390/w17071055 - 2 Apr 2025
Viewed by 339
Abstract
The effectiveness of early warning systems can help people take action to mitigate the impact of extreme weather events once warnings are issued. The early warning systems developed by public agencies usually issue standard messages that, in many situations, may not affect all [...] Read more.
The effectiveness of early warning systems can help people take action to mitigate the impact of extreme weather events once warnings are issued. The early warning systems developed by public agencies usually issue standard messages that, in many situations, may not affect all the people who receive the messages. In the long run, this can lead to behaviors in people who may not respond to relevant warnings, resulting in inefficiency. Users demand faster and more customized information that matches their needs, such as “How does this affect me right now?” or “What can I do to mitigate the impact?” This study proposes a decentralized framework at the community level that includes custom Internet of Things (IoT) sensors for timely information monitoring and large language models (LLMs) for the generation of user-defined warning messages. The sensors have the advantages of easy installation, low cost, and affordable maintenance fees. The trained LLMs expedite information processing given specific prompts and generate customized response messages to the users. In addition, the framework is established within a serverless environment, enabling rapid deployment and scalability. This integration of IoT sensors and LLMs demonstrates how the system performs once sensors detect flooding and how LLMs can deliver real-time, efficient, and localized action-ready information in different scenarios. This combination significantly enhances the responsiveness during flood events. Full article
(This article belongs to the Special Issue Application of Machine Learning Models for Flood Forecasting)
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28 pages, 9281 KiB  
Article
Water Level Forecasting Combining Machine Learning and Ensemble Kalman Filtering in the Danshui River System, Taiwan
by Jin-Cheng Fu, Mu-Ping Su, Wen-Cheng Liu, Wei-Che Huang and Hong-Ming Liu
Water 2024, 16(23), 3530; https://doi.org/10.3390/w16233530 - 8 Dec 2024
Viewed by 889
Abstract
Taiwan faces intense rainfall during typhoon seasons, leading to rapid increases in water level in rivers. Accurate flood forecasting in rivers is essential for protecting lives and property. The objective of this study is to develop a river flood forecasting model combining multiple [...] Read more.
Taiwan faces intense rainfall during typhoon seasons, leading to rapid increases in water level in rivers. Accurate flood forecasting in rivers is essential for protecting lives and property. The objective of this study is to develop a river flood forecasting model combining multiple additive regression trees (MART) and ensemble Kalman filtering (EnKF). MART, a machine learning technique, predicts water levels for internal boundary conditions, correcting a one-dimensional (1D) unsteady flow model. EnKF further refines these predictions, enabling precise real-time forecasts of water levels in the Danshui River system for up to three hours lead time. The model was calibrated and validated using observed data from four historical typhoons to evaluate its accuracy. For the present time at three water level stations in the Danshui River system, the root mean square error (RMSE) ranged from 0.088 to 0.343 m, while the coefficient of determination (R2) ranged from 0.954 to 0.999. The validated model (module 1) was divided into two additional modules: module 2, which combined the ensemble unsteady flow model with inner boundary correction and MART, and module 3, which featured an ensemble 1D unsteady flow model without inner boundary correction. These modules were employed to forecast water levels at three stations from the present time to 3 h lead time during Typhoon Muifa in 2022. The study revealed that the Tu-Ti-Kung-Pi station was less affected by inner boundaries due to significant tidal influences. Consequently, excluding the upstream and downstream boundaries, Tu-Ti-Kung-Pi station showed a superior RMSE trend from present time to 3 h lead time across all three modules. Conversely, the Taipei Bridge and Bailing Bridge stations began using inner boundary forecast values for correction from 1 h to 3 h lead times. This increased the uncertainty of the inner boundary, resulting in higher RMSE values for these locations in modules 1 and 2 compared to module 3. Full article
(This article belongs to the Special Issue Application of Machine Learning Models for Flood Forecasting)
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