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Machine Learning Methods for Flood Computation
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Machine Learning Methods for Flood Computation

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: 30 June 2025 | Viewed by 2884

Special Issue Editors

Prof. Dr. Kyung Soo Jun

E-Mail Website
Guest Editor
Graduate School of Water Resources, Sungkyunkwan University, Suwon, Republic of Korea
Interests: flood; river hydraulics; computational hydraulics; environmental hydraulics; machine learning
Prof. Dr. Yeonjoo Kim

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Yonsei University, Seoul, Republic of Korea
Interests: land surface model (LSM); AI-LSM; differentiable modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giha Lee

E-Mail Website
Guest Editor
Department of Advanced Science and Technology Convergence, Kyungpook National University, 2559 Gyeongsang, Sangju, Republic of Korea
Interests: rainfall runoff; soil erosion; AI; satellite; water disaster
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Floods are among the most devastating natural disasters, posing severe threats to human life and society. Accurate modeling and prediction of floods have historically faced significant challenges due to the complexity of modeling and the presence of various uncertainties. However, rapid advances in machine learning methods have introduced novel algorithms, models, and frameworks that enhance the computational efficiency, accuracy, and reliability of flood computations. Integrating machine learning methods into hydrological processes has significantly contributed to risk reduction and damage minimization associated with floods. This Special Issue is dedicated to presenting original research findings and reviews on the latest applications of machine learning methods in flood modeling and prediction. We encourage submissions addressing, but not limited to, the following topics:  

  • Novel machine learning methods for flood simulation and forecasting;
  • Hybridization of existing machine learning methods to improve the accuracy and efficiency of flood modeling;
  • Integration of conventional flood routing models with machine learning methods;
  • Flood risk analysis using machine learning methods;
  • Model parameter optimization algorithm based on machine learning methods.

Prof. Dr. Kyung Soo Jun
Prof. Dr. Yeonjoo Kim
Prof. Dr. Giha Lee
Guest Editors

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
  • deep learning
  • flood
  • flood modeling
  • flood control
  • flood management
  • flood forecast

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Published Papers (3 papers)

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Research

20 pages, 30643 KiB  
Article
Physics-Guided Deep Learning for Spatiotemporal Evolution of Urban Pluvial Flooding
by Hyuna Woo, Hyeonjin Choi, Minyoung Kim and Seong Jin Noh
Water 2025, 17(8), 1239; https://doi.org/10.3390/w17081239 - 21 Apr 2025
Viewed by 324
Abstract
Climate change and rapid urbanization have increased the risk of urban flooding, making timely and accurate flood prediction crucial for disaster response. However, conventional physics-based models are limited in real-time applications due to their high computational costs. Recent advances in deep learning have [...] Read more.
Climate change and rapid urbanization have increased the risk of urban flooding, making timely and accurate flood prediction crucial for disaster response. However, conventional physics-based models are limited in real-time applications due to their high computational costs. Recent advances in deep learning have enabled the development of efficient surrogate models that capture complex nonlinear relationships in hydrological processes. This study presents a deep learning-based surrogate model designed to efficiently reproduce the spatiotemporal evolution of urban pluvial flooding using data from physics-based models. For the Oncheon-cheon catchment in Busan, the spatiotemporal evolution of inundation at a 10 m spatial resolution was simulated using the physics-based model for various synthetic inundation scenarios to train the deep learning model based on a Convolutional Neural Network (CNN). The training dataset was constructed using synthetic rainfall scenarios based on probabilistic rainfall data, while the model was validated using both a synthetic flood event and a historical flood event from July 2020 with observed ground-based rainfall measurements. The model’s performance was evaluated using quantitative metrics, including the Hit Rate (HR), False Alarm Ratio (FAR), and Critical Success Index (CSI), by comparing results against both synthetic and real (historical) flood events. Validation results demonstrated high reproducibility, with a CSI of 0.79 and 0.73 for the synthetic and real experiments, respectively. In terms of computational efficiency, the deep learning model achieved a speedup 16.4 times the parallel version and 82.2 times the sequential version of the physics-based model, demonstrating its applicability for near real-time flood prediction. The findings of this study contribute to the advancement of urban flood prediction and early warning systems by offering a cost-effective, computationally efficient alternative to conventional physics-based flood modeling, enabling faster and more adaptive flood risk management. Full article
(This article belongs to the Special Issue Machine Learning Methods for Flood Computation)
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21 pages, 15316 KiB  
Article
Rapid Urban Flood Detection Using PlanetScope Imagery and Thresholding Methods
by Linh Nguyen Van, Giang V. Nguyen, Younghun Kim, May T. T. Do, Seongcheon Kwon, Jinhyeong Lee and Giha Lee
Water 2025, 17(7), 1005; https://doi.org/10.3390/w17071005 - 28 Mar 2025
Viewed by 374
Abstract
With advances in optical satellite remote sensing, urban flood mapping (UFM) leveraging water’s distinct spectral characteristics for water identification is preferred and has gained more attention. PlanetScope’s daily 3 m resolution imagery enables detailed and time-sensitive flood monitoring. Unlike machine learning, which requires [...] Read more.
With advances in optical satellite remote sensing, urban flood mapping (UFM) leveraging water’s distinct spectral characteristics for water identification is preferred and has gained more attention. PlanetScope’s daily 3 m resolution imagery enables detailed and time-sensitive flood monitoring. Unlike machine learning, which requires extensive training data, thresholding methods offer a faster and more adaptable solution for binary classification. Three global (Yen’s, Otsu’s, Isodata) and three local (Niblack, Sauvola, Gonzalez) thresholding methods, with their parameters optimized for each case study, were assessed in this study. Additionally, a hybrid approach was proposed and evaluated. In this approach, local thresholds are computed for each pixel, using the respective local thresholding method. Then, a global threshold is derived by calculating the simple arithmetic mean of all these local thresholds. This global threshold is subsequently applied across the entire image to perform a binary classification, distinguishing flooded from non-flooded areas. To enhance water detection, we also evaluated 26 remote sensing indices. Each was computed using two formulations—the normalized difference and the ratio—where at least one of the eight PlanetScope bands was NIR or RedEdge to enhance water detection. We tested this methodology on three flooding events with different water coverage scenarios in Brazil (34% water coverage), the USA (11%), and Australia (21%). The model performance was validated using the Matthews correlation coefficient (MCC) and the Fowlkes–Mallows index (FMI). The results demonstrated that combining PlanetScope imagery with carefully selected remote sensing indices and thresholding techniques enhances efficient UFM. The hybrid methods outperformed the others by capturing local variations while maintaining global consistency, with the MCC and the FMI exceeding 0.9. The indices incorporating NIR and RedEdge, particularly NDRE, achieved the highest accuracy. However, each flood event was best classified by a different combination of method and index, indicating that it is important to carefully select the appropriate remote sensing indices and thresholding techniques for each specific case. This framework provides a fast, effective solution for UFM, adaptable to diverse urban environments and flood conditions. Full article
(This article belongs to the Special Issue Machine Learning Methods for Flood Computation)
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26 pages, 8954 KiB  
Article
Deep Learning Ensemble for Flood Probability Analysis
by Fred Sseguya and Kyung-Soo Jun
Water 2024, 16(21), 3092; https://doi.org/10.3390/w16213092 - 29 Oct 2024
Cited by 2 | Viewed by 1686
Abstract
Predicting flood events is complex due to uncertainties from limited gauge data, high data and computational demands of traditional physical models, and challenges in spatial and temporal scaling. This research innovatively uses only three remotely sensed and computed factors: rainfall, runoff and temperature. [...] Read more.
Predicting flood events is complex due to uncertainties from limited gauge data, high data and computational demands of traditional physical models, and challenges in spatial and temporal scaling. This research innovatively uses only three remotely sensed and computed factors: rainfall, runoff and temperature. We also employ three deep learning models—Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—along with a deep neural network ensemble (DNNE) using synthetic data to predict future flood probabilities, utilizing the Savitzky–Golay filter for smoothing. Using a hydrometeorological dataset from 1993–2022 for the Nile River basin, six flood predictors were derived. The FNN and LSTM models exhibited high accuracy and stable loss, indicating minimal overfitting, while the CNN showed slight overfitting. Performance metrics revealed that FNN achieved 99.63% accuracy and 0.999886 ROC AUC, CNN had 95.42% accuracy and 0.893218 ROC AUC, and LSTM excelled with 99.82% accuracy and 0.999967 ROC AUC. The DNNE outperformed individual models in reliability and consistency. Runoff and rainfall were the most influential predictors, while temperature had minimal impact. Full article
(This article belongs to the Special Issue Machine Learning Methods for Flood Computation)
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