Improved surface water mapping using satellite remote sensing imagery based on optimization of the Otsu threshold and effective selection of remote-sensing water index

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132771 - 132771

Опубликована: Фев. 1, 2025

Язык: Английский

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A Lake-Flood Forecasting Method Coupling the Ce-Qual-W2 and Pinn Models

Опубликована: Янв. 1, 2025

The limited availability and low accuracy of hydrological data severely influence the flood forecasting. To address this issue, paper proposes a new way to predict floods that combines CE-QUAL-W2 model for lakes' hydrodynamics with PINN physical information. is employed verify dynamic process water level volume in Lake during season. We input lake, verified by model, into model. Utilizing we can learn nonlinear patterns reservoir discharge from historical directly transform problem solving differential equations an optimization loss functions regular equations. real-time simulated also incorporated Xin-An-Jiang (XAJ) Long Short-Term Memory (LSTM) was compared results prediction performance obtained CE-QUAL-W2&PINN This study selects Luoma as research subject, choosing 35 representative events occurred between 1960 2022. show that, (1) events, relative errors observed values were all within 20%, indicating good simulation accuracy. (2) Compared LSTM XAJ models, demonstrates higher faster forecasting capabilities 3-hour period, achieving improvement approximately 30% both training testing. (3) overall determination coefficient CE-QUAL-W2&PIN stands at 0.919. error less than 10% flow periods.

Язык: Английский

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A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 524 - 524

Опубликована: Фев. 3, 2025

Climate change has led to an increase in global temperature and frequent intense precipitation, resulting a rise severe urban flooding worldwide. This growing threat is exacerbated by rapid urbanization, impervious surface expansion, overwhelmed drainage systems, particularly regions. As becomes more catastrophic causes significant environmental property damage, there urgent need understand address flood susceptibility mitigate future damage. review aims evaluate remote sensing datasets key parameters influencing provide comprehensive overview of the causative factors utilized mapping. also highlights evolution traditional, data-driven, big data, GISs (geographic information systems), machine learning approaches discusses advantages limitations different mapping approaches. By evaluating challenges associated with current practices, this paper offers insights into directions for improving management strategies. Understanding identifying foundation developing effective resilient practices will be beneficial mitigating

Язык: Английский

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A Two-Level Early Warning System on Urban Floods Caused by Rainstorm

Sustainability, Год журнала: 2025, Номер 17(5), С. 2147 - 2147

Опубликована: Март 1, 2025

In recent years, the combined effects of rapid urbanization and climate change have led to frequent floods in urban areas. Rainstorm flood risk warning systems play a crucial role prevention mitigation. However, there has been limited research China on nationwide based rainfall predictions. This study constructs two-level early system (EWS) at national levels using two-dimensional hydrological–hydrodynamic model considering infiltration drainage standards. A methodology for rainstorm warnings is proposed, leveraging short-term high-resolution forecast data provide 231 cities central eastern China. Taking Beijing as an example, refined technique targeting city, district, street scales developed. We validated with monitoring from “7.31” event 2023 Beijing, demonstrating its applicability. It expected that findings this will serve valuable reference

Язык: Английский

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Flo-Sr: Deep Learning-Based Urban Flood Super-Resolution Model

Опубликована: Янв. 1, 2025

Язык: Английский

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Capturing Urban Pluvial River Flooding Features Based on the Fusion of Physically Based and Data-Driven Approaches

Sustainability, Год журнала: 2025, Номер 17(6), С. 2524 - 2524

Опубликована: Март 13, 2025

Driven by climate change and rapid urbanization, pluvial flooding is increasingly endangering urban environments, prompting the widespread use of coupled hydrological–hydrodynamic models that enable more accurate flood simulations enhanced forecasting. The simulation method for river caused heavy rainfall has garnered growing attention. However, existing studies primarily concentrate on prediction using hydrodynamic or machine learning models, there remains a dearth comprehensive framework couples both to simulate temporal evolution changes. This research proposes novel simulating integrating physically based with deep approaches. sample set through data augmentation Generative Adversarial Networks, scheduling control signals are incorporated into encoder–decoder architecture results demonstrate strong model performance, provided model’s structural complexity aligned available training data. After incorporating information, simulated water level process exhibits “double-peak” pattern, where first peak noticeably lower than under non-scheduling conditions. current introduces an innovative analyzing large-scale flooding, offering valuable perspectives planning mitigation strategies.

Язык: Английский

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A real-time prediction model for instantaneous dam-break flood evolution of concrete gravity dams based on attention mechanism and spatiotemporal multiple features

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 150, С. 110616 - 110616

Опубликована: Март 23, 2025

Язык: Английский

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Estimation of Flood Inundation Area Using Soil Moisture Active Passive Fractional Water Data with an LSTM Model

Sensors, Год журнала: 2025, Номер 25(8), С. 2503 - 2503

Опубликована: Апрель 16, 2025

Accurate flood monitoring and forecasting techniques are important continue to be developed for improved disaster preparedness mitigation. Flood estimation using satellite observations with deep learning algorithms is effective in detecting patterns environmental relationships that may overlooked by conventional methods. Soil Moisture Active Passive (SMAP) fractional water (FW) was used as a reference estimate areas long short-term memory (LSTM) model combination of soil moisture information, rainfall forecasts, floodplain topography. To perform modeling LSTM, datasets different spatial resolutions were resampled 30 m resolution bicubic interpolation. The model’s efficacy quantified validating the LSTM-based inundation area mask from Senti-nel-1 SAR images regions topographic characteristics. average under curve (AUC) value LSTM 0.93, indicating high accuracy FW. confusion matrix-derived metrics validate had high-performance ~0.9. SMAP FW showed optimal performance low-covered vegetation, seasonal variations flat regions. estimates show methodological promise proposed framework resilience.

Язык: Английский

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Research on flood peak prediction in the three gorges region based on similarity search with multisource information fusion

Earth Science Informatics, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 18, 2024

Язык: Английский

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