A Digital Twin Dam and Watershed Management Platform

Water, Год журнала: 2023, Номер 15(11), С. 2106 - 2106

Опубликована: Июнь 1, 2023

This paper presents an innovative digital twin dam and watershed management platform, K-Twin SJ, that utilizes real-time data simulation models to support decision-making for flood response water resource management. The platform includes a GIS-based geospatial of the entire Sumjin river system in Korea, with high-precision topography facility information dams rivers (watershed area 4913 km2, length 173 km, 91 infrastructures). synchronizes such as rainfall, levels, flow rate, closed-circuit television (CCTV), incorporates three hydraulic hydrological efficient operation considering conditions. AI technology is also used predict level suggest optimal discharge scenarios. Additionally, geotechnical safety evaluation module levees, advanced drone monitoring rivers, CCTV video surveillance function. digital-twin-based supports smart responses contributes reducing flooding damage through better

Язык: Английский

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Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling

Environmental Modelling & Software, Год журнала: 2023, Номер 167, С. 105772 - 105772

Опубликована: Июль 5, 2023

Urban flooding is a major problem for cities around the world, with significant socio-economic consequences. Conventional real-time flood forecasting models rely on continuous time-series data and often have limited accuracy, especially longer lead times than 2 h s. This study proposes novel event-based decision support algorithm using identification, dataset generation, tree flowchart machine learning models. The results of applying framework to real-world case demonstrate higher accuracy in water level rise, (e.g., 2–3 s), compared traditional proposed reduces root mean square error by 50%, increases improves normalised Nash–Sutcliffe 20%. can significantly enhance forecasting, reducing occurrences both false alarms missing improving emergency response systems.

Язык: Английский

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Adapting machine learning for environmental spatial data - A review

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102634 - 102634

Опубликована: Май 11, 2024

Large-scale modeling of environmental variables is an increasingly complex but necessary task. In this paper, we review the literature on using machine learning to cope with challenges associated spatial autocorrelation. Our focus was studies in which researchers predicted a supervised regression algorithm that accounted for autocorrelation any part pipeline from data exploration model validation. Methods included explicit covariates, splitting training–testing, calculations, and independent exploratory analysis. Authors most often analysis had no impact values. We concluded there seems be overall systematic approach how account models. selected studies, appropriate method depended specific characteristics study. Using covariates training-testing provided more insights into method's applicability. summarize these provide considerations selecting method.

Язык: Английский

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SHAP-powered insights into spatiotemporal effects: Unlocking explainable Bayesian-neural-network urban flood forecasting

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 131, С. 103972 - 103972

Опубликована: Июнь 25, 2024

Given the increased incidence of pluvial floods due to climate change and urbanization, demand for highly efficient accurate modeling within urban drainage systems has intensified, making machine learning deep techniques increasingly popular. Nonetheless, these data-driven approaches face challenges in adequately capturing interpreting dynamic process-evolving features, especially spatiotemporal effects emanating from manholes during waterlogging events. To address issues, this study proposes a general framework that extracts using spatial Durbin model, integrates such with four models (i.e., artificial neural network, Bayesian network (BNN), light gradient boosting machine, long short-term memory network), clarifies decision-making processes best model by employing Shapley Additive Explanations (SHAP) method. The results indicate (1) BNN (BNNST) not only outperforms other benchmark but also provides forecasts quantifiable uncertainties; (2) compared original enhance models' understanding flooding dynamics, thereby improving predictive precision; (3) comprise roughly 14 % contributions BNNST's output, as interpreted SHAP-based explanations; (4) incorporating interpretability into technique underscores trustworthiness explanations at varying confidence levels, deepening processes.

Язык: Английский

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Real-Time Pump Scheduling in Water Distribution Networks Using Deep Reinforcement Learning

Journal of Water Resources Planning and Management, Год журнала: 2025, Номер 151(6)

Опубликована: Март 25, 2025

Язык: Английский

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Study on the Measurement of Interprovincial Carbon Emission Reduction Performance, Regional Gaps, and Spatial Convergence in China

Energy, Год журнала: 2025, Номер unknown, С. 134597 - 134597

Опубликована: Янв. 1, 2025

Язык: Английский

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A framework for incorporating rainfall data into a flooding digital twin

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132893 - 132893

Опубликована: Фев. 1, 2025

Язык: Английский

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Optimized green infrastructure planning at the city scale based on an interpretable machine learning model and multi-objective optimization algorithm: A case study of central Beijing, China

Landscape and Urban Planning, Год журнала: 2024, Номер 252, С. 105191 - 105191

Опубликована: Авг. 19, 2024

Язык: Английский

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A spatially distributed hydrodynamic model framework for urban flood hydrological and hydraulic processes involving drainage flow quantification

Journal of Hydrology, Год журнала: 2023, Номер 625, С. 130135 - 130135

Опубликована: Сен. 10, 2023

Язык: Английский

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State-of-the-Art Techniques for Real-Time Monitoring of Urban Flooding: A Review

Water, Год журнала: 2024, Номер 16(17), С. 2476 - 2476

Опубликована: Авг. 30, 2024

In the context of increasing frequency urban flooding disasters caused by extreme weather, accurate and timely identification monitoring flood risks have become increasingly important. This article begins with a bibliometric analysis literature on identification, revealing that since 2017, this area has global research hotspot. Subsequently, it presents systematic review current mainstream technologies, drawing from both traditional emerging data sources, which are categorized into sensor-based (including contact non-contact sensors) big data-based social media surveillance camera data). By analyzing advantages disadvantages each technology their different focuses, paper points out largely emphasizes more “intelligent” technologies. However, these technologies still certain limitations, sensor techniques retain significant in practical applications. Therefore, future risk should focus integrating multiple fully leveraging strengths sources to achieve real-time flooding.

Язык: Английский

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