Exploring the association between the settlement environment and residents’ positive sentiments in urban villages and formal settlements in Shenzhen

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 98, P. 104851 - 104851

Published: Aug. 6, 2023

Language: Английский

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Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 631, P. 130841 - 130841

Published: Feb. 5, 2024

Constrained by the sparsity of observational streamflow data, large-scale catchments face pressing challenges in prediction and flood management amid climate change. Deep learning excels simulation performance while flow lag information data-driven approaches is barely highlighted. In this study, we introduce a time-lag informed deep framework for catchments. Central to utilization between upstream downstream subbasins, enabling precise forecasting at outlet driven data. Taking monsoon-influenced Dulong-Irrawaddy River Basin (DIRB) as study area, determined peak (PFL) days relative annual scale (RAFS) defined subbasins. By incorporating with historical data different time intervals, developed optimal model DIRB. This was then applied evaluate processes 2008 2009, using selected indicators. The results indicate that led significant improvements, notably LSTM_PFL_RAFS Hkamti sub-basin which achieved Kling-Gupta Efficiency (KGE) 0.891 (Nash-Sutcliffe efficiency coefficient, NSE, 0.904), surpassing LSTM's 0.683 (NSE, 0.785). Further integration specific interval, model, H(15)_PFL utilizes reached an impressive KGE 0.948 0.940). outperformed standard LSTM accurately simulating key characteristics, including flows, initiation times, durations 2009 events. Notably, provides valuable 15-day lead forecasting, extending window emergency response preparations. Future research incorporates additional essential catchment features into holds great potential unraveling complex mechanisms hydrological responses human activities

Language: Английский

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A review of flood risk assessment frameworks and the development of hierarchical structures for risk components

Discover Water, Journal Year: 2025, Volume and Issue: 5(1)

Published: Feb. 12, 2025

Language: Английский

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A novel integrated urban flood risk assessment approach based on one-two dimensional coupled hydrodynamic model and improved projection pursuit method

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 366, P. 121910 - 121910

Published: July 24, 2024

Language: Английский

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An interpretive model to assess the barriers to ocean energy toward blue economic development in India

Renewable Energy, Journal Year: 2023, Volume and Issue: 211, P. 822 - 830

Published: May 9, 2023

Language: Английский

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Study on multiscale-multivariate prediction and risk assessment of urban flood

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 173, P. 105958 - 105958

Published: Jan. 13, 2024

Language: Английский

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A comparative study on urban waterlogging susceptibility assessment based on multiple data-driven models

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 360, P. 121166 - 121166

Published: May 22, 2024

Language: Английский

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The Risk Analysis of Cart Development Based on Dynamic Bayesian Networks

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

To address the issues of multiple uncertainties, complex structures, and unpredictability during development trolley, this paper proposes a risk analysis method for trolley based on dynamic Bayesian networks. First, extensive relevant literature applying rough set reduction theory optimization, factor checklist with 5 primary indicators 16 secondary is constructed. Next, network model established by introducing time dimension. Fuzzy expert scoring are used to quantify probabilities nodes, Leaky Noisy-or Gate expansion applied correct conditional probabilities. Finally, performed using bidirectional inference function network. The time-series variation curve obtained through case analysis. By reverse reasoning, key factors occurrence identified, corresponding response strategies proposed. research results provide new approach analyzing effectively controlling risks associated development.

Language: Английский

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Risk Analysis of Service Slope Hazards for Highways in the Mountains Based on ISM-BN

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 2975 - 2975

Published: March 10, 2025

To effectively mitigate service slope disaster risks in mountainous areas and enhance the overall safety of highway operations, based on geological structural characteristics slopes, considering technical conditions, stability, potential consequences, 25 important influencing factors are systematically identified. The identification process integrates insights from relevant literature, expert opinions, historical maintenance records such slopes. An integrated approach combining Interpretive Structural Modeling (ISM) Bayesian Networks (BNs) is utilized to conduct a quantitative analysis interrelationships impact strength risk aim reveal causal mechanism provide scientific basis for assessment prevention strategies. Firstly, relationship matrix constructed prior knowledge. Then, reachability computed partitioned into different levels form directed graph which network structure constructed. Subsequently, expert’s subjective judgment further transformed set conditional probabilities embedded BN perform inference predict probability occurrence. Real-time diagnosis triggers operating slopes using backward reasoning, sensitivity analysis, influence capabilities. As an example, earth excavation area Anhui Province analyzed established model. results showed that failure model highways has good applicability, possibility medium high with 34%, where engineering micro-topographic landforms, lowest monthly average temperature main hazard them. study not only helps deepen understanding evolutionary mechanisms but also provides theoretical support practical guidance safe operation highways. offers clear information, serving as managing risks. Consequently, it reduces likelihood disasters enhances operation.

Language: Английский

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Flood resilience assessment of region based on TOPSIS-BOA-RF integrated model

Ecological Indicators, Journal Year: 2024, Volume and Issue: 169, P. 112901 - 112901

Published: Dec. 1, 2024

Language: Английский

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