Enhancing urban flood resilience: A coupling coordinated evaluation and geographical factor analysis under SES-PSR framework

International Journal of Disaster Risk Reduction, Год журнала: 2024, Номер 101, С. 104243 - 104243

Опубликована: Янв. 3, 2024

Urban flooding has emerged as a significant urban issue in cities worldwide, with China being particularly affected. To effectively manage and mitigate floods, holistic examination of the interaction between subsystems is required to improve flood resilience. However, interactions mechanisms under disaster haven't been addressed adequately previous studies. Therefore, this paper established conceptual framework for illustrating natural-ecological social-economic subsystem considering pressure, state, response within cycle. The objective investigate coupling coordination degree (CCD) these identify driving factors geographical detector model, Yangtze River Delta are selected an empirical example. findings reveal overall upward trend towards whole area notable variability among cities. resilience state dimension emerges crucial aspect determining CCD area. Key coordinated development identified air pollution, global warming, technological innovation, governance power, financial strength, urbanization. Based on factors, presents potential implications that can serve effective guidance offer insights policymakers, planners, researchers their efforts enhance sustainable future.

Язык: Английский

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Urban waterlogging susceptibility assessment based on hybrid ensemble machine learning models: A case study in the metropolitan area in Beijing, China

Journal of Hydrology, Год журнала: 2024, Номер 630, С. 130695 - 130695

Опубликована: Янв. 23, 2024

Язык: Английский

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Resilience assessment of subway system to waterlogging disaster

Sustainable Cities and Society, Год журнала: 2024, Номер 113, С. 105710 - 105710

Опубликована: Июль 26, 2024

Язык: Английский

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Analysis of mechanism and optimal value of urban built environment resilience in response to stormwater flooding

Ecological Indicators, Год журнала: 2024, Номер 158, С. 111625 - 111625

Опубликована: Янв. 1, 2024

The concept of urban resilience focuses on understanding the process and mechanisms disaster occurrence, providing innovative approaches to address stormwater flooding. However, existing studies primarily concentrate enhancing overall system resilience, with limited research examining temporal progression from disturbance flood generation. To fill this gap, study categorizes development flooding into three periods: resistance (DR), adjustment adaptation (AA), rapid recovery (RR). Using SWMM (Storm Water Management Model) software, 27 representative parcels in Beijing-Tianjin-Hebei region China were simulated. By sequentially considering single-indicator control variables, indicators that significantly impact periods identified through construction a indicator library. Subsequently, models for each phase constructed using BP (Back Propagation) neural network, genetic algorithms employed optimize determine optimal values period. Finally, findings summarized design method built environment flooding, accompanied by guide improving resilience. reveals following key findings: (1) influence physical spatial elements formation varies across different stages process; (2) distinct operate at times ways throughout entire (3) does not necessarily require setting specific threshold influencing indicator; instead, an single value emerges when multiple interact. Moreover, interact, combination module best exists. This investigates complete cycle storm formation, offering both solutions cities mitigate disasters advancing theoretical while fostering interdisciplinary integration.

Язык: Английский

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Identification of urban waterlogging indicators and risk assessment based on MaxEnt Model: A case study of Tianjin Downtown

Ecological Indicators, Год журнала: 2023, Номер 158, С. 111354 - 111354

Опубликована: Дек. 2, 2023

Waterlogging is one of the world's most dangerous climatic hazards, seriously limiting safety and sustainable development cities. Analysis factors influencing urban waterlogging disaster risk assessment are great importance for prevention control waterlogging. The study constructs a framework assessing Urban Risk (UWR) from four dimensions: natural condition, social capital, infrastructure built environment, emphasizing need to understand address vulnerabilities risks faced by cities in terms On this basis, MaxEnt model used rank contribution rate indicators identify positive negative correlations, as well assess waterlogging-prone areas, taking Tianjin Downtown research object. results show that: (1) has strong applicability assessment. (2) elements with highest impact on population density, impervious surface, precipitation, etc., prove that regional increases increase population, building surface. (3) high-risk area concentrated central part city, which characterized high degree construction dense population; low-risk mainly low at edge large green or water bodies. In study, comprehensive UWR was developed, key affecting spatial distribution were identified, perform accuracy. can provide theoretical reference warning land use optimization.

Язык: Английский

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Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Remote Sensing, Год журнала: 2023, Номер 15(10), С. 2562 - 2562

Опубликована: Май 14, 2023

Urban impervious surface (UIS) is a key parameter in climate change, environmental and sustainability. UIS extraction has been evolving rapidly the past decades. However, high-resolution mapping long-term need. There an urgent requirement for from remote sensing imagery. In this paper, we compare current methods terms of units models summarize their strengths limitations. We discuss challenges estimation high spatial resolution imagery selection resolution, spectral band, method. The uncertainties caused by clouds snow, shadows, vegetation occlusion are also analyzed. Automated sample labeling domain knowledge main directions using deep learning methods. should focus on continuous time series multi-source satellite dynamic monitoring surfaces.

Язык: Английский

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Cloud model assessment of urban flood resilience based on PSR model and game theory

International Journal of Disaster Risk Reduction, Год журнала: 2023, Номер 97, С. 104050 - 104050

Опубликована: Окт. 1, 2023

Язык: Английский

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A comprehensive framework for assessing the spatial drivers of flood disasters using an optimal Parameter-based geographical Detector–machine learning coupled model

Geoscience Frontiers, Год журнала: 2024, Номер 15(6), С. 101889 - 101889

Опубликована: Июль 11, 2024

Flood disasters pose serious threats to human life and property worldwide. Exploring the spatial drivers of flood on a macroscopic scale is great significance for mitigating their impacts. This study proposes comprehensive framework integrating driving-factor optimization interpretability, while considering heterogeneity. In this framework, Optimal Parameter-based Geographic Detector (OPGD), Recursive Feature Estimation (RFE), Light Gradient Boosting Machine (LGBM) models were utilized construct OPGD–RFE–LGBM coupled model identify essential driving factors simulate distribution disasters. The SHapley Additive ExPlanation (SHAP) interpreter was employed quantitatively explain mechanisms behind Yunnan Province, typical mountainous plateau area in Southwest China, selected implement proposed conduct case study. For purpose, disaster inventory 7332 historical events prepared, 22 potential related precipitation, surface environment, activity initially selected. Results revealed that Province exhibit high heterogeneity, with geomorphic zoning accounting 66.1% variation offers clear advantages over single LGBM identifying analyzing Moreover, simulation performance shows slight improvement (a 6% average decrease RMSE an increase 1% R2) even reduced factor data. Factor explanatory analysis indicated combination sets varied across different subregions; nevertheless, precipitation-related factors, such as precipitation intensity index (SDII), wet days (R10MM), 5-day maximum (RX5day), main controlling provides quantitative analytical at large scales significant offering reference management authorities developing macro-strategies prevention.

Язык: Английский

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A novel ensemble learning approach to extract urban impervious surface based on machine learning algorithms using SAR and optical data

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 132, С. 104013 - 104013

Опубликована: Июль 8, 2024

Accurate urban impervious surface (UIS) extraction from open-source remote sensing data remains challenging, especially for cities with heterogeneous climatic backgrounds. Contemporary, state-of-the-art techniques achieve promising results at a global scale, but accuracy is compromised the city level. Therefore, ensemble machine learning approach using Optical-SAR datasets was implemented to enhance of UIS mapping. Initially, we integrated optical and radar modified indices generate input features. Then, applied four algorithms, including AdaBoost, Gradient Boost (GB), Extreme Boosting (XGBoost), Random Forest (RF), fine-tuned them via soft voting approach. The optimized UISEM showed model 98%. method achieved classification 92% consistently performed across 32 globally zones. Regarding predictive power, XGB classifier outperformed other ML classifiers in mapping UIS. Furthermore, comparative analysis against three well-known (ESA World Cover, ESRI Land Dynamic World) also performed. proposed renowned accuracy, followed by DW 83%, ESA 86%, 82%. In future, developing spatial–temporal version can support diverse applications globally. (GEE Python) codes are available https://github.com/mnasarahmad/UISEM.

Язык: Английский

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Resilience evaluation research on coping with rainstorm inundation under the perspective of full cycle: A case study of Beijing-Tianjin-Hebei region

Sustainable Cities and Society, Год журнала: 2024, Номер 108, С. 105474 - 105474

Опубликована: Май 3, 2024

Язык: Английский

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