Flood risk assessment of Wuhan, China, using a multi-criteria analysis model with the improved AHP-Entropy method

Environmental Science and Pollution Research, Год журнала: 2023, Номер 30(42), С. 96001 - 96018

Опубликована: Авг. 10, 2023

Язык: Английский

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An integrated model chain for future flood risk prediction under land-use changes

Journal of Environmental Management, Год журнала: 2023, Номер 342, С. 118125 - 118125

Опубликована: Май 19, 2023

Язык: Английский

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A novel approach to flood risk assessment: Synergizing with geospatial based MCDM-AHP model, multicollinearity, and sensitivity analysis in the Lower Brahmaputra Floodplain, Assam

Journal of Cleaner Production, Год журнала: 2024, Номер 467, С. 142985 - 142985

Опубликована: Июнь 28, 2024

Язык: Английский

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Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

Remote Sensing, Год журнала: 2024, Номер 16(2), С. 350 - 350

Опубликована: Янв. 16, 2024

Coastal regions, increasingly threatened by floods due to climate-change-driven extreme weather, lack a comprehensive study that integrates coastal and riverine flood dynamics. In response this research gap, we conducted bibliometric analysis thorough visualization mapping of studies compound flooding risk in cities over the period 2014–2022, using VOSviewer CiteSpace analyze 407 publications Web Science Core Collection database. The analytical results reveal two persistent topics: way explore return periods or joint probabilities drivers statistical modeling, quantification with different through numerical simulation. This article examines critical causes flooding, outlines principal methodologies, details each method’s features, compares their strengths, limitations, uncertainties. paper advocates for an integrated approach encompassing climate change, ocean–land systems, topography, human activity, land use, hazard chains enhance our understanding mechanisms. includes adopting Earth system modeling framework holistic coupling components, merging process-based data-driven models, enhancing model grid resolution, refining dynamical frameworks, comparing complex physical models more straightforward methods, exploring advanced data assimilation, machine learning, quasi-real-time forecasting researchers emergency responders.

Язык: Английский

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Urban Flood Risk Analysis Using the SWAGU-Coupled Model and a Cloud-Enhanced Fuzzy Comprehensive Evaluation Method

Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106461 - 106461

Опубликована: Апрель 1, 2025

Язык: Английский

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Geospatial techniques for monitoring and mitigating climate change and its effects on human health

International Journal of Health Geographics, Год журнала: 2023, Номер 22(1)

Опубликована: Янв. 27, 2023

This article begins by briefly examining the multitude of ways in which climate and change affect human health wellbeing. It then proceeds to present a quick overview how geospatial data, methods tools are playing key roles measurement, analysis modelling its effects on health. Geospatial techniques proving indispensable for making more accurate assessments estimates, predicting future trends reliably, devising optimised adaptation mitigation plans.

Язык: Английский

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Flood hazards and risk mapping using geospatial technologies in Jimma City, southwestern Ethiopia

Heliyon, Год журнала: 2023, Номер 9(4), С. e14617 - e14617

Опубликована: Март 17, 2023

Cities in Ethiopia are suffering from unprecedented floods due to climate change and other anthropogenic activities. Failure include land use planning poorly designed urban drainage system aggravates the problem of flood. The integration geographic information system, multi-criteria evaluation (MCE) technique were used for flood hazards risk mapping. Five factors namely slope, elevation, density, cover, soil data Agrowing population increases victims during rainy season. Results revealed that about 25.16 24.38% study area is categorized under very high hazards, respectively. topographic nature hazards. increaseing number people living city has led conversion previously occupied green lands into residential areas risk. Flood mitigation measures such as better planning, public awareness creation on risks, delineation seasons, increasing greenery, strengthening river side development, watershed management catchment urgently required. findings this can provide a theoretical background prevention.

Язык: Английский

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Assessing the spatiotemporal impact of users’ exposure and vulnerability to flood risk in urban built environments

Sustainable Cities and Society, Год журнала: 2023, Номер 100, С. 105043 - 105043

Опубликована: Ноя. 6, 2023

Flood risk in an urban built environment depends on the combination of hazard, vulnerability itself and its infrastructure (referred to as physical vulnerability), exposure people residing, working or visiting it (i.e., their human condition). However, factors affecting those vary over space time depending uses environment. This research offers a methodology for combined spatiotemporal flood assessment, providing hourly variations risks due vulnerability, users' exposure, vulnerability. A mesoscale approach is adopted by collecting managing data each open layout (e.g., street, square) facing buildings. In particular, are investigated indoor outdoor temporalities, distributions density, age, familiarity with environment, direct floodwaters. Then, Analytical Hierarchy process used combine factors. Finally, application case study (an district Guimarães, Portugal) demonstrates how alter day within same element considers different elements which share hazard

Язык: Английский

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A multi-dimensional framework for improving flood risk assessment: Application in the Han River Basin, China

Journal of Hydrology Regional Studies, Год журнала: 2023, Номер 47, С. 101434 - 101434

Опубликована: Июнь 1, 2023

The Han River Basin, China As the water source of Middle Route Project South to North Water Transfer Project, security in Basin deeply impacts national resource allocation. This study constructed a Multi-Dimensional Flood Risk Assessment (MDFRA) framework combining comprehensive index system, an integrated weight method, and clustering algorithm assess flood risk Basin. We first system including eight hazard indexes, four exposure vulnerability indexes demographic, economic, ecological, infrastructural categories risk. weights were determined by which Shapley value Analytic Hierarchy Process method (SAHP). Finally, possibilistic fuzzy C-means was employed identify levels. MDFRA presents realistically comprehensively, revealing composition providing more detailed information. Results show that high-risk regions accounted for 34.51% basin area mainly concentrated mid-lower reaches, while middle-risk (19.13%) middle-high-risk (23.35%) distributed upstream. 4.52%, 5.12%, 13.37% assigned as very-low, low, middle-low respectively, adjacent Danjiangkou reservoir. Flood-prone natural conditions dense population assets causes high risk, reservoir regulation storage capacity had significantly alleviated

Язык: Английский

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Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods

Water Science & Technology, Год журнала: 2024, Номер 89(10), С. 2605 - 2624

Опубликована: Май 7, 2024

Floods are one of the most destructive disasters that cause loss life and property worldwide every year. In this study, aim was to find best-performing model in flood sensitivity assessment analyze key characteristic factors, spatial pattern evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Random Forest (RF). Suqian City Jiangsu Province selected as study area, a random sample dataset historical points constructed. Fifteen different meteorological, hydrological, geographical variables were considered assessment, 12 based on multi-collinearity study. Among results comparing ML models, RF method had highest AUC value, accuracy, comprehensive evaluation effect, is reliable effective risk model. As main output map divided into five categories, ranging from very low high sensitivity. Using (i.e., accuracy model), high-risk area covers about 44% mainly concentrated central, eastern, southern parts old city area.

Язык: Английский

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