Geospatial Mapping and Meteorological Flood Risk Assessment: A Global Research Trend Analysis DOI Creative Commons

Phila Sibandze,

Ahmed Mukalazi Kalumba,

Amal H. Aljaddani

et al.

Environmental Management, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 12, 2024

Abstract Flooding is a global threat causing significant economic and environmental damage, necessitating policy response collaborative strategy. This study assessed research trends advances in geospatial meteorological flood risk assessment (G_MFRA), considering the ongoing debate on management adaptation strategies. A total of 1872 original articles were downloaded BibTex format using Web Science (WOS) Scopus databases to retrieve G_MFRA studies published from 1985 2023. The annual growth rate 15.48% implies that field has been increasing over time during period. analysis practice highlights key themes, methodologies, emerging directions. There exists notable gap data methodologies for between developed developing countries, particularly Africa South America, highlighting urgency coordinated efforts cohesive actions. challenges identified body extant literature include technical expertise, complex communication networks, resource constraints associated with application gaps methodologies. advocates holistic approach disaster through ecosystem-based underpins Sustainable Development Goals develop innovative techniques models potential influence decision-making domain. Addressing these requires networked partnership community, institutions, countries.

Language: Английский

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management DOI Open Access
Vijendra Kumar, Hazi Mohammad Azamathulla, Kul Vaibhav Sharma

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(13), P. 10543 - 10543

Published: July 4, 2023

Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts control essential to lessen these effects safeguard populations. By utilizing its capacity handle massive amounts of data provide accurate forecasts, deep learning has emerged as potent tool for improving prediction control. The current state applications in forecasting management is thoroughly reviewed this work. review discusses variety subjects, such the sources utilized, models used, assessment measures adopted judge their efficacy. It assesses approaches critically points out advantages disadvantages. article also examines challenges with accessibility, interpretability models, ethical considerations prediction. report describes potential directions deep-learning research enhance predictions Incorporating uncertainty estimates into integrating many sources, developing hybrid mix other methodologies, enhancing few these. These goals can help become more precise effective, which will result better plans forecasts. Overall, useful resource academics professionals working on topic management. reviewing art, emphasizing difficulties, outlining areas future study, it lays solid basis. Communities prepare destructive floods by implementing cutting-edge algorithms, thereby protecting people infrastructure.

Language: Английский

Citations

105
A novel flood risk management approach based on future climate and land use change scenarios DOI
Huu Duy Nguyen, Quoc‐Huy Nguyen, Dinh Kha Dang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 921, P. 171204 - 171204

Published: Feb. 23, 2024

Language: Английский

Citations

25
Building green infrastructure for mitigating urban flood risk in Beijing, China DOI
Zehao Wang, Zhihui Li, Yifei Wang

et al.

Urban forestry & urban greening, Journal Year: 2024, Volume and Issue: 93, P. 128218 - 128218

Published: Jan. 20, 2024

Language: Английский

Citations

21
Flood risk evaluation of the coastal city by the EWM-TOPSIS and machine learning hybrid method DOI
Ziyuan Luo, Jian Tian, Jian Zeng

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: 106, P. 104435 - 104435

Published: March 28, 2024

Language: Английский

Citations

19
A review of flood risk assessment frameworks and the development of hierarchical structures for risk components DOI Creative Commons

Nazgol Tabasi,

Mohammad Fereshtehpour, Bardia Roghani

et al.

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

Published: Feb. 12, 2025

Language: Английский

Citations

2
Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India DOI Creative Commons
Rajib Mitra, Piu Saha, Jayanta Das

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2022, Volume and Issue: 13(1), P. 2183 - 2226

Published: Aug. 19, 2022

Floods have received global significance in contemporary times due to their destructive behavior, which may wreak tremendous ruin on infrastructure and civilization. The present research employed an integration of the Geographic information system (GIS) Analytical Hierarchy Process (AHP) method for identifying flood susceptibility zonation (FSZ), vulnerability (FVZ), risk (FRZ) humid subtropical Uttar Dinajpur district India. study combined a large number thematic layers (N = 12 FSZ N 9 FVZ) achieve reliable accuracy included multicollinearity analysis these variables overcome issues related highly correlated variables. According findings, 27.04, 15.62, 4.59% area were classified as medium, high, very high FRZ, respectively. ROC-AUC, MAE, MSE, RMSE model exhibited good prediction 0.73, 0.15, 0.16, 0.21, performance AHP has been evaluated using sensitivity analyses. It also recommends that persistent improvement this subject, such studies modifying criteria thresholds, changing relative criteria, desired matrix, will permit GIS MCDA be progressively adapted real hazard-management issues.

Language: Английский

Citations

65
Review on Urban Flood Risk Assessment DOI Open Access
Cailin Li, Na Sun,

Yihui Lu

et al.

Sustainability, Journal Year: 2022, Volume and Issue: 15(1), P. 765 - 765

Published: Dec. 31, 2022

Under the background of rapid urban development and continuous climate change, frequent floods around world have caused serious economic losses social problems, which has become main reason for sustainable cities. Flood disaster risk assessment is an important non-engineering measure in prevention mitigation, scientific flood premise foundation management. This paper summarizes current situation by analyzing international literature recent 20 years. The mechanism mainly discussed. methods are summarized, including historical statistics method, multi-criteria index system remote sensing GIS (Geographic Information System) coupling scenario simulation evaluation method machine learning method. Furthermore, status analysis forecasting summarized. Finally, trend direction put forward.

Language: Английский

Citations

50
A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction DOI Creative Commons
Mohammed Sarfaraz Gani Adnan, Zakaria Shams Siam, Irfat Kabir

et al.

Journal of Environmental Management, Journal Year: 2022, Volume and Issue: 326, P. 116813 - 116813

Published: Nov. 23, 2022

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate predictions, the outcomes are subject to uncertainty since (FSMs) may varying spatial predictions. However, there not attempts address these uncertainties because identifying agreement projections is a complex process. This study presents framework for reducing disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining models. The southwest coastal region Bangladesh selected as case area. A comparable percentage potential area (approximately 60% total land areas) produced by all Despite achieving high prediction accuracy, discrepancy observed, with pixel-wise correlation coefficients across different ranging from 0.62 0.91. exhibited accuracy improved number classification errors. presented this might aid formulation risk-based development plans enhancement current early warning systems.

Language: Английский

Citations

44
Development of a new integrated flood resilience model using machine learning with GIS-based multi-criteria decision analysis DOI
Muhammad Hussain, Muhammad Tayyab, Kashif Ullah

et al.

Urban Climate, Journal Year: 2023, Volume and Issue: 50, P. 101589 - 101589

Published: June 26, 2023

Language: Английский

Citations

34
Urban flood risk assessment under rapid urbanization in Zhengzhou City, China DOI Creative Commons
Guoyi Li, Jiahong Liu, Weiwei Shao

et al.

Regional Sustainability, Journal Year: 2023, Volume and Issue: 4(3), P. 332 - 348

Published: Sept. 1, 2023

With accelerated urbanization and climate change, urban flooding is becoming more serious. Flood risk assessment an important task for flood management, so it crucial to map the spatial temporal distribution of risk. This paper proposed method that takes into account influences hazard, vulnerability, exposure, by constructing a multi-index framework based on Geographic Information System (GIS). To determine weight values index factors, we used analytic hierarchy process (AHP). Also, plotted maps in Zhengzhou City 2000, 2005, 2010, 2015, 2020. The analysis results showed that, proportion very high zone was 1.362%, 5.270%, 4.936%, 12.151%, 24.236% 2020, respectively. It observed area zones trend increasing expanding, which Dengfeng City, Xinzheng Xinmi Zhongmu County had fastest growth rate most obvious increase. has been expanding with development urbanization. adapted will have good adaptability other research areas, its can provide scientific reference management personnel. In future, accuracy be further improved promoting basic data reasonably determining factors. zoning better reflect basis early warning prevention drainage.

Language: Английский

Citations

31