A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions

Land, Год журнала: 2023, Номер 12(8), С. 1514 - 1514

Опубликована: Июль 29, 2023

Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster systems, methods, obstacles, potential future paths. Specifically, it focuses on flood control, significant recurrent category natural disasters. The analysis begins by exploring types catastrophes, including earthquakes, wildfires, floods. It then delves into the different domains collectively contribute effective management. These encompass cutting-edge technologies such as big data cloud computing, providing scalable reliable infrastructure for storage, processing, analysis. study investigates Internet Things sensor networks gather real-time from flood-prone areas, enhancing situational awareness enabling prompt actions. Model-driven engineering examined its utility in developing modeling scenarios, aiding preparation response planning. includes Google Earth engine (GEE) examines previous studies involving GEE. Moreover, we discuss remote sensing; sensing undoubtedly valuable tool management, geographical situations. We explore application Geographical Information System (GIS) Spatial Data Management visualizing analyzing spatial facilitating informed decision-making resource allocation during In final section, focus shifts utilization machine learning analytics methodologies offer predictive models data-driven insights, early warning risk assessment, mitigation strategies. Through this analysis, significance incorporating these spheres control procedures highlighted, with aim improving resilience regions. paper addresses existing challenges provides research directions, ultimately striving clearer more coherent representation techniques.

Язык: Английский

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A novel framework for urban flood risk assessment: Multiple perspectives and causal analysis

Water Research, Год журнала: 2024, Номер 256, С. 121591 - 121591

Опубликована: Апрель 8, 2024

Язык: Английский

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The local coupling and telecoupling of urbanization and ecological environment quality based on multisource remote sensing data

Journal of Environmental Management, Год журнала: 2022, Номер 327, С. 116921 - 116921

Опубликована: Дек. 1, 2022

Язык: Английский

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Flood susceptibility prediction using tree-based machine learning models in the GBA

Sustainable Cities and Society, Год журнала: 2023, Номер 97, С. 104744 - 104744

Опубликована: Июнь 25, 2023

Язык: Английский

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A comparative analysis on flood risk assessment and management performances between Beijing and Munich

Environmental Impact Assessment Review, Год журнала: 2023, Номер 104, С. 107319 - 107319

Опубликована: Окт. 12, 2023

Язык: Английский

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A synergistic approach towards understanding flood risks over coastal multi-hazard environments: Appraisal of bivariate flood risk mapping through flood hazard, and socio-economic-cum-physical vulnerability dimensions

The Science of The Total Environment, Год журнала: 2023, Номер 901, С. 166423 - 166423

Опубликована: Авг. 21, 2023

Язык: Английский

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Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(35), С. 48497 - 48522

Опубликована: Июль 20, 2024

Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due climate change exacerbating extreme weather events robust flood modeling crucial support disaster resilience adaptation. This study uses multi-sourced geospatial datasets develop an advanced machine learning framework for assessment in the Arambag region of West Bengal, India. The inventory was constructed through Sentinel-1 SAR analysis global databases. Fifteen conditioning factors related topography, land cover, soil, rainfall, proximity, demographics were incorporated. Rigorous training testing diverse models, including RF, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, MARS algorithms, undertaken categorical mapping. Model optimization achieved statistical feature selection techniques. Accuracy metrics model interpretability methods like SHAP Boruta implemented evaluate predictive performance. According area under receiver operating characteristic curve (AUC), prediction accuracy models performed around > 80%. RF achieves AUC 0.847 at resampling factor 5, indicating strong discriminative AdaBoost also consistently exhibits good ability, with values 0.839 10. indicated precipitation elevation as most significantly contributing area. Most pointed out southern portions highly susceptible areas. On average, from 17.2 18.6% hazards. In analysis, various nature-inspired algorithms identified selected input parameters assessment, i.e., elevation, precipitation, distance rivers, TWI, geomorphology, lithology, TRI, slope, soil type, curvature, NDVI, roads, gMIS. As per analyses, it found that rivers play roles decision-making process assessment. results majority building footprints (15.27%) are high very risk, followed by those low risk (43.80%), (24.30%), moderate (16.63%). Similarly, cropland affected flooding this categorized into five classes: (16.85%), (17.28%), (16.07%), (16.51%), (33.29%). However, interdisciplinary contributes towards hydraulic hydrological management.

Язык: Английский

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Exploring the fidelity of satellite precipitation products in capturing flood risks: A novel framework incorporating hazard and vulnerability dimensions over a sensitive coastal multi-hazard catchment

The Science of The Total Environment, Год журнала: 2024, Номер 920, С. 170884 - 170884

Опубликована: Фев. 9, 2024

Язык: Английский

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Port's industry ecosystem construction: Empirical evidence from China

Transport Policy, Год журнала: 2025, Номер 163, С. 42 - 60

Опубликована: Янв. 6, 2025

Язык: Английский

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Output characteristics and driving factors of non-point source nitrogen (N) and phosphorus (P) in the Three Gorges reservoir area (TGRA) based on migration process: 1995–2020

The Science of The Total Environment, Год журнала: 2023, Номер 875, С. 162543 - 162543

Опубликована: Март 5, 2023

Язык: Английский

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