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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Reimagining multi-criterion decision making by data-driven methods based on machine learning: A literature review

Information Fusion, Год журнала: 2023, Номер 100, С. 101970 - 101970

Опубликована: Авг. 3, 2023

Язык: Английский

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Sustainability benefits of AI-based engineering solutions for infrastructure resilience in arid regions against extreme rainfall events

Discover Sustainability, Год журнала: 2024, Номер 5(1)

Опубликована: Сен. 18, 2024

Язык: Английский

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Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Approach to Optimize the Artificial Intelligence (AI) Factors Influencing Cost Management in Civil Engineering

Information, Год журнала: 2024, Номер 15(5), С. 280 - 280

Опубликована: Май 14, 2024

This research paper presents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using multi-criteria decision-making (MCDM) approach. The problem addressed revolves around need to effectively manage costs endeavors amidst growing complexity of and increasing integration AI technologies. methodology employed involves utilization three MCDM tools, specifically Delphi, interpretive structural modeling (ISM), Cross-Impact Matrix Multiplication Applied Classification (MICMAC). A total 17 factors, categorized into eight broad groups, were identified analyzed. Through application different techniques, relative importance interrelationships among these determined. key findings reveal role certain such as risk mitigation components, processes. Moreover, hierarchical structure generated through ISM influential via MICMAC provide insights for prioritizing strategic interventions. implications this extend informing decision-makers domain about effective strategies leveraging their practices. By adopting systematic approach, stakeholders can enhance project outcomes while resource allocation mitigating financial risks.

Язык: Английский

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Modelling of Flood Hazard Early Warning Group Decision Support System

Civil Engineering Journal, Год журнала: 2024, Номер 10(2), С. 614 - 627

Опубликована: Фев. 1, 2024

Early warning of flood hazards needs to be carried out comprehensively avoid a higher risk disaster. Every decision on early hazard is in part by one party, namely the government or water resource managers. This research aims provide collaborative decision-making model for through Group Decision Support System Model (GDSS), especially Indonesia. The novelty this that GDSS involves more than decision-maker and multi-criteria downstream Kali Sadar River, Mojokerto Regency, East Java Province, was developed using hybrid method, Analytical Network Process (ANP) VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). There result; voting BORDA method produce decision. test results were obtained Spearman rank correlation coefficient 0.8425 matrix confusion, an accuracy value 86.7%, precision recall f-measure 86.7%. Based results, good from model. Doi: 10.28991/CEJ-2024-010-02-018 Full Text: PDF

Язык: Английский

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Enhancing emergency decision-making with knowledge graphs and large language models

International Journal of Disaster Risk Reduction, Год журнала: 2024, Номер unknown, С. 104804 - 104804

Опубликована: Сен. 1, 2024

Язык: Английский

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Enhancing Flood Risk Mitigation by Advanced Data-Driven Approach

Heliyon, Год журнала: 2024, Номер 10(18), С. e37758 - e37758

Опубликована: Сен. 1, 2024

Язык: Английский

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Harnessing AHP and Fuzzy Scenarios for Resilient Flood Management in Arid Environments: Challenges and Pathways Toward Sustainability

Water, Год журнала: 2025, Номер 17(9), С. 1276 - 1276

Опубликована: Апрель 25, 2025

Flash floods rank among the most devastating natural hazards, causing widespread socio-economic, environmental, and infrastructural damage globally. Hence, innovative management approaches are required to mitigate their increasing frequency intensity, driven by factors such as climate change urbanization. Accordingly, this study introduced an integrated flood assessment approach (IFAA) for sustainable of risks integrating analytical hierarchy process-weighted linear combination (AHP-WLC) fuzzy-ordered weighted averaging (FOWA) methods. The IFAA was applied in South Khorasan Province, Iran, arid flood-prone region. Fifteen controlling factors, including rainfall (RF), slope (SL), land use/land cover (LU/LC), distance rivers (DTR), were processed using collected data. AHP-WLC method classified region into susceptibility zones: very low (10.23%), (23.14%), moderate (29.61%), high (17.54%), (19.48%). FOWA technique ensured these findings introducing optimistic pessimistic fuzzy scenarios risk. extreme scenario indicated that 98.79% area highly sensitive flooding, while less than 5% deemed low-risk under conservative scenarios. Validation demonstrated its reliability, with achieving curve (AUC) 0.83 average accuracy ~75% across all Findings revealed elevated dangers densely populated industrialized areas, particularly northern southern regions, which influenced proximity rivers. Therefore, also addressed challenges linked development goals (SDGs), SDG 13 (climate action), proposing adaptive strategies meet 60% targets. This research can offer a scalable framework risk management, providing actionable insights hydrologically vulnerable regions worldwide.

Язык: Английский

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Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

Ecological Indicators, Год журнала: 2023, Номер 153, С. 110457 - 110457

Опубликована: Июнь 15, 2023

This paper presents a novel framework for smart integrated risk management in arid regions. The combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, analysis, and decision-making modules to enhance community resilience. Flash is simulated by using Watershed Modelling System (WMS). Statistical methods are also used trim outlier data from physical systems climatic data. Furthermore, three AI including Support Vector Machine (SVM), Artificial Neural Network (ANN), Nearest Neighbours Classification (NNC), predict classify occurrences. Geographic Information (GIS) utilised assess potential risks vulnerable regions, together with Failure Mode Effects Analysis (FMEA) Hazard Operability Study (HAZOP) methods. module employs the Classic Delphi technique appropriate solutions control. methodology demonstrated its application real case study of Khosf region Iran, which suffers both drought severe floods simultaneously, exacerbated recent climate changes. results show high Coefficient determination (R2) scores SVM at 0.88, ANN 0.79, NNC 0.89. FMEA indicate that over 50% scenarios risk, while HAZOP indicates 30% same rate. Additionally, peak flows 24 m3/s considered occurrences can cause financial damage all techniques study. Finally, our research findings practical decision support system compatible sustainable development concepts resilience

Язык: Английский

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Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin

Water, Год журнала: 2024, Номер 16(2), С. 208 - 208

Опубликована: Янв. 6, 2024

Predicting monthly streamflow is essential for hydrological analysis and water resource management. Recent advancements in deep learning, particularly long short-term memory (LSTM) recurrent neural networks (RNN), exhibit extraordinary efficacy forecasting. This study employs RNN LSTM to construct data-driven forecasting models. Sensitivity analysis, utilizing the of variance (ANOVA) method, also crucial model refinement identification critical variables. covers data from 1979 2014, employing five distinct structures ascertain most optimal configuration. Application models Zarrine River basin northwest Iran, a major sub-basin Lake Urmia, demonstrates superior accuracy algorithm over LSTM. At outlet basin, quantitative evaluations demonstrate that outperforms across all structures. The S3 model, characterized by its inclusion input variable values four-month delay, exhibits notably exceptional performance this aspect. measures applicable particular context were RMSE (22.8), R2 (0.84), NSE (0.8). highlights River’s substantial impact on variations Urmia’s level. Furthermore, ANOVA method discerning relevance factors. underscores key role station streamflow, upstream maximum temperature influencing model’s output. Notably, surpassing traditional artificial network (ANN) models, excels accurately mimicking rainfall–runoff processes. emphasizes potential filter redundant information, distinguishing them as valuable tools

Язык: Английский

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