XAI-driven assessment of urban circular carbon economy: Using China's pilot cities as a case study

Urban Climate, Journal Year: 2025, Volume and Issue: 61, P. 102407 - 102407

Published: April 18, 2025

Language: Английский

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Sensitivity analysis of indicator weights for the construction of flood vulnerability indexes: A participatory approach

Frontiers in Water, Journal Year: 2023, Volume and Issue: 5

Published: Feb. 6, 2023

The assessment of flood vulnerability is a complex task that involves numerous uncertainties. Within this context, sensitivity analyses are crucial to better understand the variability index outcomes according different input parameters. present study sheds light on importance assessing criteria weights construct indexes using Maquiné basin (Brazil) as case study. Specifically, we compared scores based derived from participatory survey with 44 stakeholders those an equal weighting scheme. Results helped us identify areas low and high uncertainty variables contributing this. Overall, preference for indicator did not vary significantly among distinct socioeconomic characteristics. Furthermore, choice only had impact spatial distribution in certain regions. Compared weights, obtained by averaging stakeholder scenarios were similar, indicating results robust highly sensitive weights. By adopting approach, able consider multiple stakeholders' views, which provide more comprehensive perspective potentially increased acceptance results. Based our findings, end-users can relative each how they contribute vulnerability. help points where disagree, be used facilitate dialogue consensus building. methodology applied straightforward could easily adapted other multi-criteria decision-making problems.

Language: Английский

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Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(18), P. 3374 - 3374

Published: Sept. 11, 2024

With the increase in climate-change-related hazardous events alongside population concentration urban centres, it is important to provide resilient cities with tools for understanding and eventually preparing such events. Machine learning (ML) deep (DL) techniques have increasingly been employed model susceptibility of This study consists a systematic review ML/DL applied air pollution, heat islands, floods, landslides, aim providing comprehensive source reference both modelling approaches. A total 1454 articles published between 2020 2023 were systematically selected from Scopus Web Science search engines based on queries selection criteria. extracted categorised using ad hoc classification. Consequently, general approach was consolidated, covering data preprocessing, feature selection, modelling, interpretation, map validation, along examples related global/continental data. The most frequently across various hazards include random forest, artificial neural networks, support vector machines. also provides, per hazard, definition, requirements, insights into used, including state-of-the-art novel

Language: Английский

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Predicting Accident Outcomes in Cross-Border Pipeline Construction Projects Using Machine Learning Algorithms

Arabian Journal for Science and Engineering, Journal Year: 2023, Volume and Issue: 48(10), P. 13771 - 13789

Published: June 17, 2023

Language: Английский

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Investigation of Landslide Susceptibility Decision Mechanisms in Different Ensemble-Based Machine Learning Models with Various Types of Factor Data

Sustainability, Journal Year: 2023, Volume and Issue: 15(18), P. 13563 - 13563

Published: Sept. 11, 2023

Machine learning (ML)-based methods of landslide susceptibility assessment primarily focus on two dimensions: accuracy and complexity. The complexity is not only influenced by specific model frameworks but also the type modeling data. Therefore, considering impact factor data types model’s decision-making mechanism holds significant importance in assessing regional characteristics conducting risk warnings given achievement good predictive performance for using excellent ML methods. models coupled with different machine was explained this study utilizing Shapley Additive exPlanations (SHAP) method. Furthermore, a comparative analysis carried out to examine differential effects diverse identical factors predictions. area selected Cenxi, Guangxi, where geographic spatial database constructed combining 23 conditioning 214 samples from region. Initially, were standardized five conditional probability models, frequency ratio (FR), information value (IV), certainty (CF), evidential belief function (EBF), weights evidence (WOE), based arrangement landslides. This led formation six databases initial Subsequently, ensemble-based methods, random forest (RF) XGBoost, utilized build predicting susceptibility. Various evaluation metrics employed compare capabilities determined optimal model. Simultaneously, conducted interpretable SHAP method intrinsic mechanisms explaining comparing impacts prediction results. results illustrated that XGBoost-CF CF values exhibited best stability yielded more reasonable zoning, thus identified as global interpretation revealed slope most crucial influencing landslides, its interaction other collectively contributed occurrences. differences internal same manifested extent influence dependency factors, providing an explanation reasons behind higher Through comprehensive local analyzing sample characteristics, errors can be summarized, thereby reference framework constructing accurate rational facilitating warning management.

Language: Английский

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