Utilizing explainable AI makes it feasible to improve the readability and transparency of air quality studies, allowing stakeholders to comprehend and verify the predictions and suggestions made by AI systems. Rough woodland. To classify the data, XGBoost and KNN are used.
To improve the understanding of AI models employed for air quality analysis, researchers have investigated various machine learning interpretation techniques.
Mar 5, 2024 · Using methods based on eXplainable Artificial Intelligence (XAI) we found that air pollution (mainly and ) contribute the most to AD mortality prediction.
This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary ...
Aug 23, 2024 · The development of an explainable, high-precision model significantly improves understanding and trust in environmental assessment outcomes.
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Jun 17, 2024 · The application of XAI is critical in areas such as geohazard prediction and environmental monitoring, where understanding the basis of ...
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Jun 25, 2023 · Explainable AI (XAI) can assist us in acquiring insights into these constraints and, consequently, modifying the modeling approach and training ...
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Jun 17, 2024 · Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision- ...
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Overview of representative case studies evaluating XAI methods across three evaluation metrics, including quantitativeness (Q), anecdotal evidence (AE), and ...
May 6, 2024 · Explainable artificial intelligence (XAI) can address these concerns by providing interpretable models, transparent decision-making processes, ...