Скачать или смотреть Python Machine Learning - Integrating Decision Tools for Environmental Impact - ClickMyProject

Environmental impact reduction is crucial for sustainable urban planning, as it mitigates the harmful effects of urbanization on ecosystems, climate, and public health. As cities expand, minimizing environmental degradation through well-planned strategies becomes essential to ensuring long-term sustainability. This paper integrates T-Spherical Fuzzy Sets (T-SFS) to address the inherent uncertainty in evaluating urban development alternatives. T-SFS enables more accurate assessments by capturing expert judgment variability, particularly for complex criteria such as energy efficiency, biodiversity, and land use optimization. In addition, the study employs the CRADIS method alongside the Logarithmic Percentage Change-Driven Objective Weighting (LOPCOW) technique to assign precise weights to evaluation criteria. This integrated approach ensures that each criterion’s significance is accurately represented, supporting robust decision-making. The results provide a comparative analysis of five urban development alternatives, helping urban planners prioritize sustainable options that balance environmental, social, and economic factors. By incorporating fuzzy logic and advanced decision-making techniques, this study offers a comprehensive tool for policymakers to make informed decisions that contribute to sustainable urban growth, ultimately fostering cities that are more resilient, eco-friendly, and beneficial to society.


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