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<p>Urban community governance faces unprecedented challenges, but machine learning provides new ideas for it. The objective of this study is to examine the use of machine learning technology in urban community governance to enhance governance efficiency and decision-making quality. The study constructs an artificial neural network intelligent decision support model based on genetic algorithm optimization, which is based on the operational requirements of a smart decision support system. A weighted fuzzy inference network is further developed by the fusion of fuzzy logic and neural networks to enhance the system’s ability to deal with uncertainty and ambiguity. Next, prediction tests were conducted on the development levels of six communities to validate the model’s effectiveness. After 1686 training steps, the error squared SSE drops below 0.2%, according to the results. The composite index’s prediction error was 5.12%, while the minimum error was −1.79%. The predicted rankings of the communities did not change from the actual ones, which was in line with the normal trend, and the algorithmic model achieved better prediction results. This study not only provides a new intelligent decision support tool for community governance but also sets a theoretical and practical foundation for the intelligent development of urban community governance in the future.</p>
</abstract>

Urban community governance faces unprecedented challenges, but machine learning provides new ideas for it. The objective of this study is to examine the use of machine learning technology in urban community governance to enhance governance efficiency and decision-making quality. The study constructs an artificial neural network intelligent decision support model based on genetic algorithm optimization, which is based on the operational requirements of a smart decision support system. A weighted fuzzy inference network is further developed by the fusion of fuzzy logic and neural networks to enhance the system’s ability to deal with uncertainty and ambiguity. Next, prediction tests were conducted on the development levels of six communities to validate the model’s effectiveness. After 1686 training steps, the error squared SSE drops below 0.2%, according to the results. The composite index’s prediction error was 5.12%, while the minimum error was −1.79%. The predicted rankings of the communities did not change from the actual ones, which was in line with the normal trend, and the algorithmic model achieved better prediction results. This study not only provides a new intelligent decision support tool for community governance but also sets a theoretical and practical foundation for the intelligent development of urban community governance in the future.

Urban community governance and machine learning: practice and prospects for intelligent decision making

Jinshan College of Fujian Agriculture and Forestry University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Urban community governance faces unprecedented challenges, but machine learning provides new ideas for it. The objective of this study is to examine the use of...

Urban community governance and machine learning: practice and prospects for intelligent decision making

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 06. März 2024

Akzeptiert: 09. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1308

Schlüsselwörter
Urban community governance, Machine learning, Intelligent decision making, Genetic algorithm

© 2024 Yuxin Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Urban community governance and machine learning: practice and prospects for intelligent decision making

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 06. März 2024

Akzeptiert: 09. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1308

SchlüsselwörterUrban community governance, Machine learning, Intelligent decision making, Genetic algorithm

© 2024 Yuxin Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Urban community governance, Machine learning, Intelligent decision making, Genetic algorithm