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

[1] A. Garzón, Kapelan, Z., Langeveld, J., & Taormina, R. (2022). Machine learning‐based surrogate modeling for urban water networks: review and future research directions. Water Resources Research. Search in Google Scholar

[2] Mao, W., Lu, D., Hou, L., Liu, X., & Yue, W. (2020). Comparison of machine-learning methods for urban land-use mapping in hangzhou city, china. Remote Sensing, 12(17), 2817. Search in Google Scholar

[3] Maitland, N., Wardle, K., Haigh, F., Allender, S., & Jalaludin, B. (2020). Translating a whole of system approach to an urban community setting: a realist evaluation. European Journal of Public Health (Supplement_5), Supplement_5. Search in Google Scholar

[4] Tarawneh, A. A., & Al, S. J. (2021). Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm. Journal of Intelligent and Fuzzy Systems. Search in Google Scholar

[5] Jairo Alejandro Gómez Escobar, Patio, J. E., Duque, J. C., & Passos, S. (2019). Spatiotemporal modeling of urban growth using machine learning. Remote Sensing, 12(1), 1–41. Search in Google Scholar

[6] Sun, Y., Gao, C., Li, J., Wang, R., & Liu, J. (2019). Quantifying the effects of urban form on land surface temperature in subtropical high-density urban areas using machine learning. Remote Sensing, 11(8). Search in Google Scholar

[7] Griffith, D., & Hay, G. (2018). Integrating geobia, machine learning, and volunteered geographic information with map vegetation over rooftops. ISPRS International Journal of Geo-Information, 7(12). Search in Google Scholar

[8] Van, d. J. A. P. N., Buijs, A., Dobbs, C., Van, L. M., Pauleit, S., & Randrup, T. B., et al. (2023). With the process comes the progress: a systematic review to support governance assessment of urban nature-based solutions. Urban Forestry & Urban Greening, 87. Search in Google Scholar

[9] Nidia Isabel Molina-Gómez, Karen Rodríguez-Rojas, Dayam Calderón-Rivera, José Luis Díaz-Arévalo, & P. Amparo López-Jiménez. (2020). Using machine learning tools to classify sustainability levels in the development of urban ecosystems. Sustainability, 12. Search in Google Scholar

[10] Zhang, J., Fu, D., Urich, C., & Singh, R. (2018). Accelerated exploration for long-term urban water infrastructure planning through machine learning. Sustainability, 10(12). Search in Google Scholar

[11] Fu, & Ma. (2020). Sustainable urban community development: a case study from the perspective of self-governance and public participation. Sustainability, 12(2), 617. Search in Google Scholar

[12] Lin, Q., Liu, K., Hong, B., Xu, X., Chen, J., & Wang, W. (2022). A data-driven framework for abnormally high building energy demand detection with weather and block morphology at community scale. Journal of cleaner production(Jun.20), 354. Search in Google Scholar

[13] Zahura, F. T., Goodall, J. L., Sadler, J. M., Shen, Y., Morsy, M. M., & Behl, M. (2020). Training machine learning surrogate models from a high‐fidelity physics‐based model: application for real‐time street‐scale flood prediction in an urban coastal community. Water Resources Research, 56. Search in Google Scholar

[14] Gilani, V. N. M., Hosseinian, S. M., Ghasedi, M., & Nikookar, M. (2021). Data-driven urban traffic accident analysis and prediction using logit and machine learning-based pattern recognition models. Mathematical Problems in Engineering, 2021(1), 1–11. Search in Google Scholar

[15] Miller, D. C. (2019). Machine learning to analyze the social-ecological impacts of natural resource policy: insights from community forest management in the indian himalaya. Environmental Research Letters, 14(2), 024008 (12pp). Search in Google Scholar

[16] Wen, Y., Zhou, Z., & Zhang, S. (2022). Urban-rural disparities in air quality responses to traffic changes in a megacity of china revealed using machine learning. Environmental Science & Technology Letters(7), 9. Search in Google Scholar

[17] Fan, C., Xu, J., Natarajan, B. Y., & Mostafavi, A. (2023). Interpretable machine learning learns complex interactions of urban features to understand socio‐economic inequality. Computer-aided civil and infrastructure engineering(14), 38. Search in Google Scholar

[18] Gupta, K., Yang, Z., & Jain, R. K. (2019). Urban data integration using proximity relationship learning for design, management, and operations of sustainable urban systems. Journal of Computing in Civil Engineering, 33(2), 04018063.1–04018063.16. Search in Google Scholar