Innovative Design Synthesis: AI-Driven Solutions for Sustainable Urban Planning: Development, Evaluation, and Insights
DOI:
https://doi.org/10.25034/ijcua.2024.v8n1-10Keywords:
Artificial Intelligence, Sustainable Urban Planning, Model Development, Urban Problems DetectionAbstract
This research aims to explore the capabilities of Artificial Intelligence model applications in sustainable urban planning design, aiming to create an AI-powered model that detects urban sprawl, urban decay, infrastructure deficiencies, and environmental degradation, and generates sustainable solutions. The methodology includes model development, data preprocessing, architecture design, training, and comprehensive questionnaire design and statistical analysis. Insights into the model's performance and perception were gathered from 200 participants, including researchers, developers, architects, and urbanists Results reveal significant variations in perceived model performance across demographic groups. Statistical tests and correlations study showed that participants from urban areas rated the model's effectiveness in detecting urban sprawl significantly higher than those from rural areas, and urban planners found the model's infrastructure deficiency detection less accurate compared to architects. These findings provide valuable guidance for enhancing urban planning strategies and underscore AI's potential in transforming urban planning with more effective and inclusive solutions.
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Almaz, A. F., El-Agouz, E. a. E., Abdelfatah, M. T., & Mohamed, I. R. (2024). The Future Role of Artificial Intelligence (AI) Design’s Integration into Architectural and Interior Design Education is to Improve Efficiency, Sustainability, and Creativity. Civil Engineering and Architecture, 12(3), 1749–1772. https://doi.org/10.13189/cea.2024.120336
Auwalu, F. K., & Bello, M. (2023). Exploring the contemporary challenges of urbanization and the role of sustainable urban development: a study of Lagos City, Nigeria. Journal of Contemporary Urban Affairs, 7(1), 175–188. https://doi.org/10.25034/ijcua.2023.v7n1-12
Bandi, A., Adapa, P. V. S. R., & Kuchi, Y. E. V. P. K. (2023). The power of Generative AI: a review of requirements, models, Input–output formats, evaluation metrics, and challenges. Future Internet, 15(8), 260. https://doi.org/10.3390/fi15080260
Berčič, T., Bohanec, M., & Momirski, L. A. (2024). Integrating Multi-Criteria Decision Models in Smart Urban Planning: A case study of architectural and urban design competitions. Smart Cities, 7(2), 786–805. https://doi.org/10.3390/smartcities7020033
Bibri, S. E., Huang, J., Jagatheesaperumal, S. K., & Krogstie, J. (2024). The synergistic interplay of Artificial intelligence and digital twin in Environmentally Planning Sustainable Smart Cities: A Comprehensive Systematic Review. Environmental Science & Ecotechnology, 100433. https://doi.org/10.1016/j.ese.2024.100433
Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024). Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science & Ecotechnology, 19, 100330. https://doi.org/10.1016/j.ese.2023.100330
Bölek, B., Tutal, O., & Özbaşaran, H. (2023). A systematic review on artificial intelligence applications in architecture. Journal of Design for Resilience in Architecture and Planning :, 4(1), 91–104. https://doi.org/10.47818/drarch.2023.v4i1085
Brisotto, C., Carney, J., Foroutan, F., Ochoa, K. S., & Schroder, W. (2023). Exploring the Role of AI in Urban Design Research. The Plan Journal, 8(2). https://doi.org/10.15274/tpj.2023.08.02.5
Chaudhuri, G., & Clarke, K. C. (2013). Temporal Accuracy in Urban Growth Forecasting: A study using the SLEUTH model. Transactions in GIS, 18(2), 302–320. https://doi.org/10.1111/tgis.12047
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., . . . Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Dwivedi, Y. K., Sharma, A., Rana, N. P., Giannakis, M., Goel, P., & Dutot, V. (2023). Evolution of artificial intelligence research in Technological Forecasting and Social Change. Technological Forecasting & Social Change/Technological Forecasting and Social Change, 192, 122579. https://doi.org/10.1016/j.techfore.2023.122579
Edge. (n.d.). EDGE | The Edge. EDGE. https://edge.tech/developments/the-edge
Fallmann, J., & Emeis, S. (2020). How to bring urban and global climate studies together with urban planning and architecture? Developments in the Built Environment, 4, 100023. https://doi.org/10.1016/j.dibe.2020.100023
Ersen, M., Büyüklü, A. H., & Taşabat, S. E. (2022). Data mining as a method for comparison of traffic accidents in Şişli district of Istanbul. Journal of Contemporary Urban Affairs, 6(2), 113–141. https://doi.org/10.25034/ijcua.2022.v6n2-2
Ferramosca, G., & Terracciano, A. (2023). From Urban Vulnerabilities to Resilience: Lessons from Messina’s Integrated Risk Approach. Journal of Contemporary Urban Affairs, 7(2), 219–243. https://doi.org/10.25034/ijcua.2023.v7n2-14
Goodman, E. P., & Powles, J. (2019). Urbanism Under Google: Lessons from Sidewalk Toronto. Social Science Research Network. https://doi.org/10.2139/ssrn.3390610
He, W., & Chen, M. (2024). Advancing Urban Life: A Systematic review of emerging technologies and artificial intelligence in urban design and planning. Buildings, 14(3), 835. https://doi.org/10.3390/buildings14030835
Herath, H., & Mittal, M. (2022). Adoption of artificial intelligence in smart cities: A comprehensive review. International Journal of Information Management Data Insights, 2(1), 100076. https://doi.org/10.1016/j.jjimei.2022.100076
Huang, C., Zhang, Z., Mao, B., & Yao, X. (2023). An Overview of Artificial Intelligence Ethics. IEEE Transactions on Artificial Intelligence, 4(4), 799–819. https://doi.org/10.1109/tai.2022.3194503
John-Nsa, C., Onyebueke, V., & Enemuo, E. (2023a). Street Trading and Urban Distortion: Rethinking Impacts and Management Approaches from Urban Planners’ Perspective in Enugu City, Nigeria. Journal of Contemporary Urban Affairs, 7(2). https://doi.org/10.25034/ijcua.2023.v7n2-13
John-Nsa, C., Onyebueke, V., & Enemuo, E. (2023b). Street Trading and Urban Distortion: Rethinking Impacts and Management Approaches from Urban Planners’ Perspective in Enugu City, Nigeria. Journal of Contemporary Urban Affairs, 7(2). https://doi.org/10.25034/ijcua.2023.v7n2-13
Kanyepe, J. (2023). The Nexus between Residential Density, Travel Behavior and Traffic Congestion in Developing Metropolitans: A Case Study of Harare, Zimbabwe. Journal of Contemporary Urban Affairs, 7(1), 103–117. https://doi.org/10.25034/ijcua.2023.v7n1-7
Khatun, T. (2007). Measuring environmental degradation by using principal component analysis. Environment, Development and Sustainability, 11(2), 439–457. https://doi.org/10.1007/s10668-007-9123-2
Kanyepe, J. (2023). The Nexus between Residential Density, Travel Behavior and Traffic Congestion in Developing Metropolitans: A Case Study of Harare, Zimbabwe. Journal of Contemporary Urban Affairs, 7(1), 103–117. https://doi.org/10.25034/ijcua.2023.v7n1-7
Koutra, S., & Ioakimidis, C. S. (2022). Unveiling the potential of machine learning applications in urban planning challenges. Land, 12(1), 83. https://doi.org/10.3390/land12010083
Lepakshi, V. A. (2022). Machine Learning and Deep Learning based AI Tools for Development of Diagnostic Tools. In Elsevier eBooks (pp. 399–420). https://doi.org/10.1016/b978-0-323-91172-6.00011-x
Li, X., Stringer, L. C., & Dallimer, M. (2022). The impacts of urbanisation and climate change on the urban thermal environment in Africa. Climate, 10(11), 164. https://doi.org/10.3390/cli10110164
Mashhood, N. M., Salman, N. H., Amjad, N. R., & Nisar, N. H. (2023). The Advantages of Using Artificial Intelligence in Urban Planning – A Review of literature. Statistics, Computing and Interdisciplinary Research, 5(2), 1–12. https://doi.org/10.52700/scir.v5i2.125
Mumuni, A., & Mumuni, F. (2024). Automated data processing and feature engineering for deep learning and big data applications: a survey. Journal of Information and Intelligence. https://doi.org/10.1016/j.jiixd.2024.01.002
Omrany, H., Al-Obaidi, K. M., Husain, A., & Ghaffarianhoseini, A. (2023). Digital Twins in the Construction industry: A comprehensive review of current implementations, enabling technologies, and future directions. Sustainability, 15(14), 10908. https://doi.org/10.3390/su151410908
Paris, M., Dubois, F., Bosc, S., & Devillers, P. (2023). Integrating Wind Flow Analysis in early Urban Design: Guidelines for Practitioners. Journal of Contemporary Urban Affairs, 7(2), 194–211. https://doi.org/10.25034/ijcua.2023.v7n2-12
Peng, Z., Lu, K., Liu, Y., & Zhai, W. (2023). The Pathway of Urban Planning AI: From Planning Support to Plan-Making. Journal of Planning Education and Research. https://doi.org/10.1177/0739456x231180568
Rafsanjani, H. N., & Nabizadeh, A. H. (2023). Towards human-centered artificial intelligence (AI) in architecture, engineering, and construction (AEC) industry. Computers in Human Behavior Reports, 11, 100319. https://doi.org/10.1016/j.chbr.2023.100319
Ramm, T. M., Werwie, M., Otto, T., Gloor, P. A., & Salingaros, N. A. (2024). Artificial intelligence evaluates how humans connect to the built environment: a pilot study of two experiments in biophilia. Sustainability, 16(2), 868. https://doi.org/10.3390/su16020868
Regona, M., Yigitcanlar, T., Hon, C., & Teo, M. (2024). Artificial Intelligence and Sustainable Development Goals: Systematic Literature Review of the construction industry. Sustainable Cities and Society, 105499. https://doi.org/10.1016/j.scs.2024.105499
Safabakhshpachehkenari, M., & Tonooka, H. (2023). Assessing and enhancing predictive efficacy of machine learning models in urban land dynamics: A comparative study using Multi-Resolution Satellite data. Remote Sensing, 15(18), 4495. https://doi.org/10.3390/rs15184495
Sjödin, D., Parida, V., & Kohtamäki, M. (2023). Artificial intelligence enabling circular business model innovation in digital servitization: Conceptualizing dynamic capabilities, AI capacities, business models and effects. Technological Forecasting & Social Change/Technological Forecasting and Social Change, 197, 122903. https://doi.org/10.1016/j.techfore.2023.122903
Smorzhenkov, N., & Ignatova, E. (2021). The use of generative design for the architectural solutions synthesis in the typical construction of residential buildings. E3S Web of Conferences, 281, 04008. https://doi.org/10.1051/e3sconf/202128104008
Šoštarić, M., Vidović, K., Jakovljević, M., & Lale, O. (2021). Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement. Sustainability, 13(13), 7162. https://doi.org/10.3390/su13137162
Son, T. H., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, J. M., & Mehmood, R. (2023). Algorithmic urban planning for smart and sustainable development: Systematic review of the literature. Sustainable Cities and Society, 94, 104562. https://doi.org/10.1016/j.scs.2023.104562
Sonde, P., Balamwar, S., & Ochawar, R. S. (2020). Urban sprawl detection and analysis using unsupervised classification of high-resolution image data of Jawaharlal Nehru Port Trust area in India. Remote Sensing Applications, 17, 100282. https://doi.org/10.1016/j.rsase.2019.100282
Vallebueno, A., & Lee, Y. S. (2023). Measuring urban quality and change through the detection of physical attributes of decay. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-44551-3
Yu, D., & Fang, C. (2023). Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades. Remote Sensing, 15(5), 1307. https://doi.org/10.3390/rs15051307
Zhang, Z., Fort, J. M., & Mateu, L. G. (2023). Exploring The Potential of Artificial Intelligence as a Tool for Architectural Design: A Perception Study Using Gaudí’sWorks. Buildings, 13(7), 1863. https://doi.org/10.3390/buildings13071863
Zhao, Z., Alzubaidi, L., Zhang, J., Duan, Y., & Gu, Y. (2024). A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations. Expert Systems With Applications, 242, 122807. https://doi.org/10.1016/j.eswa.2023.122807
Zhou, Z., Lin, Y., Jin, D., & Li, Y. (2024). Large language model for participatory urban planning. arXiv preprint arXiv:2402.17161
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Copyright (c) 2024 Dr. Koudoua Ferhati, Ahmed M. Elgohary, Ahmed N. Elghar, Ahmad S. Abdelaal, Ahmed M. Kamel, Mohamed A. Elsayed, Ahmed E. Sheimy, Mena E. Habib
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