[1]	R. Quay. Anticipatory governance: a tool for climate change adaptation. J Am Plann Assoc, 76 (4) (2010), pp. 496-511.

[2]	A. Chakraborty, N. Kaza, G.J. Knaap, B. Deal. Robust plans and contingent plans-scenario planning for an uncertain world. J Am Plann Assoc, 77 (3) (2011), pp. 251-266.

[3]	A. Chakraborty, A. McMillan. Scenario planning for urban planners: toward a practitioner’s guide. J Am Plann Assoc, 81 (1) (2015), pp. 18-29.

[4]	G.D. Peterson, G.S. Cumming, S.R. Carpenter. Scenario planning: a tool for conservation in an uncertain world. Conserv Biol, 17 (2) (2003), pp. 358-366.

[5]	A. Wilkinson, R. Kupers, D. Mangalagiu. How plausibility-based scenario practices are grappling with complexity to appreciate and address 21st century challenges. Technol Forecast Soc Change, 80 (4) (2013), pp. 699-710.

[6]	M. Stojanovic, P. Mitkovic, M. Mitkovic. The scenario method in urban planning. Facta Universitatis—series. Architect Civil Eng, 12 (1) (2014), pp. 81-95.

[7]	N.N. Taleb. The black swan: the impact of the highly improbable. Penguin, Sydney (2008).

[8]	I. As, P. Basu, P. Talwar. Artificial intelligence in urban planning and design: technologies, implementation, and impacts. Elsevier, Amsterdam (2022).

[9]	C. Andrews, K. Cooke, A. Gomez, P. Hurtado, T. Sanchez, S. Shah, et al. AI in planning: opportunities and challenges and how to prepare. American Planning Association, Chicago (2022).

[10]	D. Wasserman, M. Flaxman. Artificial intelligence and planning practice. American Planning Association, Chicago (2022).

[11]	T.W. Sanchez, H. Shumway, T. Gordner, T. Lim. The prospects of artificial intelligence in urban planning. Int J Urban Sci, 27 (2) (2023), pp. 179-194.

[12]	P. Hurtado, S. Shah, J. DeAngelis, A. Gomez. 2023 trend report for planners. American Planning Association, Chicago (2023).

[13]	X. Ye, J. Du, Y. Ye. MasterplanGAN: facilitating the smart rendering of urban master plans via generative adversarial networks. Environ Plan B Urban Anal City Sci, 49 (3) (2022), pp. 794-814.

[14]	Y. Zheng, Y. Lin, L. Zhao, T. Wu, D. Jin, Y. Li. Spatial planning of urban communities via deep reinforcement learning. Nat Comput Sci, 3 (9) (2023), pp. 748-762.

[15]	Y. Wang, S. Gao, N. Li, S. Yu. Crowdsourcing the perceived urban built environment via social media: the case of underutilized land. Adv Eng Inform, 50 (2021), Article 101371.

[16]	Peng ZR, Lu KF, Liu Y, Zhai W. The pathway of urban planning AI: from planning support to plan-making. J Plann Educ Res. In press.

[17]	Y. Gil, D. Garijo, D. Khider, C.A. Knoblock, V. Ratnakar, M. Osorio, et al. Artificial intelligence for modeling complex systems: taming the complexity of expert models to improve decision making. ACM Trans Interact Intell Syst, 11 (2) (2021), pp. 1-49.

[18]	S. Ha, H. Jeong. Unraveling hidden interactions in complex systems with deep learning. Sci Rep, 11 (1) (2021), p. 12804.

[19]	S. Yu, J. Ma. Deep learning for geophysics: current and future trends. Rev Geophys, 59 (3) (2021), Article e2021RG000742.

[20]	T.H. Son, Z. Weedon, T. Yigitcanlar, T. Sanchez, J.M. Corchado, R. Mehmood. Algorithmic urban planning for smart and sustainable development: systematic review of the literature. Sustain Cities Soc, 94 (2023), Article 104562.

[21]	D. Kamrowska-Załuska. Impact of AI-based tools and urban big data analytics on the design and planning of cities. Land, 10 (11) (2021), p. 1209.

[22]	A. Sharifi, Z. Allam, S.E. Bibri, A.R. Khavarian-Garmsir. Smart cities and sustainable development goals (SDGs): a systematic literature review of co-benefits and trade-offs. Cities, 146 (2024), Article 104659.

[23]	R. Lempert, J. Syme, G. Mazur, D. Knopman, G. Ballard-Rosa, K. Lizon, et al. Meeting climate, mobility, and equity goals in transportation planning under wide-ranging scenarios: a demonstration of robust decision making. J Am Plann Assoc, 86 (3) (2020), pp. 311-323.

[24]	U. Pillkahn. Using trends and scenarios as tools for strategy development:shaping the future of your enterprise. John Wiley & Sons, Inc., New York City (2008).

[25]	M.J. Spaniol, N.J. Rowland. Defining scenario. Futures Foresight Sci, 1 (1) (2019), p. e3.

[26]	A. Khakee. Scenario construction for urban planning. Omega, 19 (5) (1991), pp. 459-469.

[27]	L. Börjeson, M. Höjer, K.H. Dreborg, T. Ekvall, G. Finnveden. Scenario types and techniques: towards a user’s guide. Futures, 38 (7) (2006), pp. 723-739.

[28]	U. Avin, R. Goodspeed. Using exploratory scenarios in planning practice: a spectrum of approaches. J Am Plann Assoc, 86 (4) (2020), pp. 403-416.

[29]	G. Abou Jaoude, O. Mumm, V.M. Carlow. An overview of scenario approaches: a guide for urban design and planning. J Plann Liter, 37 (3) (2022), pp. 467-487.

[30]	K. Ange, C. Dwyer, K. Rooney, E. Mierzejewski. Next generation scenario planning: a transportation practitioner’s guide. Federal Highway Administration, Washington, DC (2017).

[31]	K. Riahi, S. Rao, V. Krey, C. Cho, V. Chirkov, G. Fischer, et al. RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim Change, 109 (1-2) (2011), pp. 33-57.

[32]	B.C. O’Neill, E. Kriegler, K. Riahi, K.L. Ebi, S. Hallegatte, T.R. Carter, et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim Change, 122 (3) (2014), pp. 387-400.

[33]	S.A. Sherman, A. Chakraborty. Beyond plans: scenario planning as a tool for regional capacity building. J Am Plann Assoc, 88 (4) (2022), pp. 524-536.

[34]	L. Hopkins, M. Zapata. Engaging the future: forecasts, scenarios, plans, and projects. Lincoln Institute of Land Policy, Cambridge (2007).

[35]	Peregrino AA, Pradhan S, Liu Z, Ferreira N, Miranda F. Transportation scenario planning with graph neural networks. 2021. arXiv:2110.13202.

[36]	R. Goodspeed, C. Hackel. Lessons for developing a planning support system infrastructure: the case of southern California’s scenario planning model. Environ Plan B Urban Anal City Sci, 46 (4) (2019), pp. 777-796.

[37]

Hartmann S, Weinmann M, Wessel R, Klein R. StreetGAN:towards road network synthesis with generative adversarial networks. In: Proceedings of the International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision; 2017 May 29-Jun 2; Plzen, Czech Republic. München: Fraunhofer-Gesellschaft; 2017.

[38]	K. Kempinska, R. Murcio. Modelling urban networks using Variational Autoencoders. Appl Netw Sci, 4 (1) (2019), p. 114.

[39]	T. Owaki, T. Machida. RoadNetGAN: generating road networks in planar graph representation. Commun Comput Inform Sci, 1332 (2020), pp. 535-543.

[40]

Shen J, Liu C, Ren Y, Zheng H. Machine learning assisted urban filling. RE:anthropocene, design in the age of humans. In:Proceedings of the 25th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2020; 2020 Aug 5-8; Bangkok, Thailand. Hong Kong: The Association for Computer-Aided Architectural Design Research in Asia; 2020.

[41]	Tian R. Suggestive site planning with conditional GAN and urban GIS data. In: Proceedings of the 2020 DigitalFUTURES; 2020 Jul 5; Shanghai, China. Berlin: Springer; 2021.

[42]	Fedorova S. GANs for urban design. 2021. arXiv:2105. 01727v1.

[43]	S. Sun, L. Mu, R. Feng, L. Wang, J. He. GAN-based LUCC prediction via the combination of prior city planning information and land-use probability. IEEE J Sel Top Appl, 14 (2021), pp. 10189-10198.

[44]

Wang D, Fu Y, Wang P, Huang B, Lu CT. Reimagining city configuration:automated urban planning via adversarial learning. GIS:In:Proceedings of the ACM International Symposium on Advances in Geographic Information Systems; 2020 Nov 3-6; Washington, DC, USA. New York City: Association for Computing Machinery; 2020.

[45]	Wang D, Liu K, Johnson P, Sun L, Du B, Fu Y. Deep human-guided conditional variational generative modeling for automated urban planning. In: Proceedings of the IEEE International Conference on Data Mining; 2021 Dec 7-10; Auckland, New Zealand. New York City,: IEEE; 2021.

[46]	Mirza M, Osindero S. Conditional generative adversarial nets. 2014. arXiv:1411.1784v1.

[47]	Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. arXiv:1606.03657. 2016.

[48]	Z. Fang, J. Qi, L. Fan, J. Huang, Y. Jin, T. Yang. A topography-aware approach to the automatic generation of urban road networks. Int J Geogr Inf Sci, 36 (10) (2022), pp. 2035-2059.

[49]	Isola P, Zhu JY, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017; 2017 Jul 21-26; Honolulu, HI, USA. New York City: IEEE; 2017.

[50]	S.J. Quan. Urban-GAN: an artificial intelligence-aided computation system for plural urban design. Environ Plan B Urban Anal City Sci, 49 (9) (2022), pp. 2500-2515.

[51]	S. Kim, D. Kim, S. Choi. CityCraft: 3D virtual city creation from a single image. Vis Comput, 36 (5) (2020), pp. 911-924.

[52]

Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. In: Proceedings of the 4th International Conference on Learning Representations (ICLR 2016); 2016 May 2-4; San Juan, Puerto Rico. New York City: Curran Associates, Inc.; 2016.

[53]	H. Hao, Y. Wang. Disentangling relations between urban form and urban accessibility for resilience to extreme weather and climate events. Landsc Urban Plann, 220 (2022), Article 104352.

[54]	Y. Kim, G. Newman. Advancing scenario planning through integrating urban growth prediction with future flood risk models. Comput Environ Urban Syst, 82 (2020), Article 101498.

[55]	S. Berberoğlu, A. Akin, K.C. Clarke. Cellular automata modeling approaches to forecast urban growth for adana, Turkey: a comparative approach. Landsc Urban Plann, 153 (2016), pp. 11-27.

[56]	X. Liu, X. Liang, X. Li, X. Xu, J. Ou, Y. Chen, et al. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landsc Urban Plann, 168 (2017), pp. 94-116.

[57]	J. He, X. Li, Y. Yao, Y. Hong, Z. Jinbao. Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques. Int J Geogr Inf Sci, 32 (10) (2018), pp. 2076-2097.

[58]	Y. Zhai, Y. Yao, Q. Guan, X. Liang, X. Li, Y. Pan, et al. Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata. Int J Geogr Inf Sci, 34 (7) (2020), pp. 1475-1499.

[59]	Grattarola D, Livi L, Alippi C. Learning graph cellular automata. In:Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021); 2021 Dec 6-14; Online. New York City: Curran Associates, Inc.; 2021.

[60]	X. Guan, W. Xing, J. Li, H. Wu. HGAT-VCA: integrating high-order graph attention network with vector cellular automata for urban growth simulation. Comput Environ Urban Syst, 99 (2023), Article 101900.

[61]	J.A. Wardekker, A. de Jong, J.M. Knoop, J.P. van der Sluijs. Operationalising a resilience approach to adapting an urban delta to uncertain climate changes. Technol Forecast Soc Change, 77 (6) (2010), pp. 987-998.

[62]	Y. Yildirim, G. Keshavarzihaghighi, A.R. Aman. Sustainable responses of an urban park for disaster resilience: a case study of hurricane harvey. Int J Sustain Dev World Ecol, 28 (8) (2021), pp. 720-732.

[63]	S. Li, T. Wu. Deep reinforcement learning-based decision support system for transportation infrastructure management under hurricane events. Struct Saf, 99 (2022), Article 102254.

[64]	Z. Zhang, W. Tian, Z. Liao. Towards coordinated and robust real-time control: a decentralized approach for combined sewer overflow and urban flooding reduction based on multi-agent reinforcement learning. Water Res, 229 (2023), Article 119498.

[65]	T. Yang, L. Zhao, W. Li, A.Y. Zomaya. Reinforcement learning in sustainable energy and electric systems: a survey. Annu Rev Control, 49 (2020), pp. 145-163.

[66]	P. Mannion, J. Duggan, E. Howley. An experimental review of reinforcement learning algorithms for adaptive traffic signal control. T. McCluskey, A. Kotsialos, J. Müller, F. Klügl, O. Rana, R. Schumann (Eds.), Autonomic Road Transport Support Systems, Springer, Berlin (2016), pp. 47-66.

[67]	M.J. Spaniol, N.J. Rowland. AI-assisted scenario generation for strategic planning. Futures Foresight Sci, 5 (2) (2023), p. e148.

[68]	ChatGPT Assistant. AI-powered language model trained by OpenAI [Internet]. San Francisco: OpenAI. Available from:

[69]	W. Dong, X. Chen, Q. Yang. Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability. Appl Energy, 308 (2022), Article 118387.

[70]	H.W. Wang, Z.R. Peng, D. Wang, Y. Meng, T. Wu, W. Sun, et al. Evaluation and prediction of transportation resilience under extreme weather events: a diffusion graph convolutional approach. Transp Res Part C Emerg, 115 (2020), Article 102619.

[71]	M. Rüttgers, S. Lee, S. Jeon, D. You. Prediction of a typhoon track using a generative adversarial network and satellite images. Sci Rep, 9 (1) (2019), p. 6057.

[72]	Bhatia S, Jain A, Hooi B. ExGAN:adversarial generation of extreme samples. In:Proceedings of the 35th AAAI Conference on Artificial Intelligence; 2021 Feb 2-9; Online. Washington, DC: The Association for the Advancement of Artificial Intelligence; 2021.

[73]	Y. Boulaguiem, J. Zscheischler, E. Vignotto, K. van der Wiel, S. Engelke. Modeling and simulating spatial extremes by combining extreme value theory with generative adversarial networks. Environ Data Sci, 1 (2022), p. e5.

[74]	N. Oyama, N.N. Ishizaki, S. Koide, H. Yoshida. Deep generative model super-resolves spatially correlated multiregional climate data. Sci Rep, 13 (1) (2023), p. 5992.

[75]	Ahmed M, Saleh K, Abobakr A, Nahavandi S. Scenario generation-based training in simulation:pilot study. In: Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics; 2019 Oct 6-9; Bari, Italy. New York City: IEEE; 2019.

[76]	W. Li, C.W. Pan, R. Zhang, J.P. Ren, Y.X. Ma, J. Fang, et al. AADS: augmented autonomous driving simulation using data-driven algorithms. Sci Robot, 4 (28) (2019), Article eaaw0863.

[77]

Fremont DJ, Dreossi T, Ghosh S, Yue X, Sangiovanni-Vincentelli AL, Seshia SA. Scenic:a language for scenario specification and scene generation. In:Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI); 2019 Jun 22-26; Phoenix, AZ, USA. New York City: Association for Computing Machinery; 2019. p. 63-78.

[78]	A. Crooks, A. Heppenstall, N. Malleson, E. Manley. Agent-based modeling and the city:a gallery of applications. Urban Informatics, Springer, Berlin (2021).

[79]	E. Bonabeau. Agent-based modeling: Methods and techniques for simulating human systems. Proc Natl Acad Sci U S A, 99 (Suppl 3) (2002), pp. 7280-7287.

[80]	M. Yu, A. Bayram. Management of the curb space allocation in urban transportation system. Int Trans Oper Res, 28 (5) (2021), pp. 2414-2439.

[81]	Y. Wang, H. Hao, C. Wang. Preparing urban curbside for increasing mobility-on-demand using data-driven agent-based simulation: case study of city of Gainesville, Florida. J Manage Eng, 38 (2022).

[82]	M. Jaller, C. Rodier, M. Zhang, H. Lin, K. Lewis. Fighting for curb space: parking, ride-hailing, urban freight deliveries, and other users [supporting dataset]. Bureau of Transportation Statistics, Washington, DC (2021).

[83]	D. Kondor, P. Santi, D.T. Le, X. Zhang, A. Millard-Ball, C. Ratti. Addressing the “minimum parking” problem for on-demand mobility. Sci Rep, 10 (1) (2020), p. 15885.

[84]	K. Winter, O. Cats, K. Martens, B. van Arem. Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking. Transportation, 48 (4) (2021), pp. 1931-1965.

[85]	H. Yan, K.M. Kockelman, K.M. Gurumurthy. Shared autonomous vehicle fleet performance: impacts of trip densities and parking limitations. Transp Res D Transp Environ, 89 (2020), Article 102577.

[86]	W. Zhang, K. Wang. Parking futures: shared automated vehicles and parking demand reduction trajectories in Atlanta. Land Use Policy, 91 (2020), Article 103963.

[87]	Y. Han, K. Ash, L. Mao, Z.R. Peng. An agent-based model for community flood adaptation under uncertain sea-level rise. Clim Change, 162 (4) (2020), pp. 2257-2276.

[88]	R. Lemoy, C. Raux, P. Jensen. Exploring the polycentric city with multi-worker households: an agent-based microeconomic model. Comput Environ Urban Syst, 62 (2017), pp. 64-73.

[89]	H. Kavak, J.J. Padilla, C.J. Lynch, S.Y. Diallo. Big data, agents, and machine learning: towards a data-driven agent-based modeling approach. Simulation Series, 50 (2018).

[90]	M. Ale Ebrahim Dehkordi, J. Lechner, A. Ghorbani, I. Nikolic, E. Chappin, P. Herder. Using machine learning for agent specifications in agent-based models and simulations: a critical review and guidelines. JASSS, 26 (1) (2023), p. 9.

[91]	E. Sert, Y. Bar-Yam, A.J. Morales. Segregation dynamics with reinforcement learning and agent based modeling. Sci Rep, 10 (1) (2020), p. 11771.

[92]	Song B, Xiong G, Yu S, Ye P, Dong X, Lv Y. Calibration of agent-based model using reinforcement learning. In: Proceedings of the 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence; 2021 Jul 15-Aug 15; Beijing, China. New York City: IEEE; 2021.

[93]

Park JS, O’Brien J, Cai CJ, Morris MR, Liang P, Bernstein MS. Generative agents:interactive simulacra of human behavior. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology; 2023 Oct 29- Nov 1; San Francisco, CA, USA. New York City: Association for Computing Machinery; 2023.

[94]	Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, et al. Relational inductive biases, deep learning, and graph networks. 2018. arXiv:1806.01261v3.

[95]	A. Goyal, Y. Bengio. Inductive biases for deep learning of higher-level cognition. Proc R Soc A Math Phys Eng Sci, 478 (2226) (2022), p. 0068.

[96]	H. Hao, Y. Wang, L. Du, S. Chen. Enabling smart curb management with spatiotemporal deep learning. Comput Environ Urban Syst, 99 (2023), Article 101914.

[97]	R.E. Klosterman. Lessons learned about planning: forecasting, participation, and technology. J Am Plann Assoc, 79 (2) (2013), p. 161.

[98]	L. Maffei, M. Masullo, A. Pascale, G. Ruggiero, V.P. Romero. Immersive virtual reality in community planning: acoustic and visual congruence of simulated vs real world. Sustain Cities Soc, 27 (2016), pp. 338-345.

[99]

van Leeuwen JP, Hermans K, Jylhä A, Quanjer AJ, Nijman H. Effectiveness of virtual reality in participatory urban planning:a case study. In:Proceedings of the 4th Media Architecture Biennale Conference; 2018 Nov 13-16; Beijing, China. New York City: Association for Computing Machinery; 2018. p. 128-36.

[100]

T. Kelly, P. Guerrero, A. Steed, P. Wonka, N.J. Mitra. Frankengan: guided detail synthesis for building mass models using style-synchonized GANs. ACM Trans. Graph, 37 (6) (2018), pp. 1-14.

[101]

Alonso L, Zhang YR, Grignard A, Noyman A, Sakai Y, ElKatsha M, et al. CityScope:a data-driven interactive simulation tool for urban design. In: MoralesA, GershensonC, BrahaD, MinaiA, Bar-YamY, editors. Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Berilin: Springer; 2018. p. 253-61.

[102]

Y. Dan, Z. Shen, J. Xiao, Y. Zhu, L. Huang, J. Zhou. HoloDesigner: a mixed reality tool for on-site design. Autom Constr, 129 (2021), Article 103808.

[103]

S.J. Quan, J. Park, A. Economou, S. Lee. Artificial intelligence-aided design: smart design for sustainable city development. Environ Plan B Urban Anal City Sci, 46 (8) (2019), pp. 1581-1599.

[104]

S. Robbins. AI and the path to envelopment: knowledge as a first step towards the responsible regulation and use of AI-powered machines. AI & Society, 35 (2) (2020), pp. 391-400.

[105]

A. Asatiani, P. Malo, P.R. Nagbøl, E. Penttinen, T. Rinta-Kahila, A. Salovaara. Sociotechnical envelopment of artificial intelligence: an approach to organizational deployment of inscrutable artificial intelligence systems. J Assoc Inf Syst, 22 (2) (2021), pp. 325-352.

[106]

C. Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell, 1 (5) (2019), pp. 206-215.

[107]

W.J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, B. Yu. Definitions, methods, and applications in interpretable machine learning. Proc Natl Acad Sci U S A, 116 (44) (2019), pp. 22071-22080.

[108]

D. Gunning, M. Stefik, J. Choi, T. Miller, S. Stumpf, G.Z. Yang. XAI—explainable artificial intelligence. Sci Robot, 4 (37) (2019), Article eaay7120.

[109]

A.R. Javed, W. Ahmed, S. Pandya, P.K.R. Maddikunta, M. Alazab, T.R. Gadekallu. A survey of explainable artificial intelligence for smart cities. Electronics, 12 (4) (2023), p. 1020.

[110]

A. Barredo Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, et al. Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion, 58 (2020), pp. 82-115.

[111]

N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, A. Galstyan. A survey on bias and fairness in machine learning. ACM Comput Surv, 54 (6) (2022), pp. 1-35.

[112]

B. Chen, Y. Wang, R. Wang, Z. Zhu, L. Ma, X. Qiu, et al. The gray-box based modeling approach integrating both mechanism-model and data-model: the case of atmospheric contaminant dispersion. Symmetry, 12 (2) (2020), p. 254.

[113]

H. Hao, Y. Wang. Modeling dynamics of community resilience to extreme events with explainable deep learning. Nat Hazards Rev, 24 (2) (2023), Article 04023013.

[114]

B. Barzel, A.L. Barabási. Universality in network dynamics. Nat Phys, 9 (10) (2013), pp. 673-681.

[115]

Hao H, Wang Y. A deep learning representation of spatial interaction model for resilient spatial planning of community business clusters. 2024. arXiv:2401.04849v1.

[116]

H. Zhou, F. Zhang, Z. Du, R. Liu. Forecasting PM2.5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability. Environ Pollut, 273 (2021), Article 116473.

[117]

A.N. Wu, R. Stouffs, F. Biljecki. Generative Adversarial Networks in the built environment: a comprehensive review of the application of GANs across data types and scales. Build Environ, 223 (2022), Article 109477.

[118]

E. Bruno. Integrating AI and deep learning within design practice processes: XKool technology. Ardeth, 5 (2019), pp. 220-226.

[119]

J.F. Bonnefon, D. Černy, J. Danaher, N. Devillier, V. Johansson, T. Kovacikova, et al. Ethics of connected and automated vehicles: recommendations on road safety, privacy, fairness, explainability and responsibility. Publications Office of the European Union, Report. Luxembourg (2020).