Tích hợp tự động hóa tế bào và mô hình dựa trên tác nhân để dự đoán tăng trưởng đô thị: Trường hợp thành phố Dehradun

Aarthi, A. D., & Gnanappazham, L. (2018). Urban growth prediction using neural network coupled agents-based Cellular Automata model for Sriperumbudur Taluk, Tamil Nadu, India. The Egyptian Journal of Remote Sensing and Space Science, 21(3), 353–362. https://doi.org/10.1016/j.ejrs.2017.12.004 Ahmed, B., Ahmed, R., & Zhu, X. (2013). Evaluation of model validation techniques in land cover dynamics. ISPRS International Journal of Geo-Information, 2(3), 577–597. https://doi.org/10.3390/ijgi2030577 Azari, M., & Ahadnejad Reveshty, M. (2013). Interference of human impacts in urban growth modelling with transition rules of cellular automata, GIS and multi-temporal satellite imagery: A case study of Maraghe, Iran. Journal of the Indian Society of Remote Sensing, 41(4), 993–1008. https://doi.org/10.1007/s12524-013-0275-2 Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Supplement 3), 7280–7287. https://doi.org/10.1073/pnas.082080899 Brown, D. G., Walker, R., Manson, S., & Seto, K. (2012). Modeling land use and land cover change. In Land change science (pp. 395–409). Springer, Dordrecht. Chen, M., Arribas-Bel, D., & Singleton, A. (2019). Understanding the dynamics of urban areas of interest through volunteered geographic information. Journal of Geographical Systems, 21(1), 89–109. https://doi.org/10.1007/s10109-018-0284-3 Clarke, K. C. (2014). Cellular automata and agent-based models. In M. M. Fischer & P. Nijkamp (Eds.), Handbook of regional science (pp. 1217–1233). Berlin: Springer. https://doi.org/10.1007/978-3-642-23430-9_63 Deep, S., & Saklani, A. (2014). Urban sprawl modeling using cellular automata. The Egyptian Journal of Remote Sensing and Space Science, 17(2), 179–187. https://doi.org/10.1016/j.ejrs.2014.07.001 Devendran, A. A., & Lakshmanan, G. (2019). Analysis and prediction of urban growth using neural-network-coupled agent-based cellular automata model for Chennai Metropolitan Area, Tamil Nadu, India. Journal of the Indian Society of Remote Sensing, 47(9), 1515–1526. https://doi.org/10.1007/s12524-019-01003-8 Fitawok, M. B., Derudder, B., Minale, A. S., Van Passel, S., Adgo, E., & Nyssen, J. (2020). Modeling the impact of urbanization on land-use change in Bahir Dar City, Ethiopia: An integrated cellular automata–Markov chain approach. Land, 9(4), 115. https://doi.org/10.3390/land9040115 Gandhi, S. R., Sharma, S. A., & Vyas, A. (2016). Quantifying urban sprawl for Rajkot City using geospatial technology. International Journal of Built Environment and Sustainability, 3(2). https://doi.org/10.11113/ijbes.v3.n2.124 Guan, C., & Rowe, P. G. (2016). Should big cities grow? Scenario-based cellular automata urban growth modeling and policy applications. Journal of Urban Management, 5(2), 65–78. https://doi.org/10.1016/j.jum.2017.01.002 Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10(10), 3421. https://doi.org/10.3390/su10103421 He, C., Wei, A., Shi, P., Zhang, Q., & Zhao, Y. (2011). Detecting land-use/land-cover change in rural–urban fringe areas using extended change-vector analysis. International Journal of Applied Earth Observation and Geoinformation, 13(4), 572–585. https://doi.org/10.1016/j.jag.2011.03.002 Heppenstall, A., Malleson, N., & Crooks, A. (2016). “Space, the final Frontier”: How good are agent-based models at simulating individuals and space in cities? Systems, 4(1), 9. https://doi.org/10.3390/systems4010009 Herold, M., Couclelis, H., & Clarke, K. C. (2005). The role of spatial metrics in the analysis and modeling of urban land use change. Computers, Environment and Urban Systems, 29(4), 369–399. https://doi.org/10.1016/j.compenvurbsys.2003.12.001 Jana, A., Bardhan, R., Sarkar, S., & Kumar, V. (2016). Framework to assess and locate affordable and accessible housing for developing nations: Empirical evidences from Mumbai. Habitat International, 57, 88–99. https://doi.org/10.1016/j.habitatint.2016.07.005 Jitendrudu, L. R. B. (2006). Modelling dynamics of urban spatial growth using remote sensing and geographical information system. Andhra University. Retrieved from https://www.iirs.gov.in/iirs/sites/default/files/StudentThesis/thesis_jeetu.pdf Kamusoko, C., Aniya, M., Adi, B., & Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe—Simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435–447. https://doi.org/10.1016/j.apgeog.2008.10.002 Kitada, K., & Fukuyama, K. (2012). Land-use and land-cover mapping using a gradable classification method. Remote Sensing, 4(6), 1544–1558. https://doi.org/10.3390/rs4061544 Kumar, V., Bandhyopadhyay, S., Ramamritham, K., & Jana, A. (2020a). Optimizing the redevelopment cost of urban areas to minimize the fire susceptibility of heterogeneous urban settings in developing nations: A case from Mumbai, India. Process Integration and Optimization for Sustainability, 4(4), 361–378. https://doi.org/10.1007/s41660-020-00124-9 Kumar, V., Jana, A., & Ramamritham, K. (2020b). Simulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: A case of Mumbai India. Geocarto International. https://doi.org/10.1080/10106049.2020.1756463 Kumar, V., Ramamritham, K., & Jana, A. (2019). Resource allocation for handling emergencies considering dynamic variations and urban spaces: Fire fighting in Mumbai. In Proceedings of the tenth international conference on information and communication technologies and development (pp. 1–11). Presented at the ICTD ’19: Tenth International Conference on Information and Communication Technologies and Development, Ahmedabad India: ACM. https://doi.org/10.1145/3287098.3287099 Kumar, V., Ramamritham, K., & Jana, A. (2020c). Effective handling of emergencies in resource constrained urban areas by considering dynamics: A performance analysis. Transportation Research Procedia, 48, 345–362. https://doi.org/10.1016/j.trpro.2020.08.030 Lai, S.-K. (2016). Plan-based decision making for urban complexity. Journal of Urban Management, 5(1), 1–2. https://doi.org/10.1016/j.jum.2016.03.001 Laubenbacher, R., Jarrah, A. S., Mortveit, H. S., & Ravi, S. S. (2012). Agent based modeling, mathematical formalism for. In R. A. Meyers (Ed.), Computational complexity: theory, techniques, and applications (pp. 88–104). New York: Springer. https://doi.org/10.1007/978-1-4614-1800-9_6 Li, X., & Yeh, A. G.-O. (2001). Zoning land for agricultural protection by the integration of remote sensing, GIS, and cellular automata. Photogrammetric Engineering and Remote Sensing, 67(4), 471–478. Li, X., & Yeh, A.G.-O. (2000). Modelling sustainable urban development by the integration of constrained cellular automata and GIS. International Journal of Geographical Information Science, 14(2), 131–152. https://doi.org/10.1080/136588100240886 Liu, D., Zheng, X., & Wang, H. (2020). Land-use simulation and decision-support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecological Modelling, 417, 108924. https://doi.org/10.1016/j.ecolmodel.2019.108924 Macal, C. M., & North, M. J. (2005). Tutorial on agent-based modeling and simulation. In Proceedings of the winter simulation conference, 2005 (pp. 2–15). Presented at the Winter Simulation Conference, 2005, Orlando, FL. IEEE. https://doi.org/10.1109/WSC.2005.1574234 Maithani, S., Arora, M. K., & Jain, R. K. (2010). An artificial neural network based approach for urban growth zonation in Dehradun city, India. Geocarto International, 25(8), 663–681. https://doi.org/10.1080/10106049.2010.524313 Makido, Y., Shandas, V., & Ferwati, S. (2020). Predicting urban growth. In V. Shandas, C. Skelhorn, & S. Ferwati (Eds.), Urban adaptation to climate change: The role of urban form in mediating rising temperatures (pp. 75–92). Cham: Springer. https://doi.org/10.1007/978-3-030-26586-1_6 Maretto, R. V., Assis, T. O., & Gavlak, A. A. (2010). Simulating urban growth and residential segregation through agent-based modeling. In Social simulation (BWSS), 2010 second Brazilian workshop on (pp. 52–57). IEEE. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6030014. Accessed 7 Feb 2015 Mohan, M., Singh, V. K., Bhati, S., Lodhi, N., Sati, A. P., Sahoo, N. R., et al. (2020). Industrial heat island: A case study of Angul-Talcher region in India. Theoretical and Applied Climatology, 141(1–2), 229–246. https://doi.org/10.1007/s00704-020-03181-9 Mondal, B., Chakraborti, S., Das, D. N., Joshi, P. K., Maity, S., Pramanik, M. K., & Chatterjee, S. (2020). Comparison of spatial modelling approaches to simulate urban growth: A case study on Udaipur city, India. Geocarto International, 35(4), 411–433. https://doi.org/10.1080/10106049.2018.1520922 Nath, B., Wang, Z., Ge, Y., Islam, K., Singh, R. P., & Niu, Z. (2020). Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process. ISPRS International Journal of Geo-Information, 9(2), 134. https://doi.org/10.3390/ijgi9020134 Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27–S31. https://doi.org/10.1016/j.jag.2009.11.002 Pan, H., Zhang, L., Cong, C., Deal, B., & Wang, Y. (2019). A dynamic and spatially explicit modeling approach to identify the ecosystem service implications of complex urban systems interactions. Ecological Indicators, 102, 426–436. https://doi.org/10.1016/j.ecolind.2019.02.059 Pouriyeh, A., Lotfi, F. H., & Pirasteh, S. (2021). Vulnerability assessment and modelling of urban growth using data envelopment analysis. Journal of the Indian Society of Remote Sensing, 49(2), 259–273. https://doi.org/10.1007/s12524-020-01206-4 Pramanik, S., & Punia, M. (2020). Land use/land cover change and surface urban heat island intensity: Source–sink landscape-based study in Delhi, India. Environment, Development and Sustainability, 22(8), 7331–7356. https://doi.org/10.1007/s10668-019-00515-0 Prayitno, G. (2020). Land-use prediction in Pandaan District Pasuruan regency. International Journal of GEOMATE, 18(65). https://doi.org/10.21660/2020.65.41738. Punia, M., & Singh, L. (2012). Entropy approach for assessment of urban growth: A case study of Jaipur, INDIA. Journal of the Indian Society of Remote Sensing, 40(2), 231–244. https://doi.org/10.1007/s12524-011-0141-z Rabehi, W., Guerfi, M., Mahi, H., & Rojas-Garcia, E. (2019). Spatiotemporal monitoring of coastal urbanization dynamics: Case study of Algiers’ Bay, Algeria. Journal of the Indian Society of Remote Sensing, 47(11), 1917–1936. https://doi.org/10.1007/s12524-019-01037-y Rawal, D., Gupta, V., & Vyas, A. (2019). Urban development plan using open source geospatial technology—A Case Study Of Ahmedabad. ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W14, pp. 197–201. https://doi.org/10.5194/isprs-archives-XLII-4-W14-197-2019. Reis, S. (2008). Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors, 8(10), 6188–6202. https://doi.org/10.3390/s8106188 Roodposhti, M. S., Aryal, J., & Bryan, B. A. (2019). A novel algorithm for calculating transition potential in cellular automata models of land-use/cover change. Environmental Modelling & Software, 112, 70–81. https://doi.org/10.1016/j.envsoft.2018.10.006 Roy, D., & Lees, M. (2020). Understanding resilience in slums using an agent-based model. Computers, Environment and Urban Systems, 80, 101458. https://doi.org/10.1016/j.compenvurbsys.2019.101458 Saadani, S., Laajaj, R., Maanan, M., Rhinane, H., & Aaroud, A. (2020). Simulating spatial–temporal urban growth of a Moroccan metropolitan using CA–Markov model. Spatial Information Research. https://doi.org/10.1007/s41324-020-00322-0 Shi, L., Taubenböck, H., Zhang, Z., Liu, F., & Wurm, M. (2019). Urbanization in China from the end of 1980s until 2010—spatial dynamics and patterns of growth using EO-data. International Journal of Digital Earth, 12(1), 78–94. https://doi.org/10.1080/17538947.2017.1400599 Shoab, M., Singh, V. K., & Ravibabu, M. V. (2021). High-precise true digital orthoimage generation and accuracy assessment based on UAV images. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-021-01364-z Siddiqui, A., Maithani, S., Jha, A. K., Kumar, P., & Srivastav, S. K. (2018). Urban growth dynamics of an Indian metropolitan using CA Markov and logistic regression. The Egyptian Journal of Remote Sensing and Space Science, 21(3), 229–236. https://doi.org/10.1016/j.ejrs.2017.11.006 Sudhira, H. S., Ramachandra, T. V., & Jagadish, K. S. (2004). Urban sprawl: Metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5(1), 29–39. https://doi.org/10.1016/j.jag.2003.08.002 Van Dyke Parunak, H., Savit, R., & Riolo, R. L. (1998). Agent-based modeling vs. equation-based modeling: A case study and users’ guide. In J. S. Sichman, R. Conte, & N. Gilbert (Eds.), Multi-agent systems and agent-based simulation (Vol. 1534, pp. 10–25). Springer. https://doi.org/10.1007/10692956_2. Wahyudi, A., Liu, Y., & Corcoran, J. (2019). Generating different urban land configurations based on heterogeneous decisions of private land developers: An agent-based approach in a developing country context. ISPRS International Journal of Geo-Information, 8(5), 229. https://doi.org/10.3390/ijgi8050229 Wolfram, S. (1983). Statistical mechanics of cellular automata. Reviews of Modern Physics, 55(3), 601–644. https://doi.org/10.1103/RevModPhys.55.601 Wolfram, S., & Mallinckrodt, A. J. (1995). Cellular automata and complexity. Computers in Physics, 9(55). https://doi.org/10.1063/1.4823369. Xu, T., Gao, J., & Coco, G. (2019). Simulation of urban expansion via integrating artificial neural network with Markov chain—cellular automata. International Journal of Geographical Information Science, 33(10), 1960–1983. https://doi.org/10.1080/13658816.2019.1600701 Zhou, L., Dang, X., Sun, Q., & Wang, S. (2020). Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustainable Cities and Society, 55, 102045. https://doi.org/10.1016/j.scs.2020.102045