Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges

Koutra, Sesil; Christos Ioakimidis

doi:10.3390/land12010083

Download

Article (Scientific journals)

Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges

Koutra, Sesil; Christos Ioakimidis

2022 • In Land

Peer Reviewed verified by ORBi

Permalink
https://hdl.handle.net/20.500.12907/44131

DOI
10.3390/land12010083

Files (1)Send to Details Statistics Bibliography Similar publications

Files

Full Text

land-12-00083.pdf

Author postprint (6.51 MB)

Download

All documents in ORBi UMONS are protected by a user license.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

case-study analysis; machine learning; urban planning

Abstract :

[en] In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners. As a fast-growing field of Artificial Intelligence, Machine Artificial Intelligence deals with smart designs, data mining and management for complex problem-solving based on experimental data on urban applications (land use and cover, configurations of the built environment and architectural design, etc.), but with few explorations and relevant studies. In this work, a comprehensive and in-depth review is presented to discuss the future opportunities and constraints in meeting the next planning portfolio against the multiple challenges in urban environments in line with Machine Learning progress. Bringing together the theoretical views with practical analyses of cases and examples, the work unveils the huge potential, but also the potential barriers of the complexity of Machine Learning to urban planning strategies.

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

Koutra, Sesil ; Université de Mons - UMONS > Faculté d'Architecture et d'Urbanisme > Service des Projets, Ville et Territoire

Christos Ioakimidis; INTELIGG PC

Language :

English

Title :

Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges

Publication date :

27 December 2022

Journal title :

Land

eISSN :

2073-445X

Publisher :

Multidisciplinary Digital Publishing Institute (MDPI), Basel, Switzerland

Peer reviewed :

Peer Reviewed verified by ORBi

Development Goals :

11. Sustainable cities and communities

Research institute :

Energie

Available on ORBi UMONS :

since 27 December 2022

Statistics

Number of views

231 (9 by UMONS)

Number of downloads

383 (4 by UMONS)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

Jordan M.I. Mitchell T.M. Machine Learning: Trends. perspectives and prospects Science 2015 349 255 260 26185243
Al-Garadi M.A. Mohamed A. Al-Ali A.K. Du X. Guizani M. A survey of machine and deep learning methods for Internet of Things security IEEE Commun. Surv. Tutor. 2020 22 1646 1685 10.1109/COMST.2020.2988293
Bhavsar P. Safro I. Bouaynaya N. Polikar R. Dera D. Machine Learning in Transportation Data Analytics Data Analytics for Intelligent Transportation Systems Elsevier Amsterdam, The Netherlands 2017 283 306
Ross T. The synthesis of intelligence-its implications Psychol. Rev. 1938 45 185 10.1037/h0059815
Choung Y.J. Kim J.M. Study of the relationship between urban expansion and PM10 concentration using multi-temporal spatial datasets and the machine learning technique: Case study fo Daegu South Korea. Appl. Sci. 2019 9 1098 10.3390/app9061098
Fecht D. Beale L. Briggs D. A GIS-based urban simulation model for environmental health analysis Environ. Model. Softw. 2014 58 1 11 10.1016/j.envsoft.2014.03.013
Samuel A.L. Some studies in Machine Learning using the game of checkers IBM J. Res. Dev. 1959 3 210 229 10.1147/rd.33.0210
Li X. Cheng S. Lv Z. Song H. Jia T. Lu N. Data analytics of urban fabric metrics for smart cities Futur. Gener. Comput. Syst. 2020 107 871 882 10.1016/j.future.2018.02.017
Schwab K. The Fourth Industrial Revolution, 1st ed 2016 Available online: https://law.unimelb.edu.au/__data/assets/pdf_file/0005/3385454/Schwab-The_Fourth_Industrial_Revolution_Klaus_S.pdf (accessed on 12 November 2022)
Chaturvedi V. De Vries W. Machine Learning Algorithms for Urban Land Use Planning A review. Urban Sci. 2021 5 68 10.3390/urbansci5030068
Shaikh Hameed P. Mohd Nor N. Nallagownden P. Elamvazuthi I. Ibrahim T. A review on optimized control systems for building energy and comfort management of smart sustainable buildings Renew. Sustain. Energy Rev. 2014 34 409 429 10.1016/j.rser.2014.03.027
Neri E. Coppola F. Miele V. Bibbolino C. Grassi R. Artificial Intelligence: Who is responsible for the diagnosis? Radiol. Med. 2020 125 517 521 10.1007/s11547-020-01135-9
Samardzic-Petrovic M. Kovacevic M. Bajat B. Dragicevic S. Machine learning techniques for modelling short term land-use change. ISPRS Int. J. Geo-Inf. 2017, 6, 387.Batty, M. Big data and the city Built Environ. 2016 42 321 337
Hagenauer J. Omrani H. Helbich M. Assessing the performance of 38 Machine Learning models: The case of land consumption rates in Bavaria. Germany Int. J. Geogr. Inf. Sci. 2019 33 1399 1419 10.1080/13658816.2019.1579333
Robert C. Machine Learning, a Probabilistic Perspective Chance 2014 27 62 63 10.1080/09332480.2014.914768
Gomez J.A. Patino J.E. Duque J.C. Passos S. Spatiotemporal modeling of urban growth using machine learning Remote Sens. 2020 12 109 10.3390/rs12010109
Lim T.S. Loh W.Y. Shih Y.S. A comparison of prediction accuracy. complexity. and training time of thirty-three old and new classification algorithms Mach. Learn. 2000 40 203 228 10.1023/A:1007608224229
Casali Y. Yonca Aydin N. Comes T. Machine learning for spatial analyses in urban areas: A scoping review Sustain. Cities Soc. 2022 85 104050
Guigoz Y. Giuliani G. Nonguierma A. Lehmann A. Mlisa A. Ray N. Spatial data infrastructures in Africa: A gap analysis J. Environ. Inform. 2017 30 53 62 10.3808/jei.201500325
Leyk S. Gaughan A.E. Adamo S.B. Sherbinin A. Deborah B. Sergio F. Amy R. Stevens F.R. Brian B. Charlie F. The spatial allocation of population: A review of large-scale gridded population data products and their fitness for use Earth Syst. Sci. Data 2019 11 1385 1409 10.5194/essd-11-1385-2019
Varshney H. Khna R.A. Khan U. Verma R. Approaches of Artificial Intelligence and Machine Learning in Smart Cities: A critical review IOP Conf. Ser. Mater. Sci. Eng. 2021 1022 012019 10.1088/1757-899X/1022/1/012019
Gao J. O’Neill B.C. Data-driven spatial modeling of global long-term urban land development: The select model Environ. Model. Softw. 2019 119 458 471 10.1016/j.envsoft.2019.06.015
Murphy K. Machine Learning: A Probabilistic Perspective 2012 Available online: http://noiselab.ucsd.edu/ECE228/Murphy_Machine_Learning.pdf (accessed on 12 November 2022)
Fathi S. Srinivasan R. Fenner A. Fathi S. Machine learning applications in urban building energy performance forecasting: A systematic review Renew. Sustain. Energy Rev. 2020 133 110287 10.1016/j.rser.2020.110287
Chen Z. Deng L. Luo Y. Li D. Marcato J. Gonçalves W.N. Nurunnabi A. Li J. Wang C. Li D. Road extraction in remote sensing data: A survey Int. J. Appl. Earth Obs. Geoinf. 2022 112 102833 10.1016/j.jag.2022.102833
Poullis C. You S. Delineation and geometric modeling of road networks ISPRS J. Photogramm. Remote Sens. 2010 65 165 181 10.1016/j.isprsjprs.2009.10.004
Wegner J.D. Montoya-Zegarra J.A. Schindler K. Road networks as collections of minimum cost paths ISPRS J. Photogramm. Remote Sens. 2015 108 128 137 10.1016/j.isprsjprs.2015.07.002
Lu P. Du K. Yu W. Wang R. Deng Y. Balz T. A new region growing-based method for road network extraction and its application on different resolution SAR images IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014 7 4772 4783 10.1109/JSTARS.2014.2340394
Perciano T. Tupin F. Hirata R. Cesar R.M. A two-level markov random field for road network extraction and its application with optical. Sar and multitemporal data Int. J. Remote Sens. 2016 37 3584 3610 10.1080/01431161.2016.1201227
Khesali E. Zoej Valadan M.J. Mokhtarzade M. Dehghani M. Semi automatic road extraction by fusion of high resolution optical and radar images J. Indian Soc. Remote Sens. 2016 44 21 29 10.1007/s12524-015-0480-2
Jochem W.C. Bird T.J. Tatem A.J. Identifying residential neighbourhood types from settlement points in a machine learning approach Comput. Environ. Urban Syst. 2018 69 104 113 10.1016/j.compenvurbsys.2018.01.004
Ma J. Cheng J.C. Jiang F. Chen W. Zhang J. Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques Land Use Policy 2020 94 104537 10.1016/j.landusepol.2020.104537
Novack T. Esch T. Kux H. Stilla U. Machine learning comparison between worldview-2 and quickbird-2-simulated imagery regarding object-based urban land cover classification Remote Sens. 2011 3 2263 2282 10.3390/rs3102263
Shafizadeh-Moghadam H. Asghari A. Tayyebi A. Taleai M. Coupling machine learning. tree-base and statistical models with cellular automata to simulate urban growth Comput. Environ. Urban Syst. 2017 64 297 308 10.1016/j.compenvurbsys.2017.04.002
Koumetio Tekouabou S.C. Diop E.B. Azmi R. Jaligot R. Chenal J. Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems: Potential. issues and challenges J. King Saud Univ. Inf. Sci. 2022 22 5943 5967 10.1016/j.jksuci.2021.08.007
Hernandez Ruiz I.E. Shi W.A. Random forests classification method for urban land-use mapping integrating spatial metrics and texture analysis Int. J. Remote Sens. 2018 39 1175 1198 10.1080/01431161.2017.1395968
Wubie A.M. De Vries W.T. Alemie B.K. A socio-spatial analysis of land-use dynamics and process of land intervention in the peri-urban areas of Bahir Dar City Land 2020 9 445 10.3390/land9110445
Wu E. Silva N. Artificial Intelligence solutions for urban land dynamics: A review J. Plan. Lit. 2010 24 246 265
Abdulijabbar R. Dia H. Liyanage S. Bagloee S.A. Applications of Artificial Intelligence in transport: An overview Sustainability 2019 11 189 10.3390/su11010189
Yigitcanlar T. Kankanamge N. Velia K. How are smart city concepts and technologies perceived and utilized? A systematic Geo-Twitter analysis of smart cities in Australia J. Urban Technol. 2021 28 135 154 10.1080/10630732.2020.1753483
Kamrowska-Zaluska D. Impact of AI-based tool and urban big data analytics on the design and planning of cities Land 2021 10 1209 10.3390/land10111209
Patel A. Crooks A. Koizumi N. Spatial Agent-Based Modeling to Explore Slum Formation Dynamics in Ahmedabad, India GeoComputational Analysis and Modeling of Regional Systems Springer Berlin/Heidelberg, Germany 2018 121 141
Patt T.R. Multiagent approach to temporal and punctual urban development in dynamic, informal contexts Int. J. Archit. Comput. 2018 16 199 211
Grekoussis G. Manetos P. Photis Y.N. Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area Cities 2013 30 193 203 10.1016/j.cities.2012.03.006
Hwang S. Lee Z. Kim J. Real-time pedestrian flow analysis unsing networked sensors for a smart subway system Sustainability 2019 11 6560 10.3390/su11236560
Ibrahim M.R. Haworth J. Cheng T. URBAN-i: From urban scenes to mapping slums. transport modes and pedestrians in cities using deep learning and computer vision Environ. Plan. B Urban Anal. City Sci. 2019 48 76 93 10.1177/2399808319846517
Soltani A. Karimzadeh D. The spatiotemporal modeling of urban growth case study: Mahabad, Iran TeMA J. Land Use Mobil. Environ. 2013 6 189 200
Hao J. Zhu J. Zhong R. The rise of Big Data on urban studies and planning practices in China: Review and open research issues J. Urban Manag. 2015 4 92 124 10.1016/j.jum.2015.11.002
Byon Y.J. Liang S. Real-time transportation mode detection using smartphones and Artificial Neural Networks: Performance Comparisons between smartphones and conventional global positioning system sensors J. Intell. Transp. Syst. Technol. Plan. Oper. 2014 18 264 272 10.1080/15472450.2013.824762
Cheng X.M. Wang J.Y. Li H.F. Zhang Y. Wu L. Liu Y. A method to evaluate task-specific importance of spatiotemporal units based on explainable artificial intelligence Int. J. Geogr. Inf. Sci. 2020 35 2002 2025 10.1080/13658816.2020.1805116
Huang J. Obracht-Prondzynska H. Kamrowska-Zaluska D. The image of the city on social media: A comparative study using ‘Big Data’ and ‘Small Data’ methods in the Tri-city region in Poland Landsc. Urban Plan. 2021 20 103977 10.1016/j.landurbplan.2020.103977
Quand S.J. Park J. Economou A. Lee S. Artificial intelligence-aided design: Smart design for sustainable city development Environ. Plan. B 2019 46 1581 1599
Wang W.J. Wang W. Namgung M. Linking people’s perceptions and physical components of sidewalk environments-an application of rough sets theory Environ. Plan. B 2010 37 234 247 10.1068/b35072
Kwok S.S.K. Lee E.W.M. A study of the importance of occupancy to building cooling load in prediction by intelligent approach Energy Convers. Manag. 2011 52 2555 2564 10.1016/j.enconman.2011.02.002
Wenbin Tien P. Wei S. Darkwa J. Wood C. Calautit J. Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality—A Review Energy AI 2022 10 100198 10.1016/j.egyai.2022.100198
Schoenfeld J. Using Machine Learning to Improve Building Energy Efficiency 2022 Available online: https://www.buildingsiot.com/blog/using-machine-learning-to-improve-building-energy-efficiency-bd (accessed on 30 November 2022)
Amasyali K. El-Gohary N.M. A review of data-driven building energy consumption prediction studies Renew. Sustain. Energy Rev. 2018 81 1192 1205 10.1016/j.rser.2017.04.095
Fan C. Wang J. Gang W. Li S. Assessment of deep recurrent neural network-based strategies for short-term building energy predictions Appl. Energy 2019 236 700 710 10.1016/j.apenergy.2018.12.004
Ahmad M.W. Mourshed M. Mundow D. Sisinni M. Rezgui Y. Building energy metering and environmental monitoring-A state of the art review and directions for future research Energy Build. 2016 120 85 102 10.1016/j.enbuild.2016.03.059
Chammas M. Makhoul A. Demerjian J. An efficient data model for energy prediction using wireless sensors Comput. Electr. Eng. 2019 76 249 257 10.1016/j.compeleceng.2019.04.002
Zhao J. Liu X. A hybrid method of dynamic cooling and heating load forecasting for office buildings based on Artificial Intelligence and regression analysis Energy Build. 2018 174 293 308 10.1016/j.enbuild.2018.06.050
Cheng C.C. Lee D. Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part I. Problem Forumation and the Hypothesis Sensors 2019 19 1131 10.3390/s19051131
Ngaramble J. Yun G.Y. Santamouris M. The use of Artificial Intelligence (AI) methods in the prediction of thermal comfort in buildings: Energy implications of AI-based thermal comfort controls Energy Build. 2020 211 109807 10.1016/j.enbuild.2020.109807
Iddianozie C. Palmes P. Towards smart sustainable cities: Addressing semantic heterogeneity in Building Management Systems using discriminative models Sustain. Cities Soc. 2020 162 102367 10.1016/j.scs.2020.102367
Alanne K. Sierla S. An overview of machine learning applications for smart buildings Sustain. Cities Soc. 2022 76 103445 10.1016/j.scs.2021.103445
RICS Artificial Intelligence: What It Means for the Built Environment? 2017 Available online: https://www.rics.org/uk/news-insight/research/insights/artificial-intelligence-what-it-means-for-the-built-environment/ (accessed on 27 November 2022)
Sacks R. Girolami M. Brilakis I. Building Information Modelling, Artificial Intelligence and Construction Tech Dev. Built Environ. 2020 4 100011 10.1016/j.dibe.2020.100011
Gholizadeh P. Esmaeili P. Goorum B. Diffusion of Building Information Modeling functions in the construction industy J. Manag. Eng. 2018 34 04017060 10.1061/(ASCE)ME.1943-5479.0000589
Maher M.L. Fenves S.J. HI-RISE—An Expert System for the Preliminary Structural Design of High Rise Buildings Knowledge Engineering in Computer-Aided Design North-Holland Amsterdam, The Netherlands 1985
Garrett J. Fenves S.J. A knowledge-based standards processor for structural component design Eng. Comput. 1987 2 219 238 10.1007/BF01276414
Sacks R. Buyukozturk O. Expert interactive design of R/C columns under biaxial bending J. Comput. Civ. Eng. 1987 1 69 81 10.1061/(ASCE)0887-3801(1987)1:2(69)
Al Qady M. Kandil A. Concept relation extraction from construction documents using natural language processing J. Constr. Eng. Manag. 2010 136 294 302 10.1061/(ASCE)CO.1943-7862.0000131
Brilakis I. Soibelman L. Shinagawa Y. Material-based construction site image retrieval J. Comput. Civ. Eng. 2005 19 341 355 10.1061/(ASCE)0887-3801(2005)19:4(341)
Ballard G. The Lean Project Delivery System: An update Lean Constr. J. 2008 1 19
Liu M. Hu Y.M. Li C.L. Landscape metrics for three-dimensional urban building pattern recognition Appl. Geogr. 2017 87 66 72 10.1016/j.apgeog.2017.07.011
Suveg I. Vosselman G. Automatic 3D building reconstruction Three-Dimens. Image Capture Appl. V 2002 4661 59 69
Alexander C. Smith-Voysey S. Jarvis C. Tansey K. Integrating building footprints and LIDAR elevation data to classify roof structures and visualize buildings Comput. Environ. Urban Syst. 2009 33 285 292 10.1016/j.compenvurbsys.2009.01.009
Awrangjeb M. Ravanbakhsh M. Fraser C.S. Automatic detection of residential buildings using LIDAR and multispectral imagery ISPRS. J. Photogramm. Remote Sens. 2010 65 457 467 10.1016/j.isprsjprs.2010.06.001
Khosravi I. Momeni M. Rahnemoonfar M. Performance evaluation of object-based and pixel-based building detection algorithms from very high spatial resolution imagery Photogramm. Eng. Remote Sens. 2014 80 519 528 10.14358/PERS.80.6.519-528
Mohan R. Nevatia R. Using perceptual organization to extract 3-D structures IEEE Trans. Pattern Anal. Mach. Intell. 1989 11 1121 1139 10.1109/34.42852
Weidner U. Digital Surface Models for Building Extraction Automatic Extraction of Man-Made Objects from Aerial and Space Images Springer Berlin/Heidelberg, Germany 1997
Hashem N. Balakrishnan P. Change analysis of land use/land cover and modelling urban growth in greated Doha, Qatar Ann. GIS 2014 21 233 247 10.1080/19475683.2014.992369
Heistermann M. Muller C. Ronneberger K. Land in sight? Achievements, deficits and potentials of continental to global scale land-use modeling Agric. Ecosyst. Environ. 2006 114 141 158 10.1016/j.agee.2005.11.015
Kline J.D. Moses A. Lettman G.J. Azuma D.L. Modeling forest and range land development in rural locations: With examples from Eastern Oregon Landsc. Urban Plan. 2007 80 320 332 10.1016/j.landurbplan.2006.10.017
Schulp C.J. Naburrs G. Verburg P.H. Future carbon sequestration in Europe-effects of land use change Agric. Ecosyst. Environ. 2008 127 251 264 10.1016/j.agee.2008.04.010
Clarke K.C. Gaydos L.J. Loose-coupling a cellular automation model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore Int. J. Geogr. Inf. Sci. 1998 12 699 714 10.1080/136588198241617
Xia C. Gar-On Yeh A. Zhang A. Analyzing spatial relationships between urban land use intensity and urban vitality at street block level: A case study of five Chinese megacities Landsc. Urban Plan. 2020 193 103669 10.1016/j.landurbplan.2019.103669
Liu H. Chen H. Hong R. Liu H. You W. Mapping knowledge structure and research trends of emergency evacuation studies Saf. Sci. 2020 121 348 361 10.1016/j.ssci.2019.09.020
White R. Engelen G. Ulijee I. The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics Environ. Plan. B Plan. Des. 1997 24 323 343 10.1068/b240323
Batty M. Couclelis H. Eichen M. Urban systems as cellular automata Environ. Plan. B Plan. Des. 1997 24 159 164 10.1068/b240159
Liu X. Liang X. Li X. Xu X. Ou J. Chen Y. Li S. Wang S. Pei F. A Future Land-Use model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects Landsc. Urban Plan. 2017 168 94 116 10.1016/j.landurbplan.2017.09.019
Rogan J. Franklin J. Stow D. Miller J. Woodcock C. Roberts D. Mapping land-cover modifications over large areas: A comparison of machine learning algorithms Remote Sens. Environ. 2008 112 2272 2283 10.1016/j.rse.2007.10.004
Heung B. Chak Ho H. Zhang J. Knudgy A. Bulmer C. An overview and comparison of machine learning techniques for classification purposes in digital soil mapping Geoderma 2016 265 62 77 10.1016/j.geoderma.2015.11.014
Omrani H. Parmentier B. Helbich M. Pijanowski B. The land transformation model-cluster framework: Applying k-means and the Spark computing environment for large scale land change analytics Environ. Model. Softw. 2019 111 182 191 10.1016/j.envsoft.2018.10.004
Du G. Shin K.J. A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: The case of Greater Tokyo Area Int. J. Geogr. Inf. Sci. 2018 32 757 782 10.1080/13658816.2017.1410550
Hagenauer J. Helbich M. Local modelling of land consumption in Germany with RegioClust Int. J. Appl. Earth Obs. Geoinf. 2018 65 46 56 10.1016/j.jag.2017.10.003
Veldkamp A. Lambin E.F. Predicting land-use change Agric. Ecosyst. Environ. 2001 85 1 6 10.1016/S0167-8809(01)00199-2
Tayyebi A. Pijanowski B.C. Modeling multiple land use changes using ANN. CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools Int. J. Appl. Earth Obs. Geoinf. 2014 28 102 116 10.1016/j.jag.2013.11.008
Rienow A. Goetzke R. Supporting SLEUTH-Enhancing a cellular automation with support vector machines for urban growth modeling Comput. Environ. Urban Syst. 2015 49 66 81 10.1016/j.compenvurbsys.2014.05.001
Brown D. Verburg P.H. Pontius R. Lange M.D. Opportunities to improve impact. integration and evaluation of land change models Curr. Opin. Environ. Sustain. 2013 5 452 457 10.1016/j.cosust.2013.07.012
Song J. Kim J. Lee J.K. NLP and deep learning-based analysis of building regulations to support automated rule checking system Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC) IAARC Publications Waterloo, ON, Canada 2018
Zhang J. El-Gohary N. Integrating semantic NLP and logic reasoning into a unified system for fully-automated code checking Autom. Constr. 2017 73 45 57 10.1016/j.autcon.2016.08.027
Witten H. Frank E. Data Mining: Practical Machine Learning tools and techniques with java implementations ACM SIGMOD Rec. 2002 31 76 77 10.1145/507338.507355
Nagappan S.D. Mohd Daud S. Machine Learning Predictors for Sustainable Urban Planning Int. J. Adv. Comput. Sci. Appl. 2021 12 1 9 10.14569/IJACSA.2021.0120787
Shafizadeh-Moghadam H. Helbich M. Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai Int. J. Appl. Earth Obs. Geoinf. 2015 35 187 198 10.1016/j.jag.2014.08.013
Karimi F. Sultana S. Babakan Shirzadi A. Suthaharan S. An enhanced support vector machine model for urban expansion prediction Comput. Environ. Urban Syst. 2019 75 61 75 10.1016/j.compenvurbsys.2019.01.001
Mohamed M. Anders A. Schneider C. Monitoring of changes in land use/land cover in Syria from 2010 to 2018 using multitemporal landsat imagery and GIS Land 2020 9 226 10.3390/land9070226
Karakus C.B. The impact of land use/land cover changes on land surface temperature in Sivas city center and its surroundings andd assessment of Urban Heat Island Asia Pacific Dev. J. Atmos. Sci. 2019 55 69 684
Deep S. Saklani A. Urban sprawl modeling using cellular automata Egypt. J. Remote Sens. Sp. Sci. 2014 17 179 187 10.1016/j.ejrs.2014.07.001
Vaz E.N. Nijkamp P. Painho M. Caetano M. A multi-scenario forecast of urban change: A study on urban growth in the algarve Landsc. Urban Plan. 2012 104 201 211 10.1016/j.landurbplan.2011.10.007
Musa S.I. Hashim M. Reba M.N.M. A review of geospatial-based urban growth models and modelling initiatives Geocarto Int. 2016 32 813 833 10.1080/10106049.2016.1213891
Alsharif A.A.A. Pradhan B. Urban sprawl analyis of Tripoli metropolitan city (Libya) using remote sensing data and mutlivariate logistic regression models J. Indian Soc. Remote Sens. 2013 42 149 163 10.1007/s12524-013-0299-7
Tripathy P. Bandopadhyay A. Raman R. Singh S.K. Urban growth modeling using logistic regression and geo-informatics: A case of Jaipur. India Int. J. Sci. Technol. 2018 13 47 62
Motieyan H. Mesgari M.S. An agent-based modeling approach for sustainable urban planning from land use and public transit perspectives Cities 2018 81 91 100 10.1016/j.cities.2018.03.018
Shirzadi Babakan A. Taleai M. Impacts of transport development on residence choice of renter households: An agent-based evaluation Habitat Int. 2015 49 275 285 10.1016/j.habitatint.2015.05.033
Zhang Q. Vatsavai R.R. Shashidharan A. Van Berkel D. Agent-based urban growth modeling framework on apache spark Proceedings of the 5th ACM SIGSPATIAL Internationl Workshop on Analytics for Big Geospatial Data, BigSpatial Association for Computing Machinery New York, NY, USA 2016
Jin H. Mountrakis G. Integration of urban growth modelling products with image-based urban change analysis Int. J. Remote Sens. 2013 66 127 137 10.1080/01431161.2013.791760
Samardzic-Petrovic M. Dragicevic S. Kovacevic M. Bajat B. Modeling urban land use changes using support vector machines Transcactions GIS 2016 20 718 734 10.1111/tgis.12174
Deng Y. Srinivasan S. Urban land use change and regional access: A case study in Beijing. China Habitat Int. 2016 51 103 113 10.1016/j.habitatint.2015.09.007
Samara F. Tampekis S. Sakellariou S. Christopoulou O. Sustainable indicators for land use planning evaluation: The case of a small greek island Proceedings of the 4 th International Conference on Environmental Management, Engineering, Planning and Economics (CEMEPE) Mykonos, Greece 24–28 June 2013
Rahmati O. Golkarian A. Biggs T. Keesstra S. Mohammadi F. Daliakopoulos I.N. Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities J. Environ. Manag. 2019 236 466 480 10.1016/j.jenvman.2019.02.020
Tarabon S. Bergès L. Dutoit T. Isselin-Nondedeu F. Environmental impact assessment of development projects improved by merging species distribution and habitat connectivity modelling J. Environ. Manag. 2019 241 439 449 10.1016/j.jenvman.2019.02.031 30975576
Lin J. Li H. He X. Zhuang Y. Liang Y. Lu S. Estimating potential illegal and development in conservation areas based on a presence-only model J. Environ. Manag. 2022 321 115994 10.1016/j.jenvman.2022.115994 35987053
Li W. Guo Q. A maximum entropy approach to one-class classification of remote sensing imagery Int. J. Remote Sens. 2010 31 2227 2235 10.1080/01431161003702245
Townsend Peterson A. Papes M. Eaton M. Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent Ecography 2007 30 550 560 10.1111/j.0906-7590.2007.05102.x
Cavendish W. Artificial Intelligene and Machine Learning. Human Creativity Augmented by the Immense Power of Machines 2022 Available online: https://www.arup.com/services/digital/artificial-intelligence-and-machine-learning (accessed on 24 November 2022)
Sagris T. Zhao M. Shanghai’s Urban Drainage Masterplan—A Vision for 2030 2022 Available online: https://www.ciwem.org/the-environment/shanghai’s-urban-drainage-masterplan (accessed on 24 November 2022)
Rivers E. Jaynes C. Kimball A. Morrow E. Using case study data to validate 3D Agent-based pedestrian simulation tool for building egress modeling Transp. Res. Procedia 2014 2 123 131 10.1016/j.trpro.2014.09.016
Montjoy V. Powerful Crowd Simulation Software for Human-Centered Design 2022 Available online: https://www.archdaily.com/990775/powerful-crowd-simulation-software-for-human-centered-design (accessed on 30 November 2022)