Article

Multi-Temporal LULC Change and Future Urban Expansion in Faridpur Municipality, Bangladesh: A CA-ANN Based Simulation

Raiyan ORCID , Ebadullah Khan ORCID , Ali Ashrafi Sultana ORCID
    Received: 19 July 2026 | Revised: 31 August 2026 | Accepted: 9 September 2026 | Published Online: 16 September 2026

    Abstract

    The study examines land use and land cover (LULC) conversions and forecasts future urban growth in Faridpur Municipality, Bangladesh. Despite substantial urbanization in recent years, smaller cities such as Faridpur have been largely absent from LULC studies. Using Landsat data from 2004, 2014, and 2024, the research employed Maximum Likelihood Supervised Classification (MLSC) to identify land cover changes. It used an Artificial Neural Network (ANN) with Cellular Automata (CA) to anticipate LULC trends for 2034, 2044, and 2054. Findings indicate that urbanized areas grew from 2.18% in 2004 to 7.31% in 2024, initially replacing vegetation and cropland, and are projected to expand further to 15.04% by 2054. An R2 of 0.9164 represents a strong statistical fit of the plotted built-up-area trend; however, for greenspace, farmland, and water, such expansion into the projected future is not promising. Geospatial analysis suggests the most developed areas are in the northeast and southwest regions, which offer greater connectivity and are the two growth magnets. Validation of the model consistently showed an accuracy of 92.56%, Kappa of 0.878, Kappa (histogram) of 0.9829, and Kappa (location) of 0.8940, which indicates that the model reproduces the observed spatial pattern reasonably well. Thus, the findings suggest that urban planners need to pay attention to trends in sustainable expansion and in the creation of agricultural space. By conveying practical understandings into growth patterns, it supports policymakers, local authorities, and urban planners in monitoring and predicting future urbanization in Faridpur Municipality.

    Keywords

    LULC, Urban Expansion, Modules for Land Change Simulation (MOLUSCE), Prediction, Artificial Neural Network (ANN)

    References
    1. [1] Choudhury, D., Das, K., Das, A., 2019. Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. The Egyptian Journal of Remote Sensing and Space Science. 22(2), 203–218. DOI: https://doi.org/10.1016/j.ejrs.2018.05.004
    2. [2] Arshad, S., Ahmad, S.R., Abbas, S., et al., 2022. Quantifying the contribution of diminishing green spaces and urban sprawl to urban heat island effect in a rapidly urbanizing metropolitan city of Pakistan. Land Use Policy. 113, 105874. DOI: https://doi.org/10.1016/j.landusepol.2021.105874
    3. [3] United Nations Department of Economic and Social Affairs, 2018. 2018 Revision of World Urbanization Prospects. Available from: https://desapublications.un.org/publications/2018-revision-world-urbanization-prospects (cited 10 July 2026).
    4. [4] Chatterjee, A., Chattopadhyay, R.N., 2020. Metropolitan regions of India: Rapid growth, changing landuse and challenges. In Satellite Towns in Neo-Metropolitan Development in India: Lessons from Selected Cities. Springer: Singapore. pp. 87–100. DOI: https://doi.org/10.1007/978-981-15-1502-6_4
    5. [5] Alshehri, F., Abuamarah, B.A., Abd El-Hamid, H.T., 2023. Impact of land use dynamics on land surface temperature using optical remote sensing data integrated with statistical analysis in Riyadh, Saudi Arabia. Advances in Space Research. 72(5), 1739–1750. DOI: https://doi.org/10.1016/j.asr.2023.04.051
    6. [6] Guan, D.J., Li, H.F., Inohae, T., et al., 2011. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling. 222(20–22), 3761–3772. DOI: https://doi.org/10.1016/j.ecolmodel.2011.09.009
    7. [7] Patwary, M.M., Ashraf, S., Shuvo, F.K., 2020. Land use changes and their effects on urban ecosystem services value: A study of Khulna city, Bangladesh. In Proceedings of the 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), Ahmedabad, India, 1–4 December 2020; pp. 62–65. DOI: https://doi.org/10.1109/InGARSS48198.2020.9358927
    8. [8] Hassan, M.M., 2017. Monitoring land use/land cover change, urban growth dynamics and landscape pattern analysis in five fastest urbanized cities in Bangladesh. Remote Sensing Applications: Society and Environment. 7, 69–83. DOI: https://doi.org/10.1016/j.rsase.2017.07.001
    9. [9] Imran, H.M., Hossain, A., Shammas, M.I., et al., 2022. Land surface temperature and human thermal comfort responses to land use dynamics in Chittagong city of Bangladesh. Geomatics, Natural Hazards and Risk. 13(1), 2283–2312. DOI: https://doi.org/10.1080/19475705.2022.2114384
    10. [10] Fenta, A.A., Yasuda, H., Haregeweyn, N., et al., 2017. The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: The case of Mekelle City of northern Ethiopia. International Journal of Remote Sensing. 38(14), 4107–4129. DOI: https://doi.org/10.1080/01431161.2017.1317936
    11. [11] Shao, Z., Sumari, N.S., Portnov, A., et al., 2021. Urban sprawl and its impact on sustainable urban development: A combination of remote sensing and social media data. Geo-Spatial Information Science. 24(2), 241–255. DOI: https://doi.org/10.1080/10095020.2020.1787800
    12. [12] Aljenaid, S.S., Kadhem, G.R., AlKhuzaei, M.F., et al., 2022. Detecting and assessing the spatio-temporal land use land cover changes of Bahrain Island during 1986–2020 using remote sensing and GIS. Earth Systems and Environment. 6(4), 787–802. DOI: https://doi.org/10.1007/s41748-022-00315-z
    13. [13] Moarrab, Y., Salehi, E., Amiri, M.J., et al., 2022. Spatial–temporal assessment and modeling of ecological security based on land-use/cover changes (case study: Lavasanat watershed). International Journal of Environmental Science and Technology. 19(5), 3991–4006. DOI: https://doi.org/10.1007/s13762-021-03534-5
    14. [14] Ahmad, F., Goparaju, L., Qayum, A., 2017. LULC analysis of urban spaces using Markov chain predictive model at Ranchi in India. Spatial Information Research. 25(3), 351–359. DOI: https://doi.org/10.1007/s41324-017-0102-x
    15. [15] Ramadhan, G.F., Hidayati, I.N., 2022. Prediction and simulation of land use and land cover changes using open source QGIS. A case study of Purwokerto, Central Java, Indonesia. Indonesian Journal of Geography. 54(3), 344–351. DOI: https://doi.org/10.22146/ijg.68702
    16. [16] Keshtkar, H., Voigt, W., 2016. A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment. 2(1), 10. DOI: https://doi.org/10.1007/s40808-015-0068-4
    17. [17] Morshed, S.R., Fattah, M.A., Haque, M.N., et al., 2022. Future ecosystem service value modeling with land cover dynamics by using machine learning based artificial neural network model for Jashore city, Bangladesh. Physics and Chemistry of the Earth, Parts A/B/C. 126, 103021. DOI: https://doi.org/10.1016/j.pce.2021.103021
    18. [18] Qiang, Y., Lam, N.S.N., 2015. Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata. Environmental Monitoring and Assessment. 187(3), 57. DOI: https://doi.org/10.1007/s10661-015-4298-8
    19. [19] Khawaldah, H.A., 2016. A prediction of future land use/land cover in Amman area using GIS-based Markov model and remote sensing. Journal of Geographic Information System. 8(3), 412–427. DOI: https://doi.org/10.4236/jgis.2016.83035
    20. [20] Hossain, M.T., Zarin, T., Sahriar, M.R., et al., 2022. Machine learning based modeling for future prospects of land use land cover change in Gopalganj District, Bangladesh. Physics and Chemistry of the Earth, Parts A/B/C. 126, 103022. DOI: https://doi.org/10.1016/j.pce.2021.103022
    21. [21] Rana, M.S., Sarkar, S., 2021. Prediction of urban expansion by using land cover change detection approach. Heliyon. 7(11), e08437. DOI: https://doi.org/10.1016/j.heliyon.2021.e08437
    22. [22] Bangladesh Bureau of Statistics, 2013. District Statistics 2011: Faridpur. Bangladesh Bureau of Statistics: Dhaka, Bangladesh.
    23. [23] Bangladesh Bureau of Statistics, 2022. Population and Housing Census 2022. Bangladesh Bureau of Statistics: Dhaka, Bangladesh.
    24. [24] Rashid, M., Kabir, M.R., Ibne Zaman, M.M., 2023. Padma Bridge in global media and boost to SDGs in Bangladesh. Journalism and Media. 4(2), 445–466. DOI: https://doi.org/10.3390/journalmedia4020029
    25. [25] Sourav, M.M.I., Afnanin, S., Islam, M.R., 2023. Impact of Padma Bridge on transportation system: A case study for Barisal Division. Journal of Engineering and Applied Science. 70(1), 125. DOI: https://doi.org/10.1186/s44147-023-00299-1
    26. [26] CWIS-FSM Support Cell, Department of Public Health Engineering, 2023. SFD (Level 2) Report – Faridpur Municipality, Bangladesh. CWIS-FSM Support Cell, Department of Public Health Engineering: Dhaka, Bangladesh.
    27. [27] Bangladesh Bureau of Statistics, 2023. Statistical Yearbook Bangladesh 2022, 42nd ed. Bangladesh Bureau of Statistics: Dhaka, Bangladesh.
    28. [28] Altuwaijri, H.A., Kafy, A.A., Rahaman, Z.A., et al., 2025. Predicting spatiotemporal dynamics of land use influenced thermal patterns using remote sensing-based machine learning algorithms. Rangeland Ecology & Management. 103, 8–27. DOI: https://doi.org/10.1016/j.rama.2025.06.017
    29. [29] Chakouri, M., Lhissou, R., El Harti, A., et al., 2020. Assessment of the image-based atmospheric correction of multispectral satellite images for geological mapping in arid and semi-arid regions. Remote Sensing Applications: Society and Environment. 20, 100420. DOI: https://doi.org/10.1016/j.rsase.2020.100420
    30. [30] Zhang, Z., He, G., Wang, X., 2010. A practical DOS model-based atmospheric correction algorithm. International Journal of Remote Sensing. 31(11), 2837–2852. DOI: https://doi.org/10.1080/01431160903124682
    31. [31] Abutaleb, K., Ngie, A., Darwish, A., et al., 2015. Assessment of urban heat island using remotely sensed imagery over Greater Cairo, Egypt. Advances in Remote Sensing. 4(1), 35–47. DOI: https://doi.org/10.4236/ars.2015.41004
    32. [32] Arekhi, M., Goksel, C., Balik Sanli, F., et al., 2019. Comparative evaluation of the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for Igneada Longos Forest. ISPRS International Journal of Geo-Information. 8(2), 56. DOI: https://doi.org/10.3390/ijgi8020056
    33. [33] Huang, X., Huang, J., Wen, D., et al., 2021. An updated MODIS global urban extent product (MGUP) from 2001 to 2018 based on an automated mapping approach. International Journal of Applied Earth Observation and Geoinformation. 95, 102255. DOI: https://doi.org/10.1016/j.jag.2020.102255
    34. [34] Jamei, Y., Rajagopalan, P., Sun, Q., 2019. Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Science of the Total Environment. 659, 1335–1351. DOI: https://doi.org/10.1016/j.scitotenv.2018.12.308
    35. [35] Chughtai, A.H., Abbasi, H., Karas, I.R., 2021. A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment. 22, 100482. DOI: https://doi.org/10.1016/j.rsase.2021.100482
    36. [36] Foody, G.M., 2020. Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sensing of Environment. 239, 111630. DOI: https://doi.org/10.1016/j.rse.2019.111630
    37. [37] Gašparović, M., Jogun, T., 2018. The effect of fusing Sentinel-2 bands on land-cover classification. International Journal of Remote Sensing. 39(3), 822–841. DOI: https://doi.org/10.1080/01431161.2017.1392640
    38. [38] Ahmad, M.N., Shao, Z., Javed, A., 2023. Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data. Environmental Science and Pollution Research. 30(12), 32985–33001. DOI: https://doi.org/10.1007/s11356-022-24442-2
    39. [39] Choudhary, P., Devatha, C.P., Azhoni, A., 2025. Integration of multi-layer perceptron neural network and cellular automata-Markov chain approach for the prediction of land use land cover in land change modeler. Ecological Modelling. 506, 111162. DOI: https://doi.org/10.1016/j.ecolmodel.2025.111162
    40. [40] Sisay, G., Gessesse, B., Fürst, C., et al., 2023. Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia. Heliyon. 9(9), e20088. DOI: https://doi.org/10.1016/j.heliyon.2023.e20088
    41. [41] Uddin, M.S., Mahalder, B., Mahalder, D., 2023. Assessment of land use land cover changes and future predictions using CA-ANN simulation for Gazipur City Corporation, Bangladesh. Sustainability. 15(16), 12329. DOI: https://doi.org/10.3390/su151612329
    42. [42] Al Kafy, A., Rahman, M.S., Al Faisal, A., et al., 2020. Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sensing Applications: Society and Environment. 18, 100314. DOI: https://doi.org/10.1016/j.rsase.2020.100314
    43. [43] Zhou, L., Dang, X., Mu, H., et al., 2021. Cities are going uphill: Slope gradient analysis of urban expansion and its driving factors in China. Science of the Total Environment. 775, 145836. DOI: https://doi.org/10.1016/j.scitotenv.2021.145836
    44. [44] Mas, J.-F., Kolb, M., Paegelow, M., et al., 2014. Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software. 51, 94–111. DOI: https://doi.org/10.1016/j.envsoft.2013.09.010
    45. [45] Kamaraj, M., Rangarajan, S., 2022. Predicting the future land use and land cover changes for Bhavani basin, Tamil Nadu, India, using QGIS MOLUSCE plugin. Environmental Science and Pollution Research. 29(57), 86337–86348. DOI: https://doi.org/10.1007/s11356-021-17904-6
    46. [46] Chen, F., Yang, S., Yin, K., et al., 2017. Challenges to quantitative applications of Landsat observations for the urban thermal environment. Journal of Environmental Sciences. 59, 80–88. DOI: https://doi.org/10.1016/j.jes.2017.02.009
    47. [47] Li, P., Jiang, L., Feng, Z., 2014. Cross-comparison of vegetation indices derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. Remote Sensing. 6(1), 310–329. DOI: https://doi.org/10.3390/rs6010310
    48. [48] Xu, D., Guo, X., 2014. Compare NDVI extracted from Landsat 8 imagery with that from Landsat 7 imagery. American Journal of Remote Sensing. 2(2), 10–14. DOI: https://doi.org/10.11648/j.ajrs.20140202.11
    49. [49] Roy, D.P., Li, Z., Zhang, H.K., et al., 2020. A conterminous United States analysis of the impact of Landsat 5 orbit drift on the temporal consistency of Landsat 5 Thematic Mapper data. Remote Sensing of Environment. 240, 111701. DOI: https://doi.org/10.1016/j.rse.2020.111701
    50. [50] Markham, B., Storey, J., Morfitt, R., 2015. Landsat-8 sensor characterization and calibration. Remote Sensing. 7(3), 2279–2282. DOI: https://doi.org/10.3390/rs70302279
    51. [51] Yin, G., Mariethoz, G., McCabe, M.F., 2017. Gap-filling of Landsat 7 imagery using the direct sampling method. Remote Sensing. 9(1), 12. DOI: https://doi.org/10.3390/rs9010012
    52. [52] Al-Wassai, F.A., Kalyankar, N.V., 2013. Major limitations of satellite images. Journal of Global Research in Computer Science. 4(5), 51–59.
    53. [53] Kadhim, N., Mourshed, M., Bray, M., 2016. Advances in remote sensing applications for urban sustainability. Euro-Mediterranean Journal for Environmental Integration. 1(1), 7. DOI: https://doi.org/10.1007/s41207-016-0007-4
    54. [54] Moroni, S., Chiffi, D., 2022. Uncertainty and planning: Cities, technologies and public decision-making. Perspectives on Science. 30(2), 237–259. DOI: https://doi.org/10.1162/posc_a_00413
    55. [55] Reimuth, A., Hagenlocher, M., Yang, L.E., et al., 2024. Urban growth modeling for the assessment of future climate and disaster risks: Approaches, gaps and needs. Environmental Research Letters. 19(1), 013002. DOI: https://doi.org/10.1088/1748-9326/ad1082
    56. [56] Akiner, M.E., Ghasri, M., 2025. Integrating remote sensing and machine learning to analyze urban growth and its environmental effects: A 30-year assessment in Başakşehir, Turkey. Pure and Applied Geophysics. 182(6), 2507–2531. DOI: https://doi.org/10.1007/s00024-025-03727-w
    57. [57] Basheer, S., Wang, X., Farooque, A.A., et al., 2022. Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques. Remote Sensing. 14(19), 4978. DOI: https://doi.org/10.3390/rs14194978
    58. [58] Jokar Arsanjani, J., Helbich, M., Kainz, W., et al., 2013. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation. 21, 265–275. DOI: https://doi.org/10.1016/j.jag.2011.12.014
    59. [59] Seto, K.C., Güneralp, B., Hutyra, L.R., 2012. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences of the United States of America. 109(40), 16083–16088. DOI: https://doi.org/10.1073/pnas.1211658109
    60. [60] Ahmed, B., Ahmed, R., 2012. Modeling urban land cover growth dynamics using multi-temporal satellite images: A case study of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information. 1(1), 3–31. DOI: https://doi.org/10.3390/ijgi1010003
    61. [61] Bhat, P.A., Shafiq, M.U., Mir, A.A., et al., 2017. Urban sprawl and its impact on landuse/land cover dynamics of Dehradun City, India. International Journal of Sustainable Built Environment. 6(2), 513–521. DOI: https://doi.org/10.1016/j.ijsbe.2017.10.003
    62. [62] United Nations Human Settlements Programme, 2020. World Cities Report 2020: The Value of Sustainable Urbanization. United Nations Human Settlements Programme: Nairobi, Kenya.
    63. [63] Nasar-u-Minallah, M., Zia, S., Rahman, A.U., et al., 2021. Spatio-temporal analysis of urban expansion and future growth patterns of Lahore, Pakistan. Geography, Environment, Sustainability. 14(3), 41–53. DOI: https://doi.org/10.24057/2071-9388-2020-215
    64. [64] Raja, D.R., 2012. Spatial analysis of land surface temperature in Dhaka Metropolitan Area. Journal of Bangladesh Institute of Planners. 5, 151–167.
    65. [65] Firman, T., 2009. The continuity and change in mega-urbanization in Indonesia: A survey of Jakarta–Bandung Region (JBR) development. Habitat International. 33(4), 327–339. DOI: https://doi.org/10.1016/j.habitatint.2008.08.005
    66. [66] Kumar, G., Vyas, S., Sharma, S.N., et al., 2025. Urban growth prediction using CA-ANN model and spatial analysis for planning policy in Indore city, India. GeoJournal. 90(3), 139. DOI: https://doi.org/10.1007/s10708-025-11393-7
    67. [67] Renita, E., Hidayati, I.N., 2024. Use of remote sensing and geographic information system for the analysis of urban development: A case study of Banyumas Regency, Indonesia. Indonesian Journal of Geography. 56(1), 24–32. DOI: https://doi.org/10.22146/ijg.79086
    68. [68] Ismaeel, W.A., Kumar, J.S., 2025. Analysis and prediction of land use land cover dynamics for Northwest Syria using artificial neural network model. Earth Systems and Environment. 9(3), 2257–2276. DOI: https://doi.org/10.1007/s41748-025-00702-2
    69. [69] 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. DOI: https://doi.org/10.3390/ijgi2030577
    70. [70] Taubenböck, H., Esch, T., Felbier, A., et al., 2012. Monitoring urbanization in mega cities from space. Remote Sensing of Environment. 117, 162–176. DOI: https://doi.org/10.1016/j.rse.2011.09.015
    71. [71] Guttikunda, S.K., Goel, R., 2013. Health impacts of particulate pollution in a megacity—Delhi, India. Environmental Development. 6, 8–20. DOI: https://doi.org/10.1016/j.envdev.2012.12.002
    72. [72] Rahaman, M.A., Kalam, A., Al-Mamun, M., 2023. Unplanned urbanization and health risks of Dhaka City in Bangladesh: Uncovering the associations between urban environment and public health. Frontiers in Public Health. 11, 1269362. DOI: https://doi.org/10.3389/fpubh.2023.1269362
    73. [73] Bhatta, B., 2010. Urban growth and sprawl. In Analysis of Urban Growth and Sprawl from Remote Sensing Data. Springer: Berlin/Heidelberg, Germany. pp. 1–16. DOI: https://doi.org/10.1007/978-3-642-05299-6_1
    74. [74] 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. DOI: https://doi.org/10.1016/j.jag.2003.08.002
    75. [75] Murtadho, A., Pravitasari, A.E., Munibah, K., et al., 2022. Controlling the urban physical development in Karawang and Purwakarta Regencies using quantitative zoning approach. Indonesian Journal of Geography. 54(2), 272–279. DOI: https://doi.org/10.22146/ijg.70358
    76. [76] Dewan, A.M., Yamaguchi, Y., 2009. Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography. 29(3), 390–401. DOI: https://doi.org/10.1016/j.apgeog.2008.12.005
    77. [77] Angel, S., Parent, J., Civco, D.L., et al., 2011. The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Progress in Planning. 75(2), 53–107. DOI: https://doi.org/10.1016/j.progress.2011.04.001
    78. [78] Almeida, C.M., Gleriani, J.M., Castejon, E.F., et al., 2008. Using neural networks and cellular automata for modelling intra-urban land-use dynamics. International Journal of Geographical Information Science. 22(9), 943–963. DOI: https://doi.org/10.1080/13658810701731168
    79. [79] Kamusoko, C., Gamba, J., 2015. Simulating urban growth using a random forest-cellular automata (RF-CA) model. ISPRS International Journal of Geo-Information. 4(2), 447–470. DOI: https://doi.org/10.3390/ijgi4020447
    80. [80] Liu, Y., 2012. Modelling sustainable urban growth in a rapidly urbanising region using a fuzzy-constrained cellular automata approach. International Journal of Geographical Information Science. 26(1), 151–167. DOI: https://doi.org/10.1080/13658816.2011.577434
    81. [81] Pijanowski, B.C., Brown, D.G., Shellito, B.A., et al., 2002. Using neural networks and GIS to forecast land use changes: A Land Transformation Model. Computers, Environment and Urban Systems. 26(6), 553–575. DOI: https://doi.org/10.1016/s0198-9715(01)00015-1
    82. [82] Tayyebi, A., Pijanowski, B.C., Tayyebi, A.H., 2011. An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran. Landscape and Urban Planning. 100(1–2), 35–44. DOI: https://doi.org/10.1016/j.landurbplan.2010.10.007
    83. [83] Chen, Y., Li, X., Liu, X., et al., 2014. Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. International Journal of Geographical Information Science. 28(2), 234–255. DOI: https://doi.org/10.1080/13658816.2013.831868
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