- ترابی، محمدامین؛ رحمانی، فائزه؛ قبادی لموکی، تحفه؛ حاجی بابایی، حسین و فانی، مجید. (1400). پیش بینی و کنترل هوشمند چراغ های راهنمایی با استفاده از الگوریتم هوش مصنوعی خودکار یادگیر. مطالعات مدیریت ترافیک، (61 )، 1-36.
- رحمانی، محمد. (1391). کلانشهر ویژگیها و مفاهیم. تهران: انتشارات امید انقلاب.
- سهرابی، محمدرضا. (1392). اصول نگارش مقالات مروری. مجله پژوهشی دانشگاه علوم پزشکی شهید بهشتی، 18 (2)، 56-52.
- شماعی، علی؛ واحدی نژاد، سیدحسین؛ آذرشب، صغری و قاسمی، مسلم. (1401). تحلیل فرایند توسعه کالبدی شهر و ارائه الگوی راهبردی مناسب آن، مطالعه موردی: شهر اهواز. تحقیقات کاربردی علوم جغرافیایی، 22 (67)، 39-25.
- شهسواری، امیر و علم الهدی، جمیله. (1398). روششناسی پژوهش مروری و نقش آن در تولید دانش: توسعه یک گونهشناسی. روش شناسی علوم انسانی، 25(98)، 79-105. doi: 10.30471/mssh.2019.1572
- کریمی ناصری، پرویز. (1375). شهرها، فقر و توسعه. تهران: اداره کل روابط عمومی و بینالملل شهرداری تهران.
- محمدی، حمید و قربی، میترا. (1351). مفاهیم کلیدی در مطالعات شهری و منطقهای. یزد: انتشارات دانشگاه یزد.
- ملبوس باف، رامین و عزیزی، فریدون. (1389). مرور سیستماتیک “Systematic Review” چیست و چگونه نگاشته می شود؟ (مقاله مروری). پژوهش در پزشکی، 34(3)، 203-207.
- Akdeniz, H., Sag, N., & Inam, S. (2022). Analysis of land use/land cover changes and prediction of future changes with land change modeler: Case of Belek, Turkey. Environ Monit Assess 195, 135. https://doi.org/10.1007/s10661-022-10746-w
- Aryal, J., Sitaula, C., & Frery, A. (2023). Land use and land cover (LULC) performance modeling using machine learning algorithms: A case study of the city of Melbourne. Sci Rep 13, 13510. https://doi.org/10.1038/s41598-023-40564-0
- Ashwini, K., Ashwini, K., Briti Sundar, S., Abdulla Al, K., Altuwaijri, H., Nath, H., & Rahaman, Z. (2024). Harnessing machine learning algorithms to model the association between land use/land cover change and heatwave dynamics for enhanced environmental management. Land, 13(8), 1273. https://doi.org/10.3390/land13081273
- Basheer, S., Wang, X., Farooque, A., Nawaz, R., Liu, K., Adekanmbi, T., & Liu, S. (2022). Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques. Remote Sensing, 14(19), 4978. https://doi.org/10.3390/rs14194978
- Bindajam, A., Mallick, J., Talukdar, S., Towfiqul Islam, A., & Alqadhi, S. (2021). Integration of artificial intelligence–based LULC mapping and prediction for estimating ecosystem services for urban sustainability: Past to future perspective. Arabian Journal of Geosciences, 14, 1887. https://doi.org/10.1007/s12517-021-08251-4
- Chang, N.-B., Han, M., Yao, W., Chen, L.-C., & Xu, S. (2010). Change detection of land use and land cover in an urban region with SPOT-5 images and partial Lanczos extreme learning machine. Remote Sensing, 4 (1), https://doi.org/10.1117/1.3518096
- Cheng, J., & Masser, I. (2003). Urban growth pattern modeling. Landscape and Urban Planning, 62(4), 199-217. https://doi.org/10.1016/S0169-2046(02)00150-0
- Cheng, L. L., Tian, C., & Yin, T. T. (2022). Identifying driving factors of urban land expansion using Google Earth Engine and machine learning approaches in Mentougou District, China. Scientific Reports,12, 16248. https://doi.org/10.1038/s41598-022-20478-z.
- Chughtai, A., Abbasi, H., & Karas, I. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22, 100482. https://doi.org/10.1016/j.rsase.2021.100482
- Das, S., & Angadi, D. (2020). Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: A micro-level study. GeoJournal, 87, 2101–2123. https://doi.org/10.1007/s10708-020-10359-1
- Feizizadeh, B., Mohammadzade, K., Lakes, T., Blaschke, T., & Omarzadeh, D. (2021). A comparison of the integrated fuzzy object-based deep learning approach and three machine learning techniques for land use/cover change monitoring and environmental impacts assessment. GIScience & Remote Sensing, 58(8), 1543–1570. https://doi.org/10.1080/15481603.2021.2000350
- Fontana, A., Nascimento, V., Ometto, J., & Fernandes do Amaral, F. (2023). Analysis of past and future urban growth on a regional scale using remote sensing and machine learning. Remote Sensing,4, 1123254. https://doi.org/10.3389/frsen.2023.1123254
- Fu, P., & Weng, Q. (2016). A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175, 205-214. https://doi.org/10.1016/j.rse.2015.12.040
- Gupta, P., Haryani, S., & Gupta, V. (2024). Analysis of land use and land cover change detection for Indore District of Malwa Plateau Region using supervised machine learning. International Journal of Experimental, 38, 154–163. https://doi.org/10.52756/ijerr.2024.v38.014
- Hassan, Z., Shabbir, R., Ahmad, S., Malik, A., Aziz, N., Butt, A., & Erum, S. (2016). Dynamics of land use and land cover change (LULCC) using geospatial techniques: A case study of Islamabad Pakistan. SpringerPlus, 5, 812. https://doi.org/10.1186/s40064-016-2414-z
- He, C., Wei, A., Shia, P., Zhang, Q., & Zhao, Y. (2011). Detecting land-use/land-cover change in rural–urban fringe areas using extended change-vector analysis. Applied Earth Observation and Geoinformation, 13(4), 572-585. https://doi.org/10.1016/j.jag.2011.03.002
- Hongwei, Z., Bingfang, W., Shuai, W., Walter, M., Fuyou, T., Zama Eric, M., ... Mavengahama, S. (2020). A synthesizing land-cover classification method based on Google Earth Engine: A case study in Nzhelele and Levhuvu Catchments, South Africa. Chinese Geographical Science, 30, 397–409. https://doi.org/10.1007/s11769-020-1119-y
- Huang, B., Zhang, L., & Wu, B. (2009). Spatiotemporal analysis of rural urban land conversion. International Journal of Geographical Information Science, 23(3), 379–398. https://doi.org/10.1080/13658810802119685
- Illarionova, S., Tregubova, P., Shukhratov, I., Shadrin, D., Efimov, A., & Burnaev, E. (2024). Advancing forest carbon stocks’ mapping using a hierarchical approach with machine learning and satellite imagery. Scientific Reports, 14, 21032.https://doi.org/10.1038/s41598-024-71133-8
- Kalkhajeh, R., & Jamali, A. (2019). Analysis and predicting the trend of land use/cover changes using neural network and systematic points statistical analysis (SPSA). Journal of the Indian Society of Remote Sensing, 47, 1471–1485. https://doi.org/10.1007/s12524-019-00995-7
- Karimi Naseri, P. (1996). Cities, Poverty, and Development. Tehran: Public Relations and International Affairs Office of Tehran Municipality. [in Persian].
- Karimi, F., Sultana, S., Babakan, A., & Suthaharan, S. (2019). An enhanced support vector machine model for urban expansion prediction. Environment and Urban Systems, 75, 61-75. https://doi.org/10.1016/j.compenvurbsys.2019.01.001
- Li, D., Deng, L., & Cai, Z. (2020). Intelligent vehicle network system and smart city management based on genetic algorithms and image perception. Mechanical Systems and Signal Processing, 141, 106623. https://doi.org/10.1016/j.ymssp.2020.106623Get rights and content
- Li, Y., Li, M., Li, C., & Liu, Z. (2020). Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Scientific Reports, 10, 9952. https://doi.org/10.1038/s41598-020-67024-3
- Loukika, K., Keesara, V., & Sridhar, V. (2021). Analysis of land use and land cover using machine learning algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability.
- Lu, Y., Wu, P., Ma, X., & Li, X. (2019). Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata–Markov model. Environmental Monitoring and Assessment, 191, 68. https://doi.org/10.1007/s10661-019-7200-2
- Malboos Baf, R., & Azizi, F. (2010). What is a systematic review and how is it written? (Review article). Research in Medicine, 34(3), 203–207. [in Persian].
- Marie Gulsrud, N., Raymond, C., Rutt, R., Olafsson, A. S., Plienger, T., Sandberg, M., ... Jonsson, K. (2018). Rage against the machine’? The opportunities and risks concerning the automation of urban green infrastructure. Landscape and Urban Planning, 180, 85-92. https://doi.org/10.1016/j.landurbplan.2018.08.012
- Megahed, Y., Cabral, P., Joel, S., & Caetano, M. (2015). Land cover mapping analysis and urban growth modelling using remote sensing techniques in Greater Cairo Region—Egypt. Geo-Information, 4(3), 1750-1769. https://doi.org/10.3390/ijgi4031750
- Michael, B. (2013). The new science of cities. MIT Press.
- Milojevic, D., & Creutig, F. (2021). Machine learning for geographically differentiated climate change mitigation in urban areas. Mercator Research Institute on Global Commons and Climate Change.
- Mohammadi, H., & Ghorbi, M. (1972). Key Concepts in Urban and Regional Studies. Yazd: Yazd University Press. [in Persian].
- Mutale, B., Withanage, N., Mishra, P., Shen, J., Abdelrahman, K., & Fnais, M. (2024). A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: A case from Lusaka and Colombo. Frontiers in Environmental Science, 12, 1431645 https://doi.org/10.3389/fenvs.2024.1431645
- Nyamekye, C., Kwofie, S., Ghansah, B., Agyapong, E., & Boamah, L. (2020). Assessing urban growth in Ghana using machine learning and intensity analysis: A case study of the New Juaben Municipality. Elsevier.
- Ouma, Y., Nkwae, B., Odirile, P., Parida, B., Anderson, G., & Qi, J. (2022). Comparison of machine learning classifiers for multitemporal and multisensor mapping of urban LULC features. Remote Sensing, 14, 5455–5470. https://doi.org/10.5194/bg-14-5455-2017, 2017
- Patel, S., Verma, P., & Singh, G. (2019). Agricultural growth and land use land cover change in peri-urban India. Environmental Monitoring and Assessment, 91, 600. https://doi.org/10.1007/s10661-019-7736-1
- Rahmani, M. (2012). Metropolis: Characteristics and Concepts. Tehran: Omid Enqelab Publishing. [in Persian].
- Rao, P., Tassinari, P., & Torreggiani, D. (2023). Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data. Heliyon, 9 (8), e18423. https://doi.org/10.1016/j.heliyon.2023.e18423
- Reba, M., & Seto, K. (2020). A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change. Remote Sensing of Environment, 241, 111739. https://doi.org/10.1016/j.rse.2020.111739
- Shahsavari, A., & Alamolhoda, J. (2019). Review research methodology and its role in knowledge production: Developing a typology. Humanities Research Methodology, 25(98), 79–105. https://doi.org/10.30471/mssh.2019.1572[in Persian].
- Shamaei, A., Vahedi Nejad, S. H., Azarshab, S., & Ghasemi, M. (2022). Analysis of the urban physical development process and presentation of an appropriate strategic model: Case study of Ahvaz city. Applied Research in Geographical Sciences, 22(67), 25–39. [in Persian].
- Sohrabi, M. R. (2013). Principles of writing review articles. Research Journal of Shahid Beheshti University of Medical Sciences, 18(2), 52–56. [in Persian].
- Srivastava, A. (2017). Estimation of change in LU/LC mapping with classification of digital signature using AI/ML techniques over Google Earth Engine. Thesis submitted in partial fulfillment for the Award of Degree of Doctor of Philosophy.
- Tariq, A., & Shu, H. (2020). CA-Markov chain analysis of seasonal land surface temperature and land use land cover change using optical multi-temporal satellite data of Faisalabad, Pakistan. Remote Sensing, 12(20), 3402. https://doi.org/10.3390/rs12203402
- Torabi, M. A., Rahmani, F., Ghobadi Lamouki, T., Hajibabaei, H., & Fani, M. (2021). Intelligent prediction and control of traffic lights using self-learning artificial intelligence algorithms. Traffic Management Studies, (61), 1–36. [in Persian].
- Ullah, S., Qiao, X., & Abbas, M. (2024). [Title missing]. Scientific Reports.
- Ullah, Z., AL-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of artificial intelligence and machine learning in smart cities. Computer Communications, 154, 313-323. https://doi.org/10.1016/j.comcom.2020.02.069
- Verma, P., Raghubanshi, A., Srivastava, P., & Raghubanshi, A. (2020). Appraisal of kappa-based metrics and disagreement indices of accuracy assessment for parametric and nonparametric techniques used in LULC classification and change detection. Modeling Earth Systems and Environment,6, 1045–1059. https://doi.org/10.1007/s40808-020-00740-x
- Wang, S., Gebru, B., Lamchin, M., Kayastha, R., & Lee, W.-K. (2020). Land use and land cover change detection and prediction in the Kathmandu District of Nepal using remote sensing and GIS. Sustainability, 12(9), 3925. https://doi.org/10.3390/su12093925
- Zhang, C., & Li, X. (2022). Land use and land cover mapping in the era of big data. Land, 11(10), 1692. https://doi.org/10.3390/land11101692
- Zhang, H., Qi, Z.-F., Ye, X.-Y., Cai, Y.-B., Ma, W.-C., & Chen, M.-N. (2013). Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China. Applied Geography, 44, 121-133. https://doi.org/10.1016/j.apgeog.2013.07.021
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