Page 801 - NGTU_paper_withoutVideo
P. 801

Modern Geomatics Technologies and Applications

                transitions in North-East India,” Comput. Environ. Urban Syst., vol. 59, pp. 38–49, 2016, doi:
                https://doi.org/10.1016/j.compenvurbsys.2016.04.009.
          [2]   B. Shrestha, T. A. Cochrane, B. S. Caruso, and M. E. Arias, “Land use change uncertainty impacts on streamflow and
                sediment projections in areas undergoing rapid development: A case study in the Mekong Basin,” L. Degrad. Dev.,
                vol. 29, no. 3, pp. 835–848, 2018, doi: 10.1002/ldr.2831.
          [3]   Y. A. Bununu, “Integration of Markov chain analysis and similarity-weighted instance-based machine learning
                algorithm (SimWeight) to simulate urban expansion,” Int. J. Urban Sci., vol. 21, no. 2, pp. 217–237, 2017, doi:
                10.1080/12265934.2017.1284607.
          [4]   A. P. Gopi, R. N. S. Jyothi, V. L. Narayana, and K. S. Sandeep, “Classification of tweets data based on polarity using
                improved RBF kernel of SVM,” Int. J. Inf. Technol., 2020, doi: 10.1007/s41870-019-00409-4.
          [5]   Y. Feng, Y. Liu, and M. Batty, “Modeling urban growth with GIS based cellular automata and least squares SVM
                rules: a case study in Qingpu–Songjiang area of Shanghai, China,” Stoch. Environ. Res. Risk Assess., vol. 30, no. 5, pp.
                1387–1400, 2016, doi: 10.1007/s00477-015-1128-z.
          [6]   A. Ansari and M. H. Golabi, “Prediction of spatial land use changes based on LCM in a GIS environment for Desert
                Wetlands – A case study: Meighan Wetland, Iran,” Int. Soil Water Conserv. Res., vol. 7, no. 1, pp. 64–70, 2019, doi:
                https://doi.org/10.1016/j.iswcr.2018.10.001.
          [7]   B. Mondal, G. Dolui, M. Pramanik, S. Maity, S. S. Biswas, and R. Pal, “Urban expansion and wetland shrinkage
                estimation using a GIS-based model in the East Kolkata Wetland, India,” Ecol. Indic., vol. 83, pp. 62–73, 2017, doi:
                https://doi.org/10.1016/j.ecolind.2017.07.037.
          [8]   M. S. Mirakhorlo and M. Rahimzadegan, “Integration of SimWeight and Markov Chain to Predict Land Use of
                Lavasanat Basin,” Numer. Methods Civ. Eng., vol. 2, no. 4, 2018, [Online]. Available: http://nmce.kntu.ac.ir/article-1-
                147-en.html.
          [9]   H. Mirzapour, A. Arshia, and N. Tahmasebipour, “Evaluating the Performance of Geomod Model, SimWeight and
                MLP Algorithms in Urban Development Simulation (Case Study: Khorramabad County),” J. Geomatics Sci. Technol.,
                vol. 9, no. 3, 2020, [Online]. Available: http://jgst.issge.ir/article-1-933-en.html.
          [10]   S. I. Musa, M. Hashim, and M. N. M. Reba, “Urban growth assessment and its impact on deforestation in Bauchi
                metropolis, Nigeria using remote sensing and GIS techniques,” ARPN J. Eng. Appl. Sci., vol. 12, no. 6, pp. 1907–1914,
                2017.
          [11]   N. A. Jamali and M. T. Rahman, “Utilization of Remote Sensing and GIS to Examine Urban Growth in the City of
                Riyadh, Saudi Arabia,” J. Adv. Inf. Technol., no. January, pp. 297–301, 2016, doi: 10.12720/jait.7.4.297-301.
          [12]   Q. Yang, X. Li, and X. Shi, “Cellular automata for simulating land use changes based on support vector machines,”
                Comput. Geosci., vol. 34, no. 6, pp. 592–602, Jun. 2008, doi: 10.1016/j.cageo.2007.08.003.
          [13]   S. S. Gharbia, S. A. Alfatah, L. Gill, P. Johnston, and F. Pilla, “Land use scenarios and projections simulation using an
                integrated GIS cellular automata algorithms,” Model. Earth Syst. Environ., vol. 2, no. 3, p. 151, 2016, doi:
                10.1007/s40808-016-0210-y.
          [14]   Y. Feng, M. Liu, L. Chen, and Y. Liu, “Simulation of Dynamic Urban Growth with Partial Least Squares Regression-
                Based Cellular Automata in a GIS Environment,” ISPRS Int. J. Geo-Information, vol. 5, no. 12, 2016, doi:
                10.3390/ijgi5120243.
          [15]   H. Xie, Y. He, Y. Choi, Q. Chen, and H. Cheng, “Warning of negative effects of land-use changes on ecological
                security based on GIS,” Sci. Total Environ., vol. 704, p. 135427, 2020, doi:
                https://doi.org/10.1016/j.scitotenv.2019.135427.
          [16]   J. [Jokar Arsanjani], M. Helbich, and E. [de N. Vaz], “Spatiotemporal simulation of urban growth patterns using agent-
                based modeling: The case of Tehran,” Cities, vol. 32, pp. 33–42, 2013, doi:
                https://doi.org/10.1016/j.cities.2013.01.005.
          [17]   J. Jokar Arsanjani, M. Helbich, and E. de Noronha Vaz, “Spatiotemporal simulation of urban growth patterns using
                agent-based modeling: The case of Tehran,” Cities, vol. 32, pp. 33–42, 2013, doi:
                https://doi.org/10.1016/j.cities.2013.01.005.
          [18]   H. Keshtkar and W. Voigt, “Potential impacts of climate and landscape fragmentation changes on plant distributions:
                Coupling multi-temporal satellite imagery with GIS-based cellular automata model,” Ecol. Inform., vol. 32, pp. 145–
                155, 2016, doi: https://doi.org/10.1016/j.ecoinf.2016.02.002.
          [19]   A. S. i Mas, “El cadastre: la seva història (1715-1845) i la seva importància com a font documental,” Estud. d’història
                agrària, no. 4, pp. 129–143, 1983, [Online]. Available:
                http://www.raco.cat/index.php/EHA/article/view/99536/125565.
          [20]   S. Hajehforooshnia, A. Soffianian, A. S. Mahiny, and S. Fakheran, “Multi objective land allocation (MOLA) for
                zoning Ghamishloo Wildlife Sanctuary in Iran,” J. Nat. Conserv., vol. 19, no. 4, pp. 254–262, 2011, doi:
                https://doi.org/10.1016/j.jnc.2011.03.001.
          [21]   S. Mustak, N. K. Baghmar, and S. K. Singh, “Prediction of industrial land use using linear regression and mola

                                                                                                              10
   796   797   798   799   800   801   802   803   804