Machine Learning Techniques for Modelling Short Term Land-Use Change

Agarwal, C., Green, G.M., Grove, J.M., Evans, T.P., and Schweik, C.M. (2002). A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time, and Human Choice.

Verburg, 2004, Land use change modelling: Current practice and research priorities, GeoJournal, 61, 309, 10.1007/s10708-004-4946-y

Turner, 2007, The emergence of land change science for global environmental change and sustainability, Proc. Natl. Acad. Sci. USA, 104, 20666, 10.1073/pnas.0704119104

Schneider, 2001, Modeling land-use change in the Ipswich watershed, Massachusetts, USA, Agric. Ecosyst. Environ., 85, 83, 10.1016/S0167-8809(01)00189-X

Verburg, 1999, A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use, Ecol. Model., 116, 45, 10.1016/S0304-3800(98)00156-2

Hu, 2007, Modeling urban growth in Atlanta using logistic regression, Comput. Environ. Urban Syst., 31, 667, 10.1016/j.compenvurbsys.2006.11.001

Muller, 1994, A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada, Landsc. Ecol., 9, 151, 10.1007/BF00124382

Lopez, 2001, Predicting land-cover and land-use change in the urban fringe: A case in Morelia city, Mexico, Landsc. Urban Plan., 55, 271, 10.1016/S0169-2046(01)00160-8

White, 1997, The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics, Environ. Plan. B Plan. Des., 24, 323, 10.1068/b240323

White, 2009, Modeling urban growth using a variable grid cellular automaton, Comput. Environ. Urban Syst., 33, 35, 10.1016/j.compenvurbsys.2008.06.006

Yao, Y., Li, J., Zhang, X., Duan, P., Li, S., and Xu, Q. (2017). Investigation on the Expansion of Urban Construction Land Use Based on the CART-CA Model. ISPRS Int. J. Geo-Inf., 6.

Brown, 2005, Path dependence and the validation of agent-based spatial models of land use, Int. J. Geogr. Inf. Sci., 19, 153, 10.1080/13658810410001713399

Groeneveld, 2017, Theoretical foundations of human decision-making in agent-based land use models—A review, Environ. Model. Softw., 87, 39, 10.1016/j.envsoft.2016.10.008

Tayyebi, 2014, 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., 28, 102

Kamusoko, 2015, Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model, ISPRS Int. J. Geoinf., 4, 447, 10.3390/ijgi4020447

Kjærulff, U.B., and Madsen, A.L. (2008). Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Springer.

Tafazzoli Moghaddam, E. (2011). Data-driven Process Monitoring and Diagnosis with Support Vector Data Description. [Unpulished Master’s Thesis, Simon Fraser University].

Brown, D.G., Band, L.E., Green, K.O., Irwin, E.G., Jain, A., Lambin, E.F., Pontius, R.G., Seto, K.C., Turner, B.L.I., and Verburg, P.H. (2014). Advancing Land Change Modeling: Opportunities and Research Requirements, National Academies Press.

Solomatine, 2008, Data-driven modelling: Some past experiences and new approaches, J. Hydroinform., 10, 3, 10.2166/hydro.2008.015

Naghibi, 2016, GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran, Environ. Monit. Assess., 188, 1, 10.1007/s10661-015-5049-6

Bajat, 2011, Landslide susceptibility assessment using SVM machine learning algorithm, Eng. Geol., 123, 225, 10.1016/j.enggeo.2011.09.006

Pradhan, 2013, A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS, Comput. Geosci.-UK, 51, 350, 10.1016/j.cageo.2012.08.023

Dickson, 2016, Identifying the controls on coastal cliff landslides using machine-learning approaches, Environ. Model. Softw., 76, 117, 10.1016/j.envsoft.2015.10.029

Corani, 2005, Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning, Ecol. Model., 185, 513, 10.1016/j.ecolmodel.2005.01.008

Leuenberger, 2015, Extreme Learning Machines for spatial environmental data, Comput. Geosci.-UK, 85, 64, 10.1016/j.cageo.2015.06.020

Pourtaghi, 2016, Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques, Ecol. Indic., 64, 72, 10.1016/j.ecolind.2015.12.030

Bischof, 1992, Multispectral classification of Landsat-images using neural networks, IEEE Trans. Geosci. Remote Sens., 30, 482, 10.1109/36.142926

Friedl, 1997, Decision tree classification of land cover from remotely sensed data, Remote Sens. Environ., 61, 399, 10.1016/S0034-4257(97)00049-7

Schwert, 2013, A comparison of support vector machines and manual change detection for land-cover map updating in Massachusetts, USA, Remote Sens. Lett., 4, 882, 10.1080/2150704X.2013.809497

Goetz, 2015, Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling, Comput. Geosci.-UK, 81, 1, 10.1016/j.cageo.2015.04.007

Qian, 2015, Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery, Remote Sens., 7, 153, 10.3390/rs70100153

2015, Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines, Ore Geol. Rev., 71, 804, 10.1016/j.oregeorev.2015.01.001

Heung, 2016, An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping, Geoderma, 265, 62, 10.1016/j.geoderma.2015.11.014

Hong, 2016, Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models, Geomorphology, 259, 105, 10.1016/j.geomorph.2016.02.012

Johnson, 2016, Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods, Agric. For. Meteorol., 218, 74, 10.1016/j.agrformet.2015.11.003

Meyer, 2016, Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals, Atmos. Res., 169, 424, 10.1016/j.atmosres.2015.09.021

Yeh, 2003, Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning, Photogramm. Eng. Remote Sens., 69, 1043, 10.14358/PERS.69.9.1043

Almeida, 2008, Using neural networks and cellular automata for modelling intra-urban land-use dynamics, Int. J. Geogr. Inf. Sci., 22, 943, 10.1080/13658810701731168

Pijanowski, 2002, Using neural networks and GIS to forecast land use changes: A land transformation model, Comput. Environ. Urban Syst., 26, 553, 10.1016/S0198-9715(01)00015-1

Weng, Q., and Quattrochi, D.A. (2007). Urban land use prediction model with spatiotemporal data mining and GIS. Urban Remote Sensing, CRC Press, Taylor and Francis Group.

Li, 2004, Data mining of cellular automata’s transition rules, Int. J. Geogr. Inf. Sci., 18, 723, 10.1080/13658810410001705325

Yang, 2008, Cellular automata for simulating land use changes based on support vector machines, Comput. Geosci.-UK, 34, 592, 10.1016/j.cageo.2007.08.003

Okwuashi, 2012, Predicting future land use change using support vector machine based GIS cellular automata: A case of Lagos, Nigeria, J. Sustain. Dev., 5, 132, 10.5539/jsd.v5n5p132

Huang, 2010, Support Vector Machines for urban growth modeling, Geoinformatica, 14, 83, 10.1007/s10707-009-0077-4

Gong, 2015, ART-P-MAP Neural Networks Modeling of Land-Use Change: Accounting for Spatial Heterogeneity and Uncertainty, Geogr. Anal., 47, 376, 10.1111/gean.12077

Qiang, 2015, Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata, Environ. Monit. Assess., 187, 1, 10.1007/s10661-015-4298-8

Liu, H., and Motoda, H. (1998). Feature Selection for Knowledge Discovery and Data Mining, Springer.

Wang, 2003, Feature selection in data mining, Data Mining: Opportunities and Challenges, Volume 3, 80

Bajat, 2015, Exploring the Decision Tree Method for Modelling Urban Land Use Change, Geomatica, 69, 313, 10.5623/cig2015-305

Arango, 2016, Automatic arable land detection with supervised machine learning, Earth Sci. Inform., 9, 535, 10.1007/s12145-016-0270-6

Bradley, 1997, The use of the area under the roc curve in the evaluation of machine learning algorithms, Pattern Recognit., 30, 1145, 10.1016/S0031-3203(96)00142-2

Pontius, 2011, Comparison of three maps at multiple resolutions: A case study of land change simulation in Cho Don District, Vietnam, Ann. Assoc. Am. Geogr., 101, 45, 10.1080/00045608.2010.517742

Gahegan, 2000, On the application of inductive machine learning tools to geographical analysis, Geogr. Anal., 32, 113, 10.1111/j.1538-4632.2000.tb00420.x

Bajat, 2016, Modeling Urban Land Use Changes Using Support Vector Machines, Trans. GIS, 20, 718, 10.1111/tgis.12174

Shannon, 1948, A Mathematical Theory of Communication, Bell Syst. Tech. J., 27, 379, 10.1002/j.1538-7305.1948.tb01338.x

Witten, I.H., Frank, E., and Hall, M.A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers Inc.. [3rd ed.].

Fawcett, 2006, An introduction to ROC analysis, Pattern Recognit. Lett., 27, 861, 10.1016/j.patrec.2005.10.010

Rokach, L., and Maimon, O. (2014). Data Mining with Decision Trees: Theory and Applications, World Scientific. [2nd ed.].

Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and Regression Trees, Chapman and Hall/CRC, Taylor and Francis Groupe.

Quinlan, 1986, Induction of decision trees, Mach. Learn., 1, 81, 10.1007/BF00116251

Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers.

Rosenblatt, 1958, The perceptron: A probabilistic model for information storage and organization in the brain, Psychol. Rev., 65, 386, 10.1037/h0042519

Kohonen, 1982, Self-organized formation of topologically correct feature maps, Biol. Cybern., 43, 59, 10.1007/BF00337288

Broomhead, D.S., and Lowe, D. (1988). Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks, Royal Signals and Radar Establishment Malvern. Available online: http://www.dtic.mil/docs/citations/ADA196234.

Rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0

Jain, 1996, Artificial neural networks: A tutorial, Computer, 29, 31, 10.1109/2.485891

Cristianini, N., and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press.

Vapnik, V.N. (2000). The Nature of Statistical Learning Theory, Springer. [2nd ed.].

Belousov, 2002, Applicational aspects of support vector machines, J. Chemom., 16, 482, 10.1002/cem.744

Abe, S. (2010). Support Vector Machines for Pattern Classification, Springer. [2nd ed.].

Hall, 2009, The WEKA data mining software: An update, SIGKDD Explor., 11, 10, 10.1145/1656274.1656278

(2017, November 27). Weka (2016) Weka 3: Data Mining Software in Java. Available online: https://www.cs.waikato.ac.nz/ml/weka/.

URBEL, Urban Planning Institute of Belgrade (2014, March 07). The Master Plan of Belgrade 2021. Available online: http://www.urbel.com/home.aspx?ID=uzb_Home&LN=ENG.

Bajat, 2013, Dasymetric modelling of population dynamics in urban areas, Geod. Vestn., 57, 777, 10.15292/geodetski-vestnik.2013.04.777-792

Conrad, 2015, System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991, 10.5194/gmd-8-1991-2015

ESRI (2011). Version ArcGIS Desktop: Release 10, Environmental Systems Research Institute.