References
- Aburas, M.M., et al., 2016. The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: a review. International Journal of Applied Earth Observation and Geoinformation, 52, 380–389. doi:https://doi.org/10.1016/j.jag.2016.07.007
- Ben-Gal, I., et al., 2014. Efficient construction of decision trees by the dual information distance method. Quality Technology & Quantitative Management, 11 (1), 133–147. doi:https://doi.org/10.1080/16843703.2014.11673330
- Breiman, L., et al., 1984. Classification and regression trees. Boca Raton, FL: CRC Press.
- Breiman, L., 1996. Bagging predictors. Machine Learning, 24 (2), 123–140. doi:https://doi.org/10.1007/BF00058655
- Breiman, L., 2001. Random forests. Machine Learning, 45 (1), 5–32. doi:https://doi.org/10.1023/A:1010933404324
- Brochu, E., Cora, V.M., and Freitas, N.D. 2010. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599.
- Caruana, R. and Niculescu-Mizil, A., 2006. An empirical comparison of supervised learning algorithms | machine learning | support vector machine. Available from: https://www.scribd.com/document/113006633/2006-An-Empirical-Comparison-of-Supervised-Learning-Algorithms#.
- Chawla, N.V., et al., 2002. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 341–378. doi:https://doi.org/10.1613/jair.953
- Cohen, J., 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20 (1), 37–46. doi:https://doi.org/10.1177/001316446002000104
- Dai, E., et al., 2005. Modeling change-pattern-value dynamics on land-use: an integrated GIS and artificial neural networks approach. Environmental Assessment, 36 (4), 576–591.
- Ding, C., et al., 2016. A gradient boosting logit model to investigate driver’s stop-or-run behavior at signalized intersections using high-resolution traffic data. Transportation Research Part C: Emerging Technologies, 72, 225–238. doi:https://doi.org/10.1016/j.trc.2016.09.016
- Du, G., et al., 2018. A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area. International Journal of Geographical Information Science, 32 (4), 757–782. doi:https://doi.org/10.1080/13658816.2017.1410550
- Elith, J., Leathwick, J.R., and Hastie, T., 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77 (4), 802–813. doi:https://doi.org/10.1111/j.1365-2656.2008.01390.x
- Etemad-Shahidi, A. and Mahjoobi, J., 2009. Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior. Ocean Engineering, 36 (15–16), 1175–1181. doi:https://doi.org/10.1016/j.oceaneng.2009.08.008
- Friedman, J.H., 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29 (5), 1189–1232. doi:https://doi.org/10.1214/aos/1013203451
- Friedman, J.H., 2002. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38 (4), 367–378. doi:https://doi.org/10.1016/S0167-9473(01)00065-2
- Friedman, J.H. and Meulman, J.J., 2003. Multiple additive regression trees with application in epidemiology. Statistics in Medicine, 22 (9), 1365–1381. doi:https://doi.org/10.1002/sim.1501
- Georganos, S., et al., 2018. Very high resolution object-based land use–land cover urban classification using extreme gradient boosting. IEEE Geoscience And Remote Sensing Letters, 15 (4), 607–611. doi:https://doi.org/10.1109/LGRS.2018.2803259
- Groeneveld, J., et al., 2017. Theoretical foundations of human decision-making in agent-based land use models – a review. Environmental Modelling and Software, 87, 39–48. doi:https://doi.org/10.1016/j.envsoft.2016.10.008
- Guan, Q., Wang, L., and Clarke, K.C., 2005. An artificial-neural-network-based, constrained CA model for simulating urban growth. Cartography and Geographic Information Science, 32 (4), 369–380. doi:https://doi.org/10.1559/152304005775194746
- Hastie, T., Tibshirani, R., and Friedman, J., 2009. The elements of statistical learning. 2nd. New York: Springer.
- Hu, Z. and Lo, C.P., 2007. Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31 (6), 667–688. doi:https://doi.org/10.1016/j.compenvurbsys.2006.11.001
- James, G., et al., 2013. An introduction to statistical learning with applications in R. New York: Springer.
- Kamusoko, C. and 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
- Kavzoglu, T. and Mather, P.M., 2003. The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing, 24 (23), 4907–4938. doi:https://doi.org/10.1080/0143116031000114851
- Krenker, A., Bester, J., and Kos, A., 2011. Introduction to the artificial neural networks. In: K. Suzuki, ed. Artificial neural networks: methodological advances and biomedical applications. PLACE: Publisher, 1–18.
- Landis, J.R. and Koch, G.G., 1977. The measurement of observer agreement for categorical data. Biometrics, 33 (1), 159–174. doi:https://doi.org/10.2307/2529310
- Larivière, B. and den Poel, D.V., 2005. Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems with Applications, 29 (2), 472–484. doi:https://doi.org/10.1016/j.eswa.2005.04.043
- Lee, J., Newman, G., and Park, T., 2018. A comparison of vacancy dynamics between growing and shrinking cities using land transformation model. Sustainability, 10 (5), 1–17.
- Li, X., Liu, X., and Gong, P., 2015. Integrating ensemble-urban cellular automata model with an uncertainty map to improve the performance of a single model. International Journal of Geographical Information Science, 29 (5), 762–785. doi:https://doi.org/10.1080/13658816.2014.997237
- Li, X., Liu, X., and Yu, L., 2014. A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules. International Journal of Geographical Information Science, 28 (7), 1317–1335. doi:https://doi.org/10.1080/13658816.2014.883079
- Li, X. and Yeh, A.G.O., 2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16 (4), 323–343. doi:https://doi.org/10.1080/13658810210137004
- Lopez, E., et al., 2001. Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and Urban Planning, 55 (4), 271–285. doi:https://doi.org/10.1016/S0169-2046(01)00160-8
- Opitz, D.W. and Maclin, R., 1999. Popular ensemble methods: an empirical study. Journal of Artificial Intelligence Research, 11, 169–198. doi:https://doi.org/10.1613/jair.614
- Park, S., et al., 2011. Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea. Landscape and Urban Planning, 99 (2), 104–114. doi:https://doi.org/10.1016/j.landurbplan.2010.09.001
- Pijanowski, B.C., et al., 2000. A land transformation model for the Saginaw Bay Watershed. In: J. Sanderson and L. Harris, eds. Landscape ecology: a top down approach. Boca Raton: Lewis Publishers, 183–198.
- Pijanowski, B.C., 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
- Pijanowski, B.C., et al., 2005. Calibrating a neural network-based urban change model for two metropolitan areas of the Upper Midwest of the United States. International Journal of Geographical Information Science, 19 (2), 197–215. doi:https://doi.org/10.1080/13658810410001713416
- Quinlan, J.R., et al., 1996. Bagging, boosting, and C4. 5. In: Proceedings of the thirteenth national conference on artificial intelligence. Cambridge, MA: AAAI Press/MIT Press, 725–730.
- Rahimi, A., 2016. A methodological approach to urban land-use change modeling using infill development pattern—a case study in Tabriz, Iran. Ecological Processes, 5 (1), 1–15. doi:https://doi.org/10.1186/s13717-016-0044-6
- Reed, R.D. and Marks, R.J., 1998. Neural smithing: supervised learning in feedforward artificial neural networks. Cambridge, MA: MIT Press.
- Rumelhart, D.E., Mcclelland, J.L., and Group, P.D.P., 1986. Parallel distributed processing: explorations in the microstructure of cognition: foundations. London: The MIT Press.
- Saha, D., Alluri, P., and Gan, A., 2015. Prioritizing highway safety manual’s crash prediction variables using boosted regression trees. Accident Analysis and Prevention, 79, 133–144. doi:https://doi.org/10.1016/j.aap.2015.03.011
- Solomatine, D.P. and Xue, Y., 2004. M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai river in China. Journal of Hydrologic Engineering, 9 (6), 491–501.
- Sun, R., et al., 2020. A gradient boosting decision tree based GPS signal reception classification algorithm. Applied Soft Computing Journal, 86 (105942), 1–12. doi:https://doi.org/10.1016/j.asoc.2019.105942
- Svetnik, V., et al., 2003. Random Forest: a Classification and Regression Tool for Compound Classification and QSAR Modeling. Journal of Chemical Information and Computer Sciences, 43 (6), 1947–1958. doi:https://doi.org/10.1021/ci034160g
- Tayyebi, A. and Pijanowski, B.C., 2014. Modeling multiple land use changes using ANN, CART and MARS: comparing tradeoffs in goodness of fit and explanatory power of data mining tools. International Journal of Applied Earth Observation and Geoinformation, 28, 102–116. doi:https://doi.org/10.1016/j.jag.2013.11.008
- Van Vliet, J., et al., 2013. Measuring the neighbourhood effect to calibrate land use models. Computers, Environment and Urban Systems, 41, 55–64. doi:https://doi.org/10.1016/j.compenvurbsys.2013.03.006
- Van Vliet, J., White, R., and Dragicevic, S., 2009. Modeling urban growth using a variable grid cellular automaton. Computers, Environment and Urban Systems, 33 (1), 35–43. doi:https://doi.org/10.1016/j.compenvurbsys.2008.06.006
- Yao, Y., et al., 2017. Investigation on the expansion of urban construction land use based on the CART-CA model. ISPRS International Journal of Geo-Information, 6 (5), 149. doi:https://doi.org/10.3390/ijgi6050149
- Zhang, Y. and Haghani, A., 2015. A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, 308–324. doi:https://doi.org/10.1016/j.trc.2015.02.019