Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

GIScience and Remote Sensing

Volumn 54, Issue 3, 2017, Pages 283-304

  Omrani, Hichem ^a,c   Tayyebi, Amin ^b   Pijanowski, Bryan ^c  

^a LUXEMBOURG INSTITUTE OF SOCIO ECONOMIC RESEARCH LISER   (Luxembourg)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c PURDUE UNIVERSITY   (United States)

Author keywords

arti cial neural networks; cellular automata; land use change; mono label class; multi label classes

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CELLULAR AUTOMATON; INTEGRATED APPROACH; LAND USE CHANGE; MACHINE LEARNING; PARAMETERIZATION;

EID: 85008400451     PISSN: 15481603     EISSN: None     Source Type: Journal    
DOI: 10.1080/15481603.2016.1265706     Document Type: Article

References (57)

1. Akın, A., K. C., Clarke, and S., Berberoglu. 2014. “The Impact of Historical Exclusion on the Calibration of the SLEUTH Urban Growth Model.” International Journal of Applied Earth Observation and Geoinformation 27:156–168. doi:10.1016/j.jag.2013.10.002.
2. Azari, M., A., Tayyebi, M., Helbich, and M. A., Reveshty. 2016. “Integrating Cellular Automata, Artificial Neural Network, and Fuzzy Set Theory to Simulate Threatened Orchards:Application to Maragheh, Iran.” Giscience & Remote Sensing 53 (2):183–205.
3. Bagan, H., and Y., Yamagata. 2015. “Analysis of Urban Growth and Estimating Population Density Using Satellite Images of Nighttime Lights and Land-Use and Population Data.” Giscience & Remote Sensing 52 (6):765–780. doi:10.1080/15481603.2015.1072400.
4. Basse, R. M., H., Omrani, O., Charif, P., Gerber, and K., Bódis. 2014. “Land Use Changes Modelling Using Advanced Methods:Cellular Automata and Artificial Neural Networks. the Spatial and Explicit Representation of Land Cover Dynamics at the Cross-Border Region Scale.” Applied Geography 53:160–171. doi:10.1016/j.apgeog.2014.06.016.
5. Batty, M., 2011. “Modeling and Simulation in Geographic Information Science:Integrated Models and Grand Challenges.” Procedia-Social and Behavioral Sciences 21:10–17. doi:10.1016/j.sbspro.2011.07.003.
6. Blecic, I., A., Cecchini, and G. A., Trunfio. 2013. “Cellular Automata Simulation of Urban Dynamics through GPGPU.” The Journal of Supercomputing 65 (2):614–629. doi:10.1007/s11227-013-0913-z.
7. Boutell, M. R., J., Luo, X., Shen, and C. M., Brown. 2004. “Learning Multi-Label Scene Classification.” Pattern Recognition 37 (9):1757–1771. doi:10.1016/j.patcog.2004.03.009.
8. Brown, D. G., P. H., Verburg, R. G., Pontius, and M. D., Lange. 2013. “Opportunities to Improve Impact, Integration, and Evaluation of Land Change Models.” Current Opinion in Environmental Sustainability 5 (5):452–457. doi:10.1016/j.cosust.2013.07.012.
9. Clarke, K., S., Hoppen, and L., Gaydos. 1997. “A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area.” Environment and Planning B:Planning and Design 24:247–261. doi:10.1068/b240247.
10. Couclelis, H., 2005. “Where Has the Future Gone? Rethinking the Role of Integrated Land-Use Models in Spatial Planning.” Environment and Planning A 37:1353–1371. doi:10.1068/a3785.
11. De Almeida, C. M., M., Batty, A. M. V., Monteiro, G., Câmara, B. S., Soares-Filho, G. C., Cerqueira, and C. L., Pennachin. 2003. “Stochastic Cellular Automata Modeling of Urban Land Use Dynamics:Empirical Development and Estimation.” Computers, Environment and Urban Systems 27 (5):481–509. doi:10.1016/S0198-9715(02)00042-X.
12. Farr, T., P. A., Rosen, E., Caro, R., Crippen, R., Duren, S., Hensley, M., Kobrick, et al. 2007. “The Shuttle Radar Topography Mission.” Reviews of Geophysics 45 (2). doi:10.1029/2005RG000183.
13. Grekousis, G., P., Manetos, and Y. N., Photis. 2013. “Modeling Urban Evolution Using Neural Networks, Fuzzy Logic and GIS:The Case of the Athens Metropolitan Area.” Cities 30:193–203. doi:10.1016/j.cities.2012.03.006.
14. Heaton, J., 2005. Introduction to Neural Networks with Java. Chesterfield:Heaton Research.
15. Hijmans, R. J., S. E., Cameron, J. L., Parra, P. G., Jones, and A., Jarvis. 2005. “Very High Resolution Interpolated Climate Surfaces for Global Land Areas.” International Journal of Climatology 25 (15):1965–1978. doi:10.1002/(ISSN)1097-0088.
16. Huang, B., C., Xie, and R., Tay. 2010. “Support Vector Machines for Urban Growth Modeling.” Geoinformatica 14:83–99. doi:10.1007/s10707-009-0077-4.
17. Lambin, E. F., and H. J. Geist. eds. 2006. Land-Use and Land-Cover Change:Local Processes and Global Impacts, 222 pp. The IGBP Global Change Series. Berlin:Springer-Verlag.
18. Lambin, E. F., B. L., Turner, H. J., Geist, S. B., Agbola, A., Angelsen, J. W., Bruce, O. T., Coomes, T. A., Veldkamp, C., Vogel, J., Xu. 2001. “The Causes of Land-Use and Land-Cover Change:Moving beyond the Myths.” Global Environmental Change 11 (4):261–269. doi:10.1016/S0959-3780(01)00007-3.
19. Li, X., and A., Yeh. 2001. “Calibration of Cellular Automata by Using Neural Networks for the Simulation of Complex Urban Systems.” Environment and Planning A 33:1445–1462. doi:10.1068/a33210.
20. Li, X., and A., Yeh. 2002. “Neural-Network-Based Cellular Automata for Simulating Multiple Land Use Changes Using Gis.” International Journal of Geographical Information Science 16:323–343. doi:10.1080/13658810210137004.
21. Malczewski, J., 2006. “Ordered Weighted Averaging with Fuzzy Quantifiers:GIS-Based Multicriteria Evaluation for Land-Use Suitability Analysis.” International Journal of Applied Earth Observation and Geoinformation 8 (4):270–277. doi:10.1016/j.jag.2006.01.003.
22. Maps. 1999. Land Cover [Map].Scale:1:10.000. Owned by Ministry of Environment Luxembourg.
23. Maps. 2007. Land Cover [Map].Scale:1:10.000. Owned by Ministry of Environment Luxembourg.
24. Mas, J. F., H., Puig, J. L., Palacio, and A., Sosa-López. 2004. “Modelling Deforestation Using GIS and Artificial Neural Networks.” Environmental Modelling & Software 19:461–471. doi:10.1016/S1364-8152(03)00161-0.
25. Mustard, J. F., R. S., Defries, T., Fisher, and E., Moran. 2004. “Land-Use and Land-Cover Change Pathways and Impacts.” In Land Change Science, 411–429. Netherlands:Springer.
26. Olson, J. M., G., Alagarswamy, J. A., Andresen, D. J., Campbell, A. Y., Davis, J., Ge, B. C., Pijanowski, and J., Wang. 2008. “Integrating Diverse Methods to Understand Climate–Land Interactions in East Africa.” Geoforum 39 (2):898–911. doi:10.1016/j.geoforum.2007.03.011.
27. Omrani, H., F., Abdallah, O., Charif, and N. T., Longford. 2015a. “Multi-Label Class Assignment in Land-Use Modelling.” International Journal of Geographical Information Science 29 (6):1023–1041. doi:10.1080/13658816.2015.1008004.
28. Omrani, H., O., Charif, P., Gerber, A., Awasthi, and P., Trigano. 2013. “Prediction of Individual Travel Mode with Evidential Neural Network Model.” Transportation Research Record:Journal of the Transportation Research Board 2399 (2399):1–8. doi:10.3141/2399-01.
29. Omrani, H., A., Tayyebi, and B. C., Pijanowski. 2015b. “Integrating the Multi-Label Land Use Concept and Cellular Automata with the ANN-Based Land Transformation Model.” In Geocomputing Conference, 208–210. Dallas, TX:The University of Texas.
30. Pijanowski, B., D. K., Ray, A. D., Kendall, J. M., Duckles, and D. W., Hyndman. 2007. “Using Back-Cast Land-Use Change and Groundwater Travel Time Models to Generate Land-Use Legacy Maps for Watershed Management.” Ecology and Society 12 (2):25. doi:10.5751/ES-02154-120225.
31. Pijanowski, B. C., K., Alexandridis, and D., Mueller. 2006. “Modelling Urbanization Patterns in Two Diverse Regions of the World.” Journal of Land Use Science 1 (2–4):83–108. doi:10.1080/17474230601058310.
32. Pijanowski, B. C., G., Daniel, S., Brown, and A., Manik. 2002a. “Using Neural Networks and GIS to Forecast Land Use Changes:A Land Trans- Formation Model.” Computers, Environment and Urban Systems 26:553–575. doi:10.1016/S0198-9715(01)00015-1.
33. Pijanowski, B. C., B., Shellito, S., Pithadia, and K., Alexandridis. 2002b. “Forecasting and Assessing the Impact of Urban Sprawl in Coastal Watersheds along Eastern Lake Michigan.” Lakes & Reservoirs:Research & Management 7 (3):271–285. doi:10.1046/j.1440-1770.2002.00203.x.
34. Pijanowski, B. C., A., Tayyebi, M. R., Delavar, and M. J., Yazdanpanah. 2009. “Urban Expansion Simulation Using Geographic Information Systems and Artificial Neural Networks.” International Journal Environment Researcher 3 (4):493–502.
35. Pijanowski, B. C., A., Tayyebi, J., Doucette, B. K., Pekin, D., Braun, and J., Plourde. 2014. “A Big Data Urban Growth Simulation at A National Scale:Configuring the GIS and Neural Network Based Land Transformation Model to Run in A High Performance Computing (HPC) Environment.” Environmental Modelling & Software 51:250–268. doi:10.1016/j.envsoft.2013.09.015.
36. Power, C., A., Simms, and R., White. 2001. “Hierarchical Fuzzy Pattern Matching for the Regional Comparison of Land Use Maps.” International Journal of Geographical Information Science 15 (1):77–100. doi:10.1080/136588100750058715.
37. Ray, D. K., B. C., Pijanowski, A. D., Kendall, and D. W., Hyndman. 2012. “Coupling Land Use and Groundwater Models to Map Land Use Legacies:Assessment of Model Uncertainties Relevant to Land Use Planning.” Applied Geography 34:356–370. doi:10.1016/j.apgeog.2012.01.002.
38. Reed, R. D., and R. J., Marks. 1998. Neural Smithing:Supervised Learning in Feedforward Artificial Neural Networks. Cambridge, MA:MIT Press.
39. Rindfuss, R. R., S. J., Walsh, B. L., Turner, J., Fox, and V., Mishra. 2004. “Developing a Science of Land Change:Challenges and Methodological Issues.” Proceedings of the National Academy of Sciences of the United States of America 101 (39):13976–13981. doi:10.1073/pnas.0401545101.
40. Shan, J., S., Alkheder, and J., Wang. 2008. “Genetic Algorithms for the Calibration of Cellular Automata Urban Growth Modeling.” Photogrammetric Engineering & Remote Sensing 74 (10):1267–1277. doi:10.14358/PERS.74.10.1267.
41. Sorower, M. S., 2010. A Literature Survey on Algorithms for Multi-Label Learning. Corvallis:Oregon State University.
42. Tayyebi, A., B. K., Pekin, B. C., Pijanowski, J. D., Plourde, J. S., Doucette, and D., Braun. 2013. “Hierarchical Modeling of Urban Growth across the Conterminous USA:Developing Meso-Scale Quantity Drivers for the Land Transformation Model.” Journal of Land Use Science 8 (4):422–442. doi:10.1080/1747423X.2012.675364.
43. Tayyebi, A., P. C., Perry, and A. H., Tayyebi. 2014a. “Predicting the Expansion of an Urban Boundary Using Spatial Logistic Regression and Hybrid Raster–Vector Routines with Remote Sensing and GIS.” International Journal of Geographical Information Science 28 (4):639–659. doi:10.1080/13658816.2013.845892.
44. Tayyebi, A., and B. C., Pijanowski. 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:10.1016/j.jag.2013.11.008.
45. Tayyebi, A., B. C., Pijanowski, M., Linderman, and C., Gratton. 2014b. “Comparing Three Global Parametric and Local Non-Parametric Models to Simulate Land Use Change in Diverse Areas of the World.” Environmental Modelling & Software 59:202–221. doi:10.1016/j.envsoft.2014.05.022.
46. Turner, B. L., E. F., Lambin, and A., Reenberg. 2007. “The Emergence of Land Change Science for Global Environmental Change and Sustainability.” Proceedings of the National Academy of Sciences 104 (52):20666–20671. doi:10.1073/pnas.0704119104.
47. Verburg, P. H., W., Soepboer, A., Veldkamp, R., Limpiada, V., Espaldon, and S. S., Mastura. 2002. “Modeling the Spatial Dynamics of Regional Land Use:the CLUE-S Model.” Environmental Management 30 (3):391–405. doi:10.1007/s00267-002-2630-x.
48. Washington-Ottombre, C., B., Pijanowski, D., Campbell, J., Olson, J., Maitima, A., Musili, T., Kibaki, H., Kaburu, P., Hayombe, E., Owango, B., Irigia. 2010. “Using a Role-Playing Game to Inform the Development of Land-Use Models for the Study of a Complex Socio-Ecological System.” Agricultural Systems 103 (3):117–126. doi:10.1016/j.agsy.2009.10.002.
49. Wayland, K. G., D. T., Long, D. W., Hyndman, B. C., Pijanowski, S. M., Woodhams, and S. K., Haack. 2003. “Identifying Relationships between Baseflow Geochemistry and Land Use with Synoptic Sampling and R-Mode Factor Analysis.” Journal of Environment Quality 32 (1):180–190. doi:10.2134/jeq2003.1800.
50. White, R., and G., Engelen. 2000. “High-Resolution Integrated Modelling of the Spatial Dynamics of Urban and Regional Systems.” Computers, Environment and Urban Systems 24:383–400. doi:10.1016/S0198-9715(00)00012-0.
51. White, R., I., Uljee, and G., Engelen. 2012. “Integrated Modelling of Population, Employment and Land-Use Change with a Multiple Activity-Based Variable Grid Cellular Automaton.” International Journal of Geographical Information Science 26:1251–1280. doi:10.1080/13658816.2011.635146.
52. Witten, I. H., and E., Frank. 2005. Data Mining:Practical Machine Learning Tools and Techniques, 2nd ed. San Francisco, CA:Morgan Kaufmann.
53. Wolfram, S., 1994. Cellular Automata and Complexity:Collected Papers (Vol. 1). Reading:Addison-Wesley.
54. Wolfram, S., 2002. A New Kind of Science (Vol. 5). Champaign:Wolfram media.
55. Yang, Q., X., Li, and X., Shi. 2008. “Cellular Automata for Simulating Land Use Changes Based on Support Vector Machines.” Computers & Geosciences 34:592–602. doi:10.1016/j.cageo.2007.08.003.
56. Yang, Q., J., Shao, M., Scholz, C., Boehm, and C., Plant. 2012. “Multi-Label Classification Models for Sustainable flood Retention Basins.” Environmental Modelling & Software 32:27–36. doi:10.1016/j.envsoft.2012.01.001.
57. Zhang, M. L., and Z. H., Zhou. 2006. “Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization.” IEEE Transactions on Knowledge and Data Engineering 18 (10):1338–1351. doi:10.1109/TKDE.2006.162.