Benefits of a multiple‐solution approach in land change models

[en] Land change (LC) models are dedicated to a better understanding of land use and land cover dynamics. A fundamental aspect of those models lies in the calibration of spatial parameters underlying such dynamics. Although there are many studies on the calibration of LC models, current efforts have a common goal of seeking to find a single global optimum solution, even though land change dynamics may be inherently heterogeneous throughout a given space. This article presents a calibration approach for finding multiple optimal solutions. A crowding niching genetic algorithm (CNGA) is incorporated into a cellular automata LC model. The model is applied to simulate urban expansion in Wallonia (Belgium) as a case study. Our findings demonstrate the ability of the model to locate multiple solutions simultaneously. In addition, the CNGA performs better than the standard genetic algorithm—besides, the CNGA helps to better understand the properties of land change dynamics within a given landscape.

Research Center/Unit :

LEMA

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

Mustafa, Ahmed Mohamed El Saeid ; Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis)

Ebaid, Amr

Teller, Jacques ; Université de Liège - ULiège > Département ArGEnCo > Urbanisme et aménagement du territoire

Language :

English

Title :

Benefits of a multiple‐solution approach in land change models

Publication date :

2018

Journal title :

Transactions in GIS

ISSN :

1361-1682

eISSN :

1467-9671

Publisher :

Wiley, Oxford, United Kingdom

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://onlinelibrary.wiley.com/doi/full/10.1111/tgis.12482

Name of the research project :

Wal-e-Cities Project

Funders :

The research was funded through the European Regional Development Fund – FEDER.

Available on ORBi :

since 17 October 2018

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Bibliography

Achmad, A., Hasyim, S., Dahlan, B., & Aulia, D. N. (2015). Modeling of urban growth in tsunami-prone city using logistic regression: Analysis of Banda Aceh, Indonesia. Applied Geography, 62, 237–246.
Al-Ahmadi, K., See, L., Heppenstall, A., & Hogg, J. (2009). Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia. Ecological Complexity, 6, 80–101.
Ando, S., Sakuma, J., & Kobayashi, S. (2005). Adaptive isolation model using data clustering for multimodal function optimization. In Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation (pp. 1417–1424). Washington, DC: ACM.
Basse, R. M., Omrani, H., Charif, O., Gerber, P., & Bódis, K. (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.
Belgian Federal Government (2013). Statistics Belgium. Retrieved from https://statbel.fgov.be/fr/statistiques/chiffres/
Chen, C. H., Liu, T. K., & Chou, J. H. (2014). A novel crowding genetic algorithm and its applications to manufacturing robots. IEEE Transactions on Industrial Informatics, 10(3), 1705–1716.
Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment & Planning B, 24, 247–261.
Črepinšek, M., Liu, S.-H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys, 45, 1–35.
Das, S., Maity, S., Qu, B.-Y., & Suganthan, P. N. (2011). Real-parameter evolutionary multimodal optimization: A survey of the state-of-the-art. Swarm Evolutionary Computing, 1, 71–88.
De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Ann Arbor, MI: University of Michigan Press.
Feng, Y., Liu, Y., Tong, X., Liu, M., & Deng, S. (2011). Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape & Urban Planning, 102, 188–196.
Fisher, P., Comber, A., & Wadsworth, R. (2005). Land use and land cover: Contradiction or complement. In P. Fisher & D. Unwin (Eds.), Re-presenting GIS (pp. 85–98). Chichester, UK: John Wiley & Sons.
García, A. M., Santé, I., Boullón, M., & Crecente, R. (2013). Calibration of an urban cellular automaton model by using statistical techniques and a genetic algorithm: Application to a small urban settlement of NW Spain. International Journal of Geographical Information Science, 27, 1593–1611.
Goldberg, D. E., & Richardson, J. (1987). Genetic algorithms with sharing for multimodal function optimization. In J. J. Grefenstette (Ed.) Proceedings of the Second International Conference on Genetic Algorithms and Their Application (pp. 41–49). Hillsdale, NJ: Lawrence: Erlbaum Associates Inc.
Hall, M. (2012). A cumulative multi-niching genetic algorithm for multimodal function optimization. International Journal of Advanced Research in Artificial Intelligence, 1(9), 6–13.
Holland, J. H. (1975). Adaptation in natural and artificial systems. Cambridge, MA: MIT Press.
Li, J. P., Li, X. D., & Wood, A. (2010). Species based evolutionary algorithms for multimodal optimization: A brief review. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1–8). Barcelona, Spain: IEEE.
Liu, Y., Tang, W., He, J., Liu, Y., Ai, T., & Liu, D. (2015). A land-use spatial optimization model based on genetic optimization and game theory. Computers, Environment & Urban Systems, 49, 1–14.
Mahfoud, S. W. (1992). Crowding and preselection revisited. In R. Manner & B. Manderick (Eds.), Parallel problem solving from nature (pp. 27–36). Amsterdam, the Netherlands: Elsevier Science.
Mahfoud, S. W. (1993). Simple analytical models of genetic algorithms for multimodal function optimization. In Proceedings of the 5th International Conference on Genetic Algorithms (p. 643). San Francisco, CA: Morgan Kaufmann Publishers Inc.
Mahiny, A. S., & Clarke, K. C. (2012). Guiding SLEUTH land-use/land-cover change modeling using multicriteria evaluation: Towards dynamic sustainable land-use planning. Environment and Planning B, 39, 925–944.
Mengshoel, O. J., & Goldberg, D. E. (2008). The crowding approach to niching in genetic algorithms. Evolutionary Computing, 16, 315–354.
Mileva, S., Suzana, D., Miloš, K., & Branislav, B. (2015). Modeling urban land use changes using support vector machines. Transactions in GIS, 20, 718–734.
Miller, B. L., & Shaw, M. J. (1996). Genetic algorithms with dynamic niche sharing for multimodal function optimization. In Proceedings of IEEE International Conference on Evolutionary Computation (pp. 786–791). Nagoya, Japan: IEEE.
Mustafa, A., Cools, M., Saadi, I., & Teller, J. (2017). Coupling agent-based, cellular automata and logistic regression into a hybrid urban expansion model (HUEM). Land Use Policy, 69, 529–540.
Mustafa, A., Heppenstall, A., Omrani, H., Saadi, I., Cools, M., & Teller, J. (2018). Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm. Computers, Environment & Urban Systems, 67, 147–156.
Mustafa, A., Rienow, A., Saadi, I., Cools, M., & Teller, J. (2018). Comparing support vector machines with logistic regression for calibrating cellular automata land use change models. European Journal of Remote Sensing, 51(1), 391–401.
Mustafa, A., Saadi, I., Cools, M., & Teller, J. (2015). Modelling uncertainties in long-term predictions of urban growth: A coupled cellular automata and agent-based approach. In Proceedings of the 14th International Conference on Computers in Urban Planning and Urban Management. Cambridge, MA.
Petrowski, A. (1996). A clearing procedure as a niching method for genetic algorithms. In Proceedings of the IEEE International Conference on Evolutionary Computation (pp. 798–803). Nagoya, Japan: IEEE.
Petrowski, A. (1997). An efficient hierarchical clustering technique for speciation. Evry, France: Institute National des Telecommunications.
Pijanowski, B. C., Tayyebi, A., Doucette, J., Pekin, B. K., Braun, D., & Plourde, J. (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 Modeling & Software, 51, 250–268.
Sheng, W., Chen, S., Sheng, M., Xiao, G., Mao, J., & Zheng, Y. (2016). Adaptive multi-subpopulation competition and multi-niche crowding-based memetic algorithm for automatic data clustering. IEEE Transactions on Evolutionary Computing, 20, 838–858.
Singh, G., & Deb, K. (2006). Comparison of multi-modal optimization algorithms based on evolutionary algorithms. In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (pp. 1305–1312). Seattle, WA: ACM.
Tighe, P. J., Fillingim, R. B., & Hurley, R. W. (2014). Geospatial analysis of hospital consumer assessment of healthcare providers and systems pain management experience scores in U.S. hospitals. Pain, 155(5), 1016–1026.
Turner, B. L., Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences of the USA, 104, 20666–20671.
van Vliet, J., Bregt, A. K., Brown, D. G., van Delden, H., Heckbert, S., & Verburg, P. H. (2016). A review of current calibration and validation practices in land-change modeling. Environmental Modeling & Software, 82, 174–182.
Ward, D. P., Murray, A. T., & Phinn, S. R. (2000). A stochastically constrained cellular model of urban growth. Computers, Environment & Urban Systems, 24, 539–558.
White, R., & Engelen, G. (2000). High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, Environment & Urban Systems, 24, 383–400.
Wu, F. (2002). Calibration of stochastic cellular automata: The application to rural–urban land conversions. International Journal of Geographical Information Science, 16, 795–818.
Yang, X., Zheng, X.-Q., & Chen, R. (2014). A land use change model: Integrating landscape pattern indexes and Markov-CA. Ecological Modeling, 283, 1–7.
Yin, X., & Germay, N. (1991). Investigations on solving the load flow problem by genetic algorithms. Electric Power Systems Research, 22, 151–163.
Yin, X., & Germay, N. (1993). A fast genetic algorithm with sharing scheme using cluster analysis methods in multimodal function optimization. In R. F. Albrecht, C. R. Reeves, & N. C. Steele (Eds.), Artificial neural nets and genetic algorithms (pp. 450–457). Vienna, Austria: Springer.