Genetic algorithm supported by expert system to solve land redistribution problem
Corresponding Author
Hüseyin Haklı
Department of Computer Engineering, Necmettin Erbakan University, Konya, Turkey
Correspondence
Hüseyin Haklı, Department of Computer Engineering, Necmettin Erbakan University, Konya 42090, Turkey.
Email: [email protected]
Search for more papers by this authorHarun Uğuz
Department of Computer Engineering, Selcuk University, Konya, Turkey
Search for more papers by this authorTayfun Çay
Department of Geomatics Engineering, Selcuk University, Konya, Turkey
Search for more papers by this authorCorresponding Author
Hüseyin Haklı
Department of Computer Engineering, Necmettin Erbakan University, Konya, Turkey
Correspondence
Hüseyin Haklı, Department of Computer Engineering, Necmettin Erbakan University, Konya 42090, Turkey.
Email: [email protected]
Search for more papers by this authorHarun Uğuz
Department of Computer Engineering, Selcuk University, Konya, Turkey
Search for more papers by this authorTayfun Çay
Department of Geomatics Engineering, Selcuk University, Konya, Turkey
Search for more papers by this authorAbstract
Land redistribution, a real-world optimization problem, involves the distribution of land parcels in predetermined blocks based on the landowners' preferences. This process, measured in weeks or months, is usually performed manually by a technician with the support of computer software. Although various techniques have been developed in recent years to solve this complex problem, they all require improvement. This study aimed to develop a new technique and produce applicable redistribution plans using a genetic algorithm (GA) in combination with an expert system. Blocks of cadastral parcels were determined by a GA using a new objective function to consider the overflow and residual areas as well as the landowners' preferences. The expert system was employed to close (reduce to zero) the overflow or residual areas occurring after the GA distribution. To investigate the performance of the proposed method, the system was used on a real study area and the results were compared against those obtained for the same cadastral situation undertaken by a technician using a similar method from published literature. The experimental results showed that the method proposed in this study performed better than the other methods because it provided a successful and applicable redistribution plan for the study area in a much shorter time.
CONFLICTS OF INTEREST
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
REFERENCES
- Dijk Van, T. (2002). Central European land fragmentation in the years to come—A scenario study into the future need for land consolidation in Central Europé FIG XXII International Congress. Washington, D.C..
- Akkus, M. A., Karagoz, O. & Dulger, O. (2012) Automated land reallotment using genetic algorithm. Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium on. Trabzon, Turkey.
- Arsene, O., Dumitrache, L., & Mihu, I. (2015). Expert system for medicine diagnosis using software agents. Expert Systems with Applications, 42(4), 1825–1834.
- Aslan, S. T. A., Kirmikil, M., Giindogdu, K. S., & Arici, I. (2018). Reallocation model for land consolidation based on landowners' requests. Land Use Policy, 70, 463–470.
- Avci, M. (1999) A new approach oriented to new reallotment model based on block priority method in land consolidation. Tr Journal of Agriculture and Forestry, 23, 451–457.
- Ayranci, Y. (2007). Re-allocation aspects in land consolidation: A new model and its applications. Journal of Agronomy, 6(2), 270–277.
10.3923/ja.2007.270.277 Google Scholar
- Belciugi, S., & Gorunescu, F. (2013). A hybrid neural network/genetic algorithm applied to breastcancer detection and recurrence. Expert Systems, 30(3), 243–254.
- Cay, T., & Iscan, F. (2006). Optimization in land consolidation. Munich, Germany: XXIII FIG Congress.
- Cay, T., & Iscan, F. (2011). Fuzzy expert system for land reallocation in land consolidation. Expert Systems with Applications, 38(9), 11055–11071.
- Cay, T., & Uyan, M. (2013). Evaluation of reallocation criteria in land consolidation studies using the Analytic Hierarchy Process (AHP). Land Use Policy, 30(1), 541–548.
- Changchit, C. (2008). Expert systems. In L. A. Tomei (Ed.), Encyclopedia of information technology curriculum integration. IGI Global
10.4018/978-1-59904-881-9.ch053 Google Scholar
- Demetriou, D. (2016). The assessment of land valuation in land consolidation schemes: The need for a new land valuation framework. Land Use Policy, 54, 487–498.
- Demetriou, D., See, L., & Stillwell, J. (2013). A spatial genetic algorithm for automating land partitioning. International Journal of Geographical Information Science, 27(12), 2391–2409.
- Demetriou, D., Stillwell, J., & See, L. (2012a). An integrated planning and decision support system (IPDSS) for land consolidation: Theoretical framework and application of the land-redistribution modules. Environment and Planning B-Planning & Design, 39(4), 609–628.
- Demetriou, D., Stillwell, J., & See, L. (2012b). Land consolidation in Cyprus: Why is an integrated planning and decision support system required? Land Use Policy, 29(1), 131–142.
- Durkin, J. (1990). Application of expert systems in the sciences. Ohio Journal of Science, 90(5), 171–179.
- Ertunc, E., Cay, T., & Hakli, H. (2018). Modeling of reallocation in land consolidation with a hybrid method. Land Use Policy, Article in Press.
10.1016/j.landusepol.2018.03.003 Google Scholar
- Essadiki, M., Ettarid, M. & Robert, P. (2003) Optimisation of technical steps of a rural land consolidation using a geographic information system: Land reallocation step. FIG Working Week 2003. Paris, France.
- Fang, S. F., Zhu, Y. Q., Xu, L. D., Zhang, J. Q., Zhou, P. J., Luo, K., & Yang, J. (2017). An integrated system for land resources supervision based on the IoT and cloud computing. Enterprise Information Systems, 11(1), 105–121.
- Fernandes, D. R. M., Rocha, C., Aloise, D., Ribeiro, G. M., Santos, E. M., & Silva, A. (2014). A simple and effective genetic algorithm for the two-stage capacitated facility location problem. Computers & Industrial Engineering, 75, 200–208.
- Gao, Q., Xu, L. D., & Liang, N. (2001). Dynamic modelling with an integrated ecological knowledge-based system. Knowledge-Based Systems, 14(5–6), 281–287.
- Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison Wesley.
- Hakli, H., & Uguz, H. (2017). A novel approach for automated land partitioning using genetic algorithm. Expert Systems with Applications, 82, 10–18.
- Hakli, H., Uguz, H., & Cay, T. (2016). A new approach for automating land partitioning using binary search and Delaunay triangulation. Computers and Electronics in Agriculture, 125, 129–136.
- Holland, J. H. (1975). Adaption in natural and artificial systems. The University of Michigan Press.
- Hu, F. Y., Yang, S. N., & Xu, W. (2014). A non-dominated sorting genetic algorithm for the location and districting planning of earthquake shelters. International Journal of Geographical Information Science, 28(7), 1482–1501.
- Inceyol, Y. (2014) Application of genetic algorithm in land consolidation activities. Geomatics Engineering. The Graduate School of Natural and Applied Science of Selcuk University.
- Ipek, M., Selvi, I. H., Findik, F., Torkul, O., & Cedimoglu, I. H. (2013). An expert system based material selection approach to manufacturing. Materials & Design, 47, 331–340.
- Ishikawa, S., Kubota, R., & Horio, K. (2015). Effective hierarchical optimization by a hierarchical multi-space competitive genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications, 42(24), 9434–9440.
- Jansen, L. J. M., Karatas, M., Küsek, G., Lemmen, C. & Wouters, R. (2010) The computerised land re-allotment process in Turkey and the Netherlands in multi-purpose land consolidation projects. FIG Congress 2010. Sydney, Australia.
- Jun, S., & Park, J. (2015). A hybrid genetic algorithm for the hybrid flow shop scheduling problem with nighttime work and simultaneous work constraints: A case study from the transformer industry. Expert Systems with Applications, 42(15–16), 6196–6204.
- Kou, G., Ergu, D., & Shi, Y. (2014). An integrated expert system for fast disaster assessment. Computers & Operations Research, 42, 95–107.
- Lau, H. C. W., Ho, G. T. S., Zhao, Y., & Hon, W. T. (2010). Optimizing patrol force deployment using a genetic algorithm. Expert Systems with Applications, 37(12), 8148–8154.
- Lee, Z.-J., Su, S.-F., Lee, C.-Y., & Hung, Y.-S. (2003). A heuristic genetic algorithm for solving resource allocation problems. Knowledge and Information Systems, 5, 503–511.
10.1007/s10115-003-0082-0 Google Scholar
- Lemmen, C., Jansen, L. J. M. & Rosman, F. (2012) Informational and computational approaches to Land Consolidation. FIG Working Week 2012,Knowing to Manage the Territory, Protect the Environment, Evaluate the Cultural Heritage. Rome, Italy, 2–16.
- Len, P. (2018). An algorithm for selecting groups of factors for prioritization of land consolidation in rural areas. Computers and Electronics in Agriculture, 144, 216–221.
- Li, Y., Wu, W., & Liu, Y. (2018). Land consolidation for rural sustainability in China: Practical reflections and policy implications. Land Use Policy, 74, 137–141.
- Martinez, R., Solla, M., Arias, P., & Armesto, J. (2013). Semi-automatic land consolidation software based on geographic information systems. Computers and Electronics in Agriculture, 97, 1–5.
- Melek, W. W., & Sadeghian, A. (2009). A theoretic framework for intelligent expert systems in medical encounter evaluation. Expert Systems, 26(1), 82–99.
- Muchova, Z., Leitmanova, M., & Petrovic, F. (2016). Possibilities of optimal land use as a consequence of lessons learned from land consolidation projects (Slovakia). Ecological Engineering, 90, 294–306.
- Porta, J., Parapar, J., Doallo, R., Rivera, F. F., Sante, I., & Crecente, R. (2013). High performance genetic algorithm for land use planning. Computers Environment and Urban Systems, 37, 45–58.
- Prazan, J., & Dumbrovsky, M. (2011). Soil conservation policies: Conditions for their effectiveness in the Czech Republic. Land Degradation & Development, 22(1), 124–133.
- Seok, J., Kasa-Vubu, J., Dipietro, M., & Girard, A. (2016). Expert system for automated bone age determination. Expert Systems with Applications, 50, 75–88.
- Uyan, M., Cay, T., & Akcakaya, O. (2013). A spatial decision support system design for land reallocation: A case study in Turkey. Computers and Electronics in Agriculture, 98, 8–16.
- Uyan, M., Cay, T., Inceyol, Y., & Hakli, H. (2015). Comparison of designed different land reallocation models in land consolidation: A case study in Konya/Turkey. Computers and Electronics in Agriculture, 110, 249–258.
- Vitikainen, A. (2004). An overview of land consolidation in Europe. Nordic Journal of Surveying and Real Estate Research, 1, 15–34.
- Xu, L. D., Liang, N., & Gao, Q. (2008). An integrated approach for agricultural ecosystem management. Ieee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 38(4), 590–599.
