Proposing a flood management solution based on optimal usage allocation via genetic algorithm

Document Type : Original Article

Authors

Department of Remote Sensing and Geographical Information Systems, Faculty of Geography and Urban Planning, Tabriz University, Tabriz, Iran

Abstract

Introduction:
One of the most influential factors in the occurence of a flood in the spillway basins is proposed to be the unsuitable or exorbitanceuse use of lands. One of the flood management solutions is to optimize the land use allocation by considering multiple objectives and parameters. In this regard, GIS capabilities could be applied as one of the novel scientific and technical methods along with taking the advantage of Artifcial intelligence capabilities, such as multi-objective genetic algorithm. This research aimed to model the land use allocation in GIS platform using NSGA-II algorithm to monitor flood crisis.
Material and methods:
In the designed model, using the interruptive method, land’s ecological capability was extracted and then using NSGA-II algorithm capabilities, optimal applications were obtained for various parts of the area in order to decrease the flood height as well as to increase the economic profit with the least difficult change of utilities. In the designed model, the curve number parameter (CN) was used to investigate the role of land use on the flood.
Results and discussion:
The results of the designed model are represented in several optimal patterns that have the same applicable value. Based on the present conditions of the studied region and the expert’s opinion, the optimal model could be executed. To evaluate the capability of the designed model, the Taleghan basin was selected which is located in Alborz Province; CN range of the study area was 83 in the searching space, while in designed output models, the lowest amount of CN, with 11% decrease compared to the current situation, was about 74.5%. Also, the economic profit growth was 52.19% in this land synthetic pattern.
Conclusion:
The results and achievements of this study include proposing a land use optimization model based on a multi-objective genetic algorithm for flood reduction, integrated river basin management, as well as programming in an expansive form to use in future studies.

Keywords


  1. Ar, Y. and Bostanci, E., 2016. A genetic algorithm solution to the collaborative filtering problem. Expert Systems with Applications.61, 122–128.
  2. sadzadeh, L., 2015. A local search genetic algorithm for the job shop scheduling problem with intelligent agents. Computers , Industrial Engineering. 85, 376-383.
  3. Bakhtiari Far, M., Mesgari, M., Karimi, M. and Chahaghani, A., 2011. Modeling of land use change using multi-criteria and GIS methods. Journal of Environmental Studies. 37, 43-52.
  4. Bladt, J., 2002. “Moltiobjective land use optimisation using evolutionary algorithms”. MS.c. Thesis. University of Aarhus. 52, 54-96.
  5. Campbell, J.C., Radke, J., Gless, J.T. and Wirtshafter, R.M, 1992. An application of linear programming and geographic information systems: cropland allocation in Antigua. Environment and Planning.24: 535–549.
  6. Coello Coello, C.A., Lamont G.B. and Van Veldhuizen, D.A., 2007. Evolutionary Algorithms for Solving Multi-Objective Problems. Springer Science+Business Media, LLC,. 810p
  7. Datta, D., Deb, K. and Fonseca, C.M., 2007. Multi-Objective Evolutionary Algorithm for Land-Use Management Problem. International Journal of Computational Intelligence Research. 3, 371-384.
  8. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., 2002. A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-II. IEEE Transaction on Evolutionary Computation. 6, 182-197.
  9. Deb, K., 2001. Multi-Objective Optimization Using Evolutionary Algorithms. Computers .497, 115-201.
  10. Ducheyne, E.I., Wulf, R.R.De. and Baets, B.De., 2006. A spatial approach to forest-management optimization: linking GIS and A spatial approach to forest-management optimization: linking GIS and multiple objective genetic algorithms, Taylor & Francis. International Journal of Geographical Information Science. 20, 917-928.
  11. Denham, M., Wendt, K., Bianchini, G., Cortes, A. and Margalef, T., 2012. Dynamic data-driven genetic algorithm for forest fire spread prediction. Journal of Computational Science. 3 , 398–404 .
  12. Fogue, M., Sanguesa, J.A., Naranjo, F., Gallardo, J., Garrido, P. and Martinez, F.J., 2016. Non-mergency patient transport services planning through genetic algorithms. Expert Systems with Applications. 61 , 262–271
  13. Geoffrion, A.M., Dyer, J.S. and Feinberg, A., 1972. An interactive approach for multicriterion optimization with an application to the operation of an academic department. Management Science. 19, 335-368.
  14. Hakli, H. and Uguz, H., 2017. A novel approach for automated land partitioning using genetic algorithm. Expert Systems With Applications. 82, 10-18.
  15. Herzig, A., 2008. A GIS-based Module for the Multiobjective Optimization of Areal Resource Allocation. In Proceedings 11th AGILE International Conference on Geographic Information Science, University of Girona, Spain, pp. 17-19.
  16. Khosheamoze, G., 2011. Development of a Multi-Objective Decision-Making Plot Model with Emphasis on Industrial Planning. Master's thesis of Spatial Information System.of Khaje Nasir University 26, 33-65.
  17. Liu.D T.J. Stewart ,2004. Object-Oriented decision support system modelling for multicriteria decision making natural resource management. Computers & Operations Research. 31, 985-999.
  18. Mahdoum, M., 2008. The Foundation of the Alignment of the Land. Tehran University Press, Tehran, Iran.
  19. Holland, J.H., 1975. Adaption in natural and artificial systems. The University of Michigan Press. 64, 73-95.
  20. Masoumi, Z., Mansourian, A. and Mesgari, M.S., 2010, Application of Multi-objective Genetic Algorithms in Locations of Industrial Applications. Remote Sensing and GIS of Iran, Second Year. 4, 1-22.
  21. Sadeghi, S.H., Jalili, Kh. and Nikkami, D., 2008. Maximizing profitability of land use in Breimond watershed. Journal of Natural Resources. 60, 6-15.
  22. Porta, J., Parapar, J., Doallo, R., Rivera, F.F., Sante, I. and Crecente, R., 2013. High performance genetic algorithm for land use planning. Computers Environment and Urban Systems. 37 , 45–58 .
  23. Pourvaziri, H. and Naderi, B., 2014. A hybrid multi-population genetic algorithm for the dynamic facility layout problem. Applied Soft Computing. 24, 457–469.
  24. Sha'bani, M., Ahmadi, H., Mohseni Saravi, M., Azarnivand, H. and Niknami, D., 2008. Land use optimization in order to reduceing erosion and increaseing profitability of watersheds (Case study: Khorestan Watershed of Fars). Journal of Natural Resources. 60(1-65), 33-74.
  25. Sukhija, P., Behal, S. and Singh, P., 2016. Face recognition system using genetic algorithm. Procedia Computer Science. 85, 410–417 .
  26. Suhaedi, E., 2002. Models of Spatial Planning For Sustainable Rural Development. Ph.D. Thesis. Curtin University of Technology, Perth, Australi.
  27. Tenasan, M., 2012. Designing a land use optimization model base on multi-objective genetic algorithm with a land-use approach. MS.c. Thesis. Shahid Beheshti University, Tehran, Iran.
  28. Thomas, J. and Chaudhari, N.S., 2014. Design of efficient packing system using genetic algorithm based on hyper heuristic approach. Advances in Engineering Software. 73, 45–52.
  29. The comprehensive study of watershed management in Taleghan watershed, 1993. Irrigation group, University of Tehran55,54-61.
  30. Vahhabi, J., 1997. Flood zoning in Abkhizat-e-Alagh Basin. MS.c. Thesis. University of Tehran, Tehran, Iran.
  31. Ziaee, A., 2007. Multi-objective optimization of reservoir exploitation using genetic algorithm. MS.c. Thesis. Tehran University, Tehran, Iran.