Multi-Objective Optimal Allocation for Regional Water and Land Resources

Abstract:

With rapid development of society and economy, the issue of water shortage has presently been more and more serious in China. Optimal water and land resources allocation, involving many aspects such as society, economy, ecology etc., is a rational approach to solve this problem. In this study, a substantially improved model, i.e., multi-objective optimal allocation, is established for coordinating the usage of water and land resources. The model was developed on the basis of Immune Genetic Algorithms (IGA), and it mainly includes three objectives and seven constraints. The results of case study show that there is no water shortage in the predicting year of 2020 in Dongtai City, Jiangsu Province by using the optimal allocation of water and land resources. The new optimal allocation proposed in this study has a positive influence to promote the economic and social harmonious development and the natural environment protection for coastal areas of China.

• Periodical: Applied Mechanics and Materials (Volumes 641-642)
• Pages: 58-64
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.641-642.58
• Online since: September 2014
• Authors: Guo Hua Fang, Qian Qi Yin, Xian Feng Huang, Shuo Xu
• Keywords: Immune Genetic Algorithms, Land Resources Allocation, Multi-Objective Technique, Optimal Method, Urban Area, Water Resources Allocation

References

[1] Uzoagbala EM. Water demand management and water scarcityproblems in Nigeria Towns. Sub Sahara Bull (2006)4(2), p.84–90.

[2] Yan Han, Shi-guo Xu, Xiang-zhou Xu. Modeling Multisource Multiuser WaterResources Allocation. Water Resour Manage(2008)22, pp.911-923.

DOI: 10.1007/s11269-007-9201-0

[3] BinBin Huang, FaLiang Gui, XiaoHui Zhang. Study of the Optimal Water Resources Allocation Scenarios in Pingxiang City. CSISE 2011, (2011) AISC 105, p.487–493.

DOI: 10.1007/978-3-642-23756-0_78

[4] Emma E. Ezenwaji, Raymond N. C. Anyadike, Nnaemeka I. Igu. Optimal allocation of public water supply to the urban sectors of Enugu, Nigeria: a linear programming approach. Water Sci (2014) 4, p.73–78.

DOI: 10.1007/s13201-013-0131-0

[5] Holland, J.H. Adaptation in Natural and Artificial Systems. University of Michigan Press, Cambridge. Mass, (1975).

[6] Nei, M. and W.H. Li. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proceedings of the National Academy of Sciences. (1979)76(10), pp.5269-5273.

DOI: 10.1073/pnas.76.10.5269

[7] Goldberg, D.E. Genetic Algorithms in Search, Optimization AND Machine Learning. Addison Wesley, Reading, Mass, (1989).

[8] Deb, Kalyanmoy. Optimization for Engineering Design. Journal of Prentice Hall of India, (1995).

[9] Oliveira, R., Loucks, D.P. Operating rules for multi reservoir system. Journal of Water Resour. Res. (1997)33 (4), pp.839-852.

DOI: 10.1029/96wr03745

[10] Aly, A.H., Peralta, R.C. Comparison of a genetic algorithm and mathematical programming to the design of groundwater clean up system. Journal of Water Resour. Res. (1999)35 (8), pp.2415-2426.

DOI: 10.1029/1998wr900128

[11] Deb, K., et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. Journal of Evolutionary Computation, IEEE Transactions on. (2002)6(2), pp.182-197.

DOI: 10.1109/4235.996017

[12] Long Wang, Tong-guang , Wang Yuan Luo. Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades. Journal of Mathematics and Mechanics. (2011)32(6), pp.739-748.

DOI: 10.1007/s10483-011-1453-x

[13] Rajni Goyal, Shiv Prasad Yadav, Amar Kishor. A new approach to solve a general system of linear inequalities based on NSGA-II. International Journal of System Assurance Engineering and Management. (2012)3(1), pp.17-23.

DOI: 10.1007/s13198-012-0087-8

[14] Alireza Soroudi, Mehdi Ehsan. Application of a Modified NSGA Method for Multi-Objective Static Distributed Generation Planning. Arabian Journal for Science and Engineering. (2011)36(5), pp.809-825.

DOI: 10.1007/s13369-011-0077-1