Applying Case Based Reasoning in Cuckoo Search for the Expedition of Groundwater Exploration

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 201)

Abstract

A new metaheuristic algorithm named Cuckoo search came up in the recent years. Though lots of metaheuristic algorithms exist but the main advantage of this algorithm is that its search space is extensive in nature. Due to its new arrival it hardly has any footprint in any application. Thus we have adapted this new nature inspired algorithm in our application. The main objective of our application is to find out the potentiality of groundwater in any area as queried by the user. To detect the presence of groundwater, we not only applied Cuckoo search but also case based reasoning. Thus our paper tries to integrate the above mentioned techniques. Our expert provided us with different geographical attributes such as landform, soil, lineament, geology, landuse, and slope. Depending on these attribute values, our proposed algorithm finds out the intensity of groundwater in such areas. Basically the intensity values are narrowed down to high, moderate and low. Thus, once a problem case is given by the user, the case based reasoning uses the K nearest neighbor algorithm to find out the best possible match. After the best possible match is obtained, we apply some propositional logic conditions. The need of propositional logic arises because we have observed a lot of varieties in our case base. Thus to maintain consistency in our output propositional logic is required. Our algorithm achieved 99 % efficiency. Thus we can use our proposed work in any real life problem where groundwater detection is necessary. In our application, cases are basically nests.

Keywords

Groundwater detection Nature inspired Meta-heuristic Cuckoo search K-nearest neighbor 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. I. Pufford, H. Flowers, “Environmental chemistry at a glance,” Great Britain: Blackwell, pp 46-47, 2006.Google Scholar
  2. Xin-She Yang, Suash Deb, “Cuckoo search via Lévy flights,” In Proceedings of World Congress on Nature & Biologically Inspired Computing, (NaBIC), IEEE Publications USA, pp 210-214, Dec. 2009, India.Google Scholar
  3. E. Bonabeau, M. Dorigo, G. Theraulaz, “Swarm Intelligence From Nature to Artificial Systems,” Oxford University Press, 1999.Google Scholar
  4. Sankar K. Pal, Simon C.K. Shiu, “Case based reasoning: concepts, features & soft computing,” Artificial Intelligence 21, pp 233-238, 2004.Google Scholar
  5. C.K. Reisbeck, R.C. Schank, “Inside Case based reasoning”, L. Erlbaum Associates Inc. Hillsdale, New Jersey, 1989.Google Scholar
  6. V.K. Panchal, Harish Kundra, Navpreet Kaur, “A Novel Approach to Integration of waves of swarms with case based reasoning to detect groundwater potential,” 8th Annual Asian Conference & Exhibition of Geospatial information technology & application, Map Asia, Singapore, 2009.Google Scholar
  7. Chunhua Yang, Hongqui Zhu, Weihua Gui, “Permeability prediction model for imperial smelting furnance based on improved case based reasoning,” IEEE Proceedings of the 7th World Congress on Intelligent Control & Automation, June 25-27, Chongqing, China, 2008.Google Scholar
  8. Li feng Li, “Consistency of finite theory in three types of many-valued propositional logic systems,” 2009 WRI World Computer science & Information engineering, vol. 4, pp 651, Los Angeles, March 31, 2009.Google Scholar
  9. K.J.Mammond, “Case based reasoning planning: viewing planning as a memory task”, In JL Kolodner (ed.) Proceedings of the DARPA Case based reasoning workshop, Morgan Kaufmann, CA, USA, 1988.Google Scholar
  10. I. Watson :Case based reasoning is a methodology not a technology. International Journal of Elsevier, Knowledge based system 12, 303-308 (1999).Google Scholar
  11. Zhao Yong, Lujian, “Case based reasoning for emergency disposal of the traffic accidents in regional highway network,” 2010 IEEE International Conference on Emerging management & managerial science, (ICEMMS), pp 258-260, Beijing, 2010.Google Scholar
  12. Ereck R. Speed, “Evolving a Mario agent using cuckoo search and softmax heuristics,” 2010 2nd International IEEE Consumer Electronics society’s games innovations conference, ICE-GIC, pp 1-7, Hong Kong.Google Scholar
  13. Xin-She Yang, Suash Deb :Engineering Optimization by Cuckoo search. International Journal Mathematical Modelling and Numerical Optimization, vol. 1, no.4, 330-343 (2010).Google Scholar
  14. David W. Aha :The Omnipresence of case based reasoning in science & applications. International Journal of Elsevier, Knowledge Based systems 11, 261-273 (1998).Google Scholar
  15. V.K.Panchal, Harish Kundra, Amanpreet Kaur :Biogeography based groundwater exploration. 2010 International Journal of Computer Application, IJCA, vol. 1, no. 8, 0975-8887 (2010).Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Computer EngineeringDelhi Technological UniversityDelhiIndia
  2. 2.Defence Terrain Research LaboratoryDefence Research and Development OrganizationDelhiIndia

Personalised recommendations