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Application of a Genetic Algorithm to Predict the Growth Rate of Bufo melanostictus in Urban Forest

  • Mrinmoy MajumderEmail author
  • Rabindra Nath Barman
Chapter

Abstract

Bufo melanostictus, commonly known as the Asian common toad, is widely found in various urban forests and marshy lands. The animal is not endangered (present conservation status: Least Concern) but is under threat of being so due to various issues. One of the major threats to the toad population is the recent rapid scale of urbanization, which is gradually diminishing the common habitat and reproduction places of the species. Like other species of Bufonidae family, toads breed in still and slow-flowing rivers and temporary and permanent ponds. Many rivers have changed their characteristics due to climate change. For this reason many habitats formerly suitable for breeding are now found to be unsuitable. The present study aims to estimate the growth rate of the toad based on its various habitats and on climate patterns along with food availability. The impact from urbanization and deforestation is also considered. Overall the study tries to analyze the impact of urbanization and changes in climate patterns on the Asian common toad using a genetic algorithm technique.

Keywords

Growth rate Genetic algorithm Climate impacts 

References

  1. Chikumbo O, Nicholas I (2011) Efficient thinning regimes for Eucalyptus fastigata: multi-objective stand-level optimisation using the island model genetic algorithm. Ecol Model 222(10):1683–1695CrossRefGoogle Scholar
  2. Coillie FMBV, Verbeke LPC, Wulf RRD (2007) Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium. Remote Sens Environ 110(4):476–487CrossRefGoogle Scholar
  3. Gaafar LK, Masoud SA, Nassef AO (2008) A particle swarm-based genetic algorithm for scheduling in an agile environment. Comput Ind Eng 55(3):707–720CrossRefGoogle Scholar
  4. Galán CO, Rodríguez-Pérez JR, Martínez Torres J, García Nieto PJ (2011) Analysis of the influence of forest environments on the accuracy of GPS measurements by using genetic algorithms. Math Comput Model 54(7–8):1829–1834CrossRefGoogle Scholar
  5. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, 2nd edn. MIT Press, Cambridge, MAGoogle Scholar
  6. Ines AVM, Honda K (2005) On quantifying agricultural and water management practices from low spatial resolution RS data using genetic algorithms: a numerical study for mixed-pixel environment. Adv Water Res 28(8):856–870CrossRefGoogle Scholar
  7. Kardani-Moghaddam S, Khodadadi F, Entezari-Maleki R, Movaghar A (2012) Hybrid genetic algorithm and variable neighborhood search for task scheduling problem in grid environment. Procedia Eng 29:3808–3814CrossRefGoogle Scholar
  8. Ooka R, Chen H, Kato S (2008) Study on optimum arrangement of trees for design of pleasant outdoor environment using multi-objective genetic algorithm and coupled simulation of convection, radiation and conduction. J Wind Eng Ind Aerodyn 96(10–11):1733–1748CrossRefGoogle Scholar
  9. Rowland T, Weisstein EW. Genetic algorithm. Retrieved from MathWorld – A Wolfram Web Resource. http://mathworld.wolfram.com/GeneticAlgorithm.html. Feb 2012
  10. van Dijk PP, Iskandar D, Lau MWN, Huiqing G, Baorong G, Kuangyang L, Wenhao C, Zhigang Y, Chan B, Dutta S, Inger R, Manamendra-Arachchi K, Khan MSK (2004) Duttaphrynus melanostictus. IUCN Red List of Threatened Species. Version 2011.2. International Union for Conservation of Nature, Retrieved from www.iucnredlist.org. Accessed 23 Jan 2013

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.School of Hydro-Informatics EngineeringNational Institute of Technology Agartala, BarjalaJiraniaIndia
  2. 2.Department of Production EngineeringNational Institute of Technology Agartala, BarjalaJiraniaIndia

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