A study of using grey system theory and artificial neural network on the climbing ability of Buergeria robusta frog

Yuan-Hsiou Chang, Tsai-Fu Chuang

Department of Design for Sustainable Environment, Mingdao University, Chanhua, Chinese Taipei.
Department of Landscape and Architecture, Mingdao University, Chanhua, Chinese Taipei.

DOI: 10.4236/oje.2013.32010                

Abstract

Ecological engineering is an emerging study of integrating both ecology and engineering, concerned with the design, monitoring, and construction of ecosystems. In recent years, the threat to amphibian animals is becoming more and more serious. In particular, the loss of habitats caused by changes to the way land is used by human beings has hit amphibians particularly hard. Amphibians are known to be particularly vulnerable to human activities because they rely on both terrestrial and aquatic habitats for survival. With the increasing development of many areas in recent years, concrete structures are often installed along water bodies in order to increase the safety of local residents. The construction of concrete banks along rivers associated with human development has become a serious problem in Taiwan. Most ecosystems used by amphibians are lakes and stream banks, yet no related design solutions to accommodate the needs of amphibians. The need to develop the relevant design specification considering protecting the amphibian is imperative. Buergeria robusta, an endemic species in Taiwan, is tree frog widely distributed in lowland montane regions. Their breeding season is from April to September. They like to rest on trees or hide at caves during the daytime and move to the stream nearby in dusk for breeding. Males usually emit weak mating call while standing on stones. Sticky eggs are attached to undersides of rocks and stones. Tadpoles are found in slow flowing water of streams [1]. The goal of this study is to improve the understanding of the relationship between the climbing ability and the physical characteristics of amphibians. In this study, we use Artificial Neural Network to simulate the climbing ability of Buergeria robusta. Besides, Grey System Theory is also adopted to improve the performance of Artificial Neural Network. Artificial Neural Network (ANN) is a computing system that uses a large number of artificial neurons imitating natural neural ability to deal with an information network by computing system. The numerical results have show good agreement with the experimental results. The results can serve as a reference for technicians involved in future ecological engineering designs of banks throughout the world.

Keywords

Ecological Engineering; Artificial Neural Network; Grey System Theory; Buergeria robusta

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Yang, Y.R. (1998) A field guide to the frogs and toads of Taiwan, Nature and Ecology Photographer’s Society. Taipei.
[2]	David and Markus (2012) Endemic species in Taiwan. The Global Biodiversity Information Facility (GBIF). http://ecat-dev.gbif.org/checklist/1055
[3]	Bergen, S.D., Bolton, S.M. and Fridley, J.L. (2001) Design principles for ecological engineering. Ecological Engineering, 18, 201-210. doi:10.1016/S0925-8574(01)00078-7
[4]	Mitsch, W.J. (1996) Ecological engineering: A new paradigm for engineers and ecologists, In: Schulze, P.C., Ed., Engineering within Ecological Constraints, National Academy Press, Washington DC, 114-132.
[5]	Kim, K.C. and Byrne, L.B. (2006) Biodiversity loss and the taxonomic bottleneck: Emerging biodiversity science. Ecological Research, 21, 794-810. doi:10.1007/s11284-006-0035-7
[6]	Stenseth, N.C., Mysterud, A., Ottersen, G., Hurrel, J.W., Chan, K.S. and Lima, M. (2002) Ecological effects of climate fluctuations. Science, 297, 1292-1296. doi:10.1126/science.1071281
[7]	Dirzo, R. and Raven, P.H. (2003) Global state of biodiversity and loss. Annual Review of Environment and Resources, 28, 137-167. doi:10.1146/annurev.energy.28.050302.105532
[8]	Turner, W.R., Nakamura, T. and Dinetti, M. (2004) Global urbanization and the separation of humans from nature. Bioscience, 54, 585-590. doi:10.1641/0006-3568(2004)054[0585:GUATSO]2.0.CO;2
[9]	Biesmeijer, J.C., Roberts, S.P.M., Reemer, M., Ohlemiller, R., Edwards, M., Peeters, T., Schaffer, A.D., Potts, S.G., Keenkers, R., Thomas, C.D., Settele, J. and Kumin, W.E. (2006) Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science, 313, 351-354. doi:10.1126/science.1127863
[10]	Stuart, S.N., Chanson, J.S., Cox, N.A., Young, B.E., Rodrigues, A.S.L., Fischman, D.L. and Waller, R.W. (2004) Status and trends of amphibian declines and extinctions worldwide. Science, 306, 1783-1786. doi:10.1126/science.1103538
[11]	Barinaga, M. (1990) Where have all the froggies gone? Science, 247, 1033-1034. doi:10.1126/science.247.4946.1033
[12]	Blaustein, A.R. and Wake, D.B. (1990) Declining amphibian populations: A global phenomenon? Trends in Ecology and Evolution, 5, 203-204. doi:10.1016/0169-5347(90)90129-2
[13]	Wake, D.B. (1991) Declining amphibian populations. Science, 253, 860.
[14]	Woodford, J.E. and Meyer, M.W. (2003) Impact of lakeshore development on green frog abundance. Biological Conservation, 110, 277-284.
[15]	Fujioka, M. and Lane, S.J. (1997) The impact of changing irrigation practices in rice fields on frog populations of the Kanto Plain, central Japan. Ecological Research, 12, 101-108. doi:10.1007/BF02523615
[16]	Blaustein, A.R., Hoffman, P.D., Hokit, D.G., Kiesecker, J.M., Watts, S.C. and Havs, J.B. (1994) UV repair and resistance to solar UV-B in amphibian eggs: A link to population declines? Proceedings of the National Academy of Science, 91, 1791-1795. doi:10.1073/pnas.91.5.1791
[17]	Pechmann, J.H.K., Scott, D.E., Semtitsch, R.D., Caldwell, J.P., Vitt, L.J. and Gibbsons J.W. (1991) Declining amphibian populations: The problem of separating human impacts from natural fluctuations. Science, 253, 892-895. doi:10.1126/science.253.5022.892
[18]	Philips, K. (1990) Where have all the frogs and toads gone? BioScience, 40, 422-424. doi:10.2307/1311385
[19]	Laurance, W.F. (1996) Catastrophic declines of Australian rainforest frogs is unusual weather responsible. Biological Conservation, 77, 203-212. doi:10.1016/0006-3207(95)00142-5
[20]	Hamer, A.J., Lane, S.J. and Mahony, M.J. (2002) Management of freshwaterwetlands for the endangered green and golden bell frog (Litoria aurea): Roles of habitat determinants and space. Biological Conservation, 106, 413-424. doi:10.1016/S0006-3207(02)00040-X
[21]	Blaustein, A.R., Wake, D.B. and Sousa, W.P. (1994) Amphibian declines: Judging stability, persistence, and susceptibility of populations to local and global extinctions. Conservation Biology, 8, 60-71. doi:10.1046/j.1523-1739.1994.08010060.x
[22]	Gillespie, G.R. (2002) Impacts of sediment loads, tadpole density, and food type on the growth and development of tadpoles of the spotted tree frog. Biological Conservation, 106, 141-150. doi:10.1016/S0006-3207(01)00127-6
[23]	Lue, K.Y. (1996) A handbook of amphibian animal resources. Council of Agriculture, Executive Yuan, 31-33.
[24]	Chen, W.S. (2003) 31 frogs in Taiwan. Wild Bird Society of Taipei, 62-63.
[25]	Hou, W.S., Chang, Y.H. and Wang, H.W. (2008) Climatic effects and impacts of lakeshore bank designs on the activity of Chirixalus idiootocus in Yilan, Taiwan. Ecological Engineering, 32, 52-59. doi:10.1016/j.ecoleng.2007.09.004
[26]	Hou, W.S., Chang, Y.H., Chuang, T.F. and Chen, C.H. (2010) Effect of ecological engineering design on biological motility and habitat environment of hynobius arisanensis at high altitude areas in Taiwan. Ecological Engineering, 36, 791-798. doi:10.1016/j.ecoleng.2010.02.004
[27]	Chang, Y.H., Wang, H.W. and Hou, W.S. (2011) Effects of construction materials and design of lake and stream banks on climbing ability of frogs and salamanders. Ecological Engineering, 37, 1726-1733. doi:10.1016/j.ecoleng.2011.07.005
[28]	Deng, J.L. (1989) Introduction to grey system. The Journal of Grey System, 1, 1-24.
[29]	Yu, W.C. (1976) Biometrics with experiment designs. Nung-Ying Books, Taipei, 234-262.
[30]	Green, D.M. (1981) Adhesion and toe-pads of tree frogs. Copeia, 4, 790-796. doi:10.2307/1444179
[31]	Lin, S.T., Horng, S.H, Lee, B.H., Fan, P., Pan, Y., Lai, J.L., Chen, R.J. and Khan, M.K. (2011) Application of grey-relational analysis to find the most suitable watermarking scheme. International Journal of Innovative Computing, Information and Control, 7, 5389-5401
[32]	Waller, B. and Aiken, M. (1998) Predicting prepayment of residential mortgages: A neural network approach, International Journal of Information and Management Sciences, 9, 37-44.
[33]	Jost, A. (1993) Neural networks: A logical progression in credit and marketing decision system, Credit World, 81, 26-33.
[34]	Yang, H.C. and Chang, F.J. (2005) Modeling the combined open channel flow by artificial neural network. Hydrological Processes, 19, 3747-3762. doi:10.1002/hyp.5858
[35]	Han, M. and Xi, J. (2004) Efficient clustering of radial basis perception neural network for pattern recognition. Pattern Recognition, 37, 2059-2067,
[36]	Burger, C.J.S.C., Dohnal, M., Kathrada, M. and Law, R. (2001) A practitioners guide to time-series method for tourism demand forecasting—A case study of Durban, South Africa. Tourism Management, 22, 403-409. doi:10.1016/S0261-5177(00)00068-6
[37]	Schollhom, W.I. (2004) Applications of artificial neural nets in clinical biomechanics. Clinical Biomechanics, 19, 876-898. doi:10.1016/j.clinbiomech.2004.04.005
[38]	Buscb, A.C., Schefl’er, C. and Basson, A.H., (2009) Development and testing of a prototype reflex measurement system employing artificial neural networks. Computer Methods and Programs in Biomedicine, 94, 15-25. doi:10.1016/j.cmpb.2008.08.008
[39]	Barton, G., Lisboa, P., Lees, A. and Attficld, S. (2007) Gait quality assessment using self-organising artificial neural networks, Gait & Posture, 25, 374-379. doi:10.1016/j.gaitpost.2006.05.003
[40]	Wu, D., Yang, Z. and Liang, L. (2006) Using DEA-neural network approach to evaluate a branch efficiency of a large Canadian bank. Expert System with Applications, 32, 108-115. doi:10.1016/j.eswa.2005.09.034
[41]	MATLAB (2011) Getting started guide. The MathWorks, Inc, Natick.
[42]	Tsou, K.W., Chang, Y.S. and Tu, C.H. (2006) A forecast of building destruction in earthquakes: Applications of artificial neural network, Journal of Housing Studies, 15, 21-42.