Recognizing spatial distribution patterns of grassland insects: Neural network approaches

Stochastic Environmental Research and Risk Assessment

Volumn 22, Issue 2, 2008, Pages 207-216

  Zhang, WenJun ^a   Zhong, XiaoQing ^a   Liu, GuangHua ^b  

^a SUN YAT SEN UNIVERSITY   (China)

^b Guangdong AIB Polytechnic College   (China)

Author keywords

BP neural network; Insects; Learning vector quantization neural network; Linear discriminant analysis; Linear neural network; Recognition; Spatial distribution patterns

Indexed keywords

GRASSLAND INSECTS; LEARNING VECTOR QUANTIZATION NEURAL NETWORK; LINEAR DISCRIMINANT ANALYSIS; SPATIAL DISTRIBUTION PATTERNS;

BACKPROPAGATION; NEURAL NETWORKS; PATTERN RECOGNITION; STATISTICAL METHODS; VECTOR QUANTIZATION;

BIODIVERSITY;

ARTIFICIAL NEURAL NETWORK; DISCRIMINANT ANALYSIS; GRASSLAND; INSECT; PATTERN RECOGNITION; SPATIAL DISTRIBUTION;

HEXAPODA;

EID: 37449012506     PISSN: 14363240     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00477-007-0108-3     Document Type: Article

References (33)

1. Abrahart RJ, White SM (2001) Modelling sediment transfer in Malawi: comparing backpropagation neural network solutions against a multiple linear regression benchmark using small data set. Phys Chem Earth (B) 26(1):19-24
2. Acharya C, Mohanty S, Sukla LB, Misra VN (2006) Prediction of sulphur removal with Acidithiobacillus sp. using artificial neural networks. Ecol Modell 190(1-2):223-230
3. Almasri MN, Kaluarachchi JJ (2005) Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. Environ Modell Softw 20:851-871
4. Brosse S, Guegan JF, Tourenq JN et al (1999) The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake. Ecol Modell 120:299-311
5. Brown KS Jr (1991) Conservation of neotropical insects: Insects as indicators. In: Collins NM, Thomas JA (eds) The conservation of insects and their habitats. Academic, London, pp 349-404
6. Cereghino R, Giraudel JL, Compin A (2001) Spatial analysis of stream invertebrates distribution in the Adour-Garonne drainage basin (France), using Kohonen self organizing maps. Ecol Modell 146:167-180
7. Chon TS, Park YS, Moon KH et al (1996) Patternizing communities by using an artificial neural network. Ecol Modell 90:69-78
8. Dawson CW, Abrahart RJ, Shamseldin AY et al (2006) Flood estimation at ungauged sites using artificial neural networks. J Hydrol 319:391-409
9. Feis (2003) Analysis and design of neural networks in MATLAB 6.5, Electronics Industry Press, Beijing
10. Filippi AM, Jensen JR (2006) Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sens Environ 100:512-530
11. Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing Company, USA
12. Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464-1480
13. Krebs CJ (1989) Ecological methodology. HarperCollins, New York
14. Kremen C, Colwell RK, Erwin TL, Murphy DD (1993) Invertebrate assemblages: Their use as indicators in conservation planning. Conserv Biol 7:796-808
15. Lek S, Delacoste M, Baran P et al (1996) Application of neural networks to modelling non-linear relationships in ecology. Ecol Modell 90:39-52
16. Maravelias CD, Haralabous J, Papaconstantinou C (2003) Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks. Mar Ecol Prog Ser 255:249-258
17. Marchant JA, Onyango CM (2003) Comparison of a Bayesian classifier with a multilayer feed-forward neural network using the example of plant/weed/ soil discrimination. Comput Electron Agric 39:3-22
18. Mathworks (2002) Neural network toolbox, MATLAB 6.5
19. Moshou D, Vrindts E, Ketelaere BD (2001) A neural network based plant classifier. Comput Electron Agric 31:5-16
20. Nagendra SMS, Khare M (2006) Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions. Ecol Modell 190(1-2):99-115
21. Olden JD, Joy MK, Death RG (2004) An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Modell 178:389-397
22. Paruelo JM, Tomasel F (1997) Prediction of functional characteristics of ecosystems: A comparison of artificial neural networks and regression models. Ecol Modell 98:173-186
23. Schultz A, Wieland R (1997) The use of neural networks in agroecologica modeling. Comput Electron Agric 18:73-90
24. Shao J, Xin H, Harmon JD (1998) Comparison of image feature extraction for classification of swine thermal comfort behavior. Comput Electron Agric 19:223-232
25. Smith DW, El-Din MG, Prepas EE (2006) The application of artificial neural networks to flow and phosphorus dynamics in small streams on the Boreal Plain, with emphasis on the role of wetlands. Ecol Modell 191(1):19-32
26. SPSS Inc., SPSS for Windows 11.0.0, 2001
27. Yan PF, Zhang CS (2000) Artificial neural networks and simulated evolution. Tsinghua University Press, Beijing
28. Yu R, Leung PS, Bienfang P (2006) Predicting shrimp growth: Artificial neural network versus non-linear regression models. Aquac Eng 34:26-32
29. Zhang WJ, Qi YH (2002) Functional link artificial neural network and agri-biodiversity analysis. Biodivers Sci 3:345-350
30. Zhang WJ, Barrion AT (2006) Function approximation and documentation of sampling data using artificial neural networks. Environ Monit Assess 122(1-3):185-201
31. Zhang WJ (2007a) Supervised neural network recognition of habitat zones of rice invertebrates. Stochastic Environ Res Risk Assess (in press)
32. Zhang WJ (2007b) Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks. Environ Monit Assess (in press)
33. Zhang WJ, Bai CJ, Liu GD (2007) Neural network modeling of ecosystems: A case study on cabbage growth system. Ecol Modell 201:317-325