Supervised neural network recognition of habitat zones of rice invertebrates

Stochastic Environmental Research and Risk Assessment

Volumn 21, Issue 6, 2007, Pages 729-735

  Zhang, WenJun ^a  

^a SUN YAT SEN UNIVERSITY   (China)

Author keywords

Habitat zones; Pattern recognition; Rice invertebrates; Supervised neural networks

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DISCRIMINANT ANALYSIS; HABITAT; INVERTEBRATE; PATTERN RECOGNITION; PROBABILITY; REGRESSION ANALYSIS; REMOTE SENSING; SENSITIVITY ANALYSIS;

INVERTEBRATA; LEPIDOPTERA;

EID: 34648846726     PISSN: 14363240     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00477-006-0085-y     Document Type: Article

References (31)

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 Software 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. Filippi AM, Jensen JR (2006) Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sens Environ 100:512-530
10. Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing Company, USA
11. Kremen C, Colwell RK, Erwin TL, Murphy DD (1993) Invertebrate assemblages: Their use as indicators in conservation planning. Conserv Biol 7:796-808
12. Lek S, Delacoste M, Baran P et al (1996) Application of neural networks to modelling nonlinear relationships in ecology. Ecol Modell 90:39-52
13. Maravelias CD, Haralabous J, Papaconstantinou C (2003) Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks. Marine Ecol Prog Ser 255:249-258
14. 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 Agri 39:3-22
15. Mathworks (2002) Neural network toolbox, MATLAB 6.5
16. Moshou D, Vrindts E, Ketelaere BD (2001) A neural network based plant classifier. Comput Electron Agri 31:5-16
17. 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
18. 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
19. 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
20. Schoenly KG, Zhang WJ (1999) IRRI Biodiversity Software Series. I. LUMP, LINK, AND JOIN: Utility programs for biodiversity research. IRRI Technical Bulletin No. 1. Manila (Philippines): International Rice Research Institute. p 23
21. Schultz A, Wieland R (1997) The use of neural networks in agroecologica modeling. Comput Electron Agri 18:73-90
22. Shao J, Xin H, Harmon JD (1998) Comparison of image feature extraction for classification of swine thermal comfort behavior. Comput Electron Agri 19:223-232
23. 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
24. Sprecht DF (1988) Probabilistic neural networks for classification, mapping and association memory. IEEE ICNN San Dieg CA, I525-I532
25. Sprecht DF (1991) A general regression neural network. IEEE Trans NN 2:568-576
26. SPSS Inc., SPSS for Windows 11.0.0, 2001
27. Way MJ, Heong KL (1994) The role of biodiversity in the dynamics and management of insect pests of tropical irrigated rice-a review. Bull Entomol Res 84:567-587
28. Yan PF, Zhang CS (2000) Artificial neural networks and simulated evolution. Tsinghua University Press, Beijing
29. Yu R, Leung PS, Bienfang P (2006) Predicting shrimp growth: Artificial neural network versus nonlinear regression models. Aquacultural Eng 34:26-32
30. Zhang WJ, Qi YH (2002) Functional link artificial neural network and agri-biodiversity analysis. Biodiver Sci 3:345-350
31. Zhang WJ, Barrion AT (2006) Function approximation and documentation of sampling data using artificial neural networks. Environ Monitor Assess (in press)