A new library to combine artificial neural networks and support vector machines with statistics and a database engine for application in environmental modeling

Environmental Modelling and Software

Volumn 25, Issue 4, 2010, Pages 412-420

  Wieland, Ralf ^a   Mirschel, Wilfried ^a   Zbell, Bernd ^a   Groth, Karin ^a   Pechenick, Alison ^b   Fukuda, Kyoko ^c  

^a LEIBNIZ CENTRE FOR AGRICULTURAL LANDSCAPE RESEARCH ZALF   (Germany)

^b University of Vermont   (United States)

^c UNIVERSITY OF CANTERBURY   (New Zealand)

Author keywords

Artificial neural networks; Database management; Open source software; Spectral analysis; Statistics; Support vector machine; Yield modeling

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DATABASE MANAGEMENT; OPEN SOURCE SOFTWARE; SPECTRAL ANALYSIS; YIELD MODELING;

BACKPROPAGATION; COMPUTER SOFTWARE; DATABASE SYSTEMS; GEARS; GRAPHICAL USER INTERFACES; IMAGE RETRIEVAL; INTERFACES (COMPUTER); MANAGEMENT INFORMATION SYSTEMS; MULTILAYER NEURAL NETWORKS; OPEN SYSTEMS; SIMULATORS; SPECTRUM ANALYSIS; SPECTRUM ANALYZERS; SUPPORT VECTOR MACHINES; VECTORS;

NETWORK MANAGEMENT;

ARTIFICIAL NEURAL NETWORK; DATA MANAGEMENT; DATABASE; ENVIRONMENTAL MODELING; SOFTWARE; SPECTRAL ANALYSIS; STATISTICAL ANALYSIS;

EID: 72249118415     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2009.11.006     Document Type: Article

References (28)

1. Adoloye A. The relative utility of regression and artificial neural networks models for rapidly predicting the capacity of water supply reservoirs. Environmental Modeling and Software 24 10 (2009) 1233-1240
2. Brumelis G., Lapina L., Nikodemus O., and Tabors G. Use of an artificial model of monitoring data to aid interpretation of principal component analysis. Environmental Modeling & Software 15 2000 (2000) 835-845
3. Chang C.-C., and Lin C.-J. LIBSVM: a library for support vector machines (2008). http://www.csie.ntu.edu.tw/cjlin/libsvm
4. Chenard J.F., and Caissie D. Stream temperature modeling using artificial neural networks: application on Catamaran Brook, New Brunswick, Canada. Hydrological Processes 22 (2008) 3361-3372
5. Dimitriadou E., Hornik K., Leisch F., Meyer D., and Weingessel A. Miss Function of the Department of Statistics (e1071), TU Wien (2008). http://www.cran.r-project.org
6. ECHAM5 (2008). http://www.mpimet.mpg.de/en/wissenschaft/modelle/echam/echam5.html
7. GNU. General Public License Version 3 (2007). http://www.gnu.org/copyleft/gpl.html
8. Gough B., Galassi M., Davies J., Theiler J., Jungman B., Alken P., Booth M., Rossi F. 2009. GNU Scientific Library Reference Manual. Third edition (v1.12) 592 p.
9. Hansen, L.K., Larsen, J., Fog, T., 1997. Early stop criterion from the Bootstrap ensemble. In: CASSP 97: International Conference on Acoustics, Speech, and Signal Processing (Munich, April 21-24, 1997). Multidimensional Signal Processing, Neural Networks. vol. IV p. 3205-3208.
10. Hsu C.-W., Chang C.-C., and Lin C.-J. A practical guide to support vector classication (2008). http://www.csie.ntu.edu.tw/cjlin/papers/guide/guide.pdf
11. Hubbard B.B. The World According to Wavelets (2006), Peters, A.K., Massachusets
12. Kalteh A.M., Hjorth P., and Berndtsson R. Review of the self-organizing map (SOM) approach in water resources: analysis, modeling and application. Environmental Modeling & Software 23 (2008) 835-845
13. Klemens B. Modeling with Data: Tools and Techniques for Scientific Computing (2008), Princeton University Press 470 p.
14. LandCaRe2020 (2008). http://www.landcare2020.de/
15. Lu W.Z., and Wang W.J. Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends. Chemospere 59 (2005) 693-701
16. MacKay D. Information Theory, Inference and Learning Algorithms (2003), Cambridge University Press
17. May D.B., and Sivakumar M. Prediction of urban stormwater quality using artificial neural networks. Environmental Modeling and Software 24 2 (2009) 296-302
18. Mirschel W., Wieland R., and Wenkel K.-O. Spatial analysis and modeling tool V2.0-applications to the landscape indicators crop yield and crop coverage. In: Studzinski J., and Hryniewicz O. (Eds). Eco-Info and Systems Research. Series: Systems Research 52 (2006), Polish Academy of Sciences/Systems Research Institute, Warsaw 29-42
19. Nadeau C., and Bengio Y. Inference for the generalization error. Machine Learning 52 (2003)
20. Noble P.A., and Tribou E. Neuronet: an easy-to-use artificial neural network for ecological and biological modeling. Ecological Modeling 203 (2006) 87-98
21. Schoelkopf B., and Smola A.J. Learning with Kernels (2002), MIT Press 624 p.
22. Schultz A., and Wieland R. The use of neural networks in agroecological modeling. Computers & Electronics in Agriculture 18 (1997) 73-90
23. Solomatine D.P., and Ostfeld A. Data-driven modeling: some past experiences and new approaches. Journal of Hydroinformatics 10 (2008) 3-22
24. Stevens, M., Henry H., 2009. A Primer of Ecology with R. Springer. 388p.
25. Vellido A., Marti E., Comas J., Rodrigues-Roda I., and Sabater F. Exploring the ecological status of human altered streams through Generative Topographic Mapping. Environmental Modeling and Software 22 7 (2007) 1053-1065
26. Wendling U., Schellin H.-G., and Thomae M. Bereitstellung von taeglichen Informationen zum Wasserhaushalt des Bodens fuer die Zwecke der agrarmeteorologischen Beratung. Zeitschrift fuer Meteorologie 41 (1991) 468-474
27. Wieland R., Voss M., Holtmann X., Mirschel W., and Ajibefun I. Spatial Analysis and Modeling Tool (SAMT), structure and possibilities. Ecological Informatics 1 (2006) 67-76
28. Wieland R., and Mirschel W. Adaptive fuzzy modeling versus artificial neural networks. Environmental Modeling & Software 23 (2007) 215-224