Data-driven topo-climatic mapping with machine learning methods

Natural Hazards

Volumn 50, Issue 3, 2009, Pages 497-518

  Pozdnoukhov, A ^a   Foresti, Loris ^a   Kanevski, M ^a  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

Author keywords

Decision support systems; Downscaling; Environmental modelling; Feature selection; Machine learning; Natural hazards; Support vector machines; Topo climatic mapping

Indexed keywords

ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CLIMATE PREDICTION; CONFERENCE PROCEEDING; DIGITAL ELEVATION MODEL; DOWNSCALING; MODELING;

EID: 68549119086     PISSN: 0921030X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11069-008-9339-y     Document Type: Conference Paper

References (44)

1. Ambrosetti P, Mariani L, Scioli P (2005) Climatology of North Fohn in Canton Ticino and Western Lombardy. Riv Ital Agrometeorol 2:24-30
2. Andre JC, Mahrt L (1982) The nocturnal surface inversion and influence of clear air radiative cooling. J Atmos Sci 39:864-878
3. Aronszajn N (1950) Theory of reproducing kernels. Trans Am Math Soc 68:337, 404
4. Asefa T, Kemblowski M, McKee M, Khalil A (2006) Multi-time scale stream flow predictions: The support vector machines approach. J Hydrol 318(1-4):7-16
5. Atkinson BW (1981) Meso-scale atmospheric circulations. Academic Press, London, p 445
6. Boser B, Guyon I, Vapnik V (1992) A training algorithm for optimal margin classifiers. In: Fifth annual workshop on computational learning theory. ACM Press, Pittsburgh
7. Breiman L (2001) Statistical modelling: The two cultures. Stat Sci 16(3):199-231. doi: 10.1214/ss/1009213726
8. Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5(6):853-862
9. Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121-167. doi: 10.1023/ A:1009715923555
10. Cherkassky V, Mullier F (1998) Learning from data. Wiley, New York
11. Cherkassky V, Krasnopolsky M, Solomatine D, Valdés J (2006) Introduction to special issue: Earth sciences and environmental applications of computational intelligence. Neural Netw 19(2):111. doi: 10.1016/j.neunet.2006.01.013
12. Chiles J-P, Delfiner P (1999) Geostatistics: Modelling spatial uncertainty. Wiley Series in Probability and Statistics, New York
13. Clements CB, Whiteman CD, Horel JD (2003) Cold-air-pool structure and evolution in a Mountain Basin: Peter Sinks, Utah. J Appl Meteorol 42:752-768. doi:10.1175/1520-0450(2003)042<0752:CSAEIA>2.0.CO;2
14. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273-297
15. Cressie N (1993) Statistics for spatial data, revised edition. Wiley, New York
16. Daly C, Neilson RP, Phillips DL (1994) Statistical-topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 33(2):140-158
17. Dobech H, Dumolard P, Dyras I (eds) (2007) Spatial interpolation of climate data. The use of GIS in climatology and meteorology. ISTE Press, London, ISBN 978-1-905209-70-5
18. Evensen G (2006) Data assimilation: The ensemble Kalman filter. Springer, Berlin
19. Gallant J, Wilson J (2000) Primary topographic attributes. In: Wilson J, Gallant J (eds) Terrain analysis: Principles and applications. Wiley, Chichester, pp 51-85
20. Geiger R (1965) The climate near the ground. Harvard University Press, Cambridge, 482 pp
21. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157-1182. doi: 10.1162/153244303322753616
22. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1-3):389-422. doi: 10.1023/A:1012487302797
23. Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, New York
24. Haykin S (1998) Neural networks: A comprehensive foundation. Prentice Hall, Upper Saddle River
25. Kanevski M (ed) (2008) Advanced mapping of environmental data. Geostatistics, machine learning and Bayesian maximum entropy. ISTE Press, UK, 352 pp, ISBN 9781847040268
26. Kanevski M, Canu S (2000) Spatial data mapping with support vector regression. IDIAP Research Report RR-00-09
27. Kanevski M, Maignan M (2004) Analysis and modelling of spatial environmental data. EPFL Press, Lausanne
28. Kanevski M, Arutyunyan R, Bolshov L, Demyanov V, Maignan M (1996) Artificial neural networks and spatial estimations of Chernobyl fallout. Geoinformatics 7(1):5-11
29. Kanevski M, Pozdnoukhov A, Canu S, Maignan M (2002) Advanced spatial data analysis and modelling with support vector machines. Int J Fuzzy Syst 4(1):606-616
30. Meyer D, Leisch F, Hornik K (2003) The support vector machine under test. Neurocomputing 55:169-186. doi: 10.1016/S0925-2312(03)00431-4
31. Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33:1065-1076. doi: 10.1214/aoms/1177704472
32. Platt J (1999) Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Smola AJ, Bartlett P, Scholkopf B, Schuurmans D (eds) Advances in large margin classiers. MIT Press, Cambridge, MA
33. Pozdnoukhov A (2005) Support vector regression for automated robust spatial mapping of natural radioactivity. Appl GIS 1(2):21-01-21-10
34. Pozdnoukhov A, Purves RS, Kanevski M (2008) Applying machine learning methods to avalanche forecasting. Ann Glaciol 49:107-113
35. Rasmussen C, Williams C (2006) Gaussian processes for machine learning. MIT Press, Cambridge, 266 pp
36. Scholkopf B, Smola A (2002) Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge
37. See L, Solomatine DP, Abrahart R, Toth E (2007) Hydroinformatics: computational intelligence and technological developments in water science applications. J Hydrol Sci 52(3):391-396 (Special Issue: Hydroinformatics)
38. Smola A, Scholkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199-222. doi: 10.1023/B:STCO.0000035301.49549.88
39. Specht DF (1991) A generalized regression neural network. IEEE Trans Neural Netw 2:568-576. doi: 10.1109/72.97934
40. Timonin V, Savelieva E (2005) Spatial prediction of radioactivity using general regression neural network. Appl GIS 1(2):19-01-19-14
41. Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: A support vector machine approach. J Hydrol 330(3-4):621-640
42. Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York
43. Vapnik V (1998) Statistical learning theory. Wiley, New York
44. Whiteman CD (2000) Mountain meteorology: Fundamentals and applications. Oxford University Press, New York, 376 pp