Interpolating monthly precipitation by self-organizing map (SOM) and multilayer perceptron (MLP)

Hydrological Sciences Journal

Volumn 52, Issue 2, 2007, Pages 305-317

  Kalteh, Aman Mohammad ^a   Berndtsson, Ronny ^a  

^a LUND UNIVERSITY   (Sweden)

Author keywords

Interpolation; Missing data; Multilayer perceptron (MLP); Northern Iran; Precipitation; Self organizing map (SOM)

Indexed keywords

COMPUTER SIMULATION; INTERPOLATION; MULTILAYER NEURAL NETWORKS; SELF ORGANIZING MAPS;

JACK-KINFE PROCEDURE; REGIONALIZATION;

PRECIPITATION (METEOROLOGY);

INTERPOLATION; MODELING; PRECIPITATION (CLIMATOLOGY); PRECIPITATION ASSESSMENT;

ASIA; EURASIA; IRAN; MIDDLE EAST;

EID: 34147214608     PISSN: 02626667     EISSN: None     Source Type: Journal    
DOI: 10.1623/hysj.52.2.305     Document Type: Article

References (29)

1. Alhoniemi, E., Hollmen, J., Simula, O. & Vesanto, J. (1999) Process monitoring and modeling using the self-organizing map. Integ. Comp.-Aid. Engng 6(1), 3-14.
2. Arabkhedri, M. (1989) Investigation of maximum floods in Northern Alborz watersheds. MSc Thesis, Tehran University, Tehran, Iran (in Persian).
3. ASCE Task Committee on application of the Artificial Neural Networks in Hydrology (2000a) Artificial neural networks in hydrology, I: preliminary concepts. J. Hydrol. Engng ASCE 5(2), 115-123.
4. ASCE Task Committee on application of the Artificial Neural Networks in Hydrology (2000b) Artificial neural networks in hydrology, II: hydrologic applications. J. Hydrol. Engng ASCE 5(2), 124-137.
5. Bishop, C. M. (1995) Neural Networks for Pattern Recognition. Oxford University Press, New York, USA.
6. Cavazos, T. (2000) Using self-organizing maps to investigate extreme climate events: an application to wintertime precipitation in the Balkan. J. Climate 13, 1718-1732.
7. Crane, R. G. & Hewitson, B. C. (2003) Clustering and upscaling of station precipitation records to regional patterns using self-organizing maps (SOMs). Clim. Res. 25, 95-107.
8. Dawson, C. W. & Wilby, R. L. (2001) Hydrological modelling using artificial neural networks. Prog. Phys. Geogr. 25(1), 80-108.
9. French, M. N., Krajewski, W. F. & Cuykendal, R. R. (1992) Rainfall forecasting in space and time using a neural network. J. Hydrol. 137, 1-31.
10. Haykin, S. (1999) Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River, New Jersey, USA.
11. Hsieh, W. W. & Tang, B. (1998) Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull. Am. Met. Soc. 79, 1855-1870.
12. Junninen, H. Niska, H. Tuppurainen, K. Ruuskanen, J., & Kolehmainen, M. (2004) Methods for imputation of missing values in air quality data sets. Atmos. Environ. 38, 2895-2907.
13. Kalteh, A. M. (2002) Investigation of effective factors on sediment yield in northern and southern Alborz Mountains. MSc Thesis, Tehran University, Tehran, Iran (in Persian).
14. Kohonen, T. (1982a) Analysis of a simple self-organizing process. Biol. Cyb. 44, 135-140.
15. Kohonen, T. (1982b) Self-organized formation of topologically correct feature maps. Biol. Cyb. 43, 59-69.
16. Kohonen, T. (2001) Self-Organizing Maps. Springer-Verlag, Berlin, Germany.
17. Kuligowski, R. J. & Barros, A. P. (1998) Using artificial neural networks to estimate missing rainfall data. J. Am. Water Resour. Assoc. 34(6), 1437-1447.
18. Maier, H. R. & Dandy, G. C. (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Modelling & Software 15, 101-124.
19. Malmgren, B. A. & Winter, A. (1999) Climate zonation in Puerto Rico based on principal components analysis and an artificial neural network. J. Climate 12, 977-985.
20. Ramirez, M. C. V., Velho, H. F. D. C. & Ferreira, N. J. (2005) Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. J. Hydrol. 301, 146-162.
21. Shaw, E. M. (1999) Hydrology in Practice. Stanley Thornes, Cheltenham, UK.
22. Teegavarapu, R. S. V. & Chandramouli, V. (2005) Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. J. Hydrol. 312, 191-206.
23. Uvo, C. B. (2003) Regionalization of northern Europe winter precipitation and its relationship with the North Atlantic Oscillation. Int. J. Climatol. 23, 1185-1194.
24. Vesanto, J. (1999) SOM-based data visualization methods. Intell. Data Anal. 3, 111-126.
25. Vesanto, J. & Ahola, J. (1999) Hunting for correlations in data using the self-organizing map. In: Proc. Int. ICSC Cong. Computational Intell. Methods and Appl. (CIAM), ICSC Academic Press, 279-285.
26. Ward, R. C. & Robinson, M. (1990) Principles ofHydrology. McGraw-Hill, New York, USA.
27. Webster, R. & Oliver, M. A. (2003) Geostatistics for Environmental Scientists. John Wiley & Sons Ltd, Chichester, UK.
28. Weigang, L. & Silva, N. (1999) Implementation of parallel self-organizing map to the classification of the image. In: Proc. SPIE-Int. Soc. Opt. Eng. 3722, 284-292.
29. Zoppou, C., Roberts, S. & Hegland, M. (2000) Spatial and temporal rainfall approximation using additive models. ANZIAM J. 42(E), C1599-C1611.