Using artificial neural networks to estimate missing rainfall data

Journal of the American Water Resources Association

Volumn 34, Issue 6, 1998, Pages 1437-1447

  Kuligowski, R J ^a   Barros, A P ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Backpropagation neural networks; Data estimation; Missing data; Rainfall

Indexed keywords

BACKPROPAGATION; DATA REDUCTION; ESTIMATION; INTERPOLATION; NEURAL NETWORKS; RAIN;

DATA ESTIMATION; FUNCTION FITTING SCHEMES;

HYDROLOGY;

ARTIFICIAL NEURAL NETWORK; DATA MANAGEMENT; ESTIMATION METHOD; PRECIPITATION (CLIMATOLOGY);

EID: 0032424739     PISSN: 1093474X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1752-1688.1998.tb05443.x     Document Type: Article

References (21)

1. Bennett, H. M., R. P. Haining, and D. A. Griffith, 1984. The Problem of Missing Data on Spatial Surfaces. Annals of the Association of American Geographers 74 (1): 138–158.
2. Caudill, M., 1991. Neural Network Training Tips and Techniques. Al Expert 6 (1): 56–61.
3. Caudill, M. and C. Butler, 1992. Understanding Neural Networks. Basic Networks, Vol. 1, MiT Press, Cambridge, Massachusetts.
4. Cheng, B. and D. M. Titterington, 1994. Neural Networks: A Review from a Statistical Perspective. Statistical Science 9 (1): 2–54.
5. Comrie, A. C., 1997. Comparing Neural Networks and Regression Models for Ozone Forecasting. Journal of the Air and Waste Management Association 47: 653–663.
6. Creutin, J. D. and C. Obled, 1982. Objective Analyses and Mapping Techniques for Rainfall Fields: An Objective Comparison. Water Resources Research 18 (2): 413–431.
7. Elsner, J. B. and C. P. Schmertmann, 1994. Assessing Forecast Skill through Cross Validation. Weather and Forecasting 9: 619–624.
8. Fletcher, D. and E. Goss, 1993. Forecasting with Neural Networks: An Application Using Bankruptcy Data. Information and Management 24: 159–167.
9. Hecht‐Nielsen, R., 1989. Theory of the Backpropagation Neural Network. Proceedings of the International Joint Conference on Neural Networks, pp. 1593–1606.
10. Hornik, K., 1991. Approximation Capabilities of Multilayer Feedforward Networks. Neural Networks 4 (2): 251–257.
11. Kuligowski, R. J., 1997. An Overview of National Weather Service Quantitative Precipitation Estimates. TDL Office Note 97—4, National Weather Service, NOAA, U. S. Department of Commerce, 27 pp.
12. Kuligowski, R. J. and A. P. Barros, 1998. Experiments in Short‐Term Precipitation Forecasting Using Artificial Neural Networks. Monthly Weather Review 126 (2): 470–482.
13. Lam, N. S.‐N., 1983. Spatial Interpolation Methods: A Review. The American Cartographer 10 (2): 129–149.
14. Linsley, Ray K., Max A. Kohler, and Joseph, L. H. Paulhus, 1982. Hydrology for Engineers. McGraw‐Hill Pub., 508 pp.
15. Marzban, C. and G. J. Stumpf, 1996. A Neural Network for Tornado Prediction Based on Doppler Radar‐Derived Attributes. Journal of Applied Meteorology 35: 617–626.
16. McCann, D. W., 1992. A Neural Network Short‐Term Forecast of Significant Thunderstorms. Weather and Forecasting 7: 525–534.
17. Michaelsen, J., 1987. Cross‐Validation in Statistical Climate Forecast Models. Journal of Climate and Applied Meteorology 26: 1589–1600.
18. Murphy, A. H., 1988. Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient. Monthly Weather Review 116: 2417–2424.
19. Peck, E. L., 1997. Quality of Hydrometeorological Data in Cold Regions. Journal of the American Water Resources Association 33 (1): 125–133.
20. Ripley, B. D., 1994. Neural Networks and Related Methods for Classification. Journal of the Royal Statistical Society 56 (3): 409–456.
21. Willmott, C. J., 1981. On the Validation of Models. Physical Geography 2 (2): 184–194.