A machine learning approach for probabilistic drought classification

Proceedings of the 2011 Conference on Intelligent Data Understanding, CIDU 2011

Volumn , Issue , 2011, Pages 263-275

  Mallya, Ganeshchandra ^a   Tripathi, Shivam ^b   Govindaraju, Rao S ^a  

^a PURDUE UNIVERSITY   (United States)

^b INDIAN INSTITUTE OF TECHNOLOGY KANPUR   (India)

Author keywords

[No Author keywords available]

Indexed keywords

DROUGHT CLASSIFICATIONS; MACHINE LEARNING APPROACHES; MODEL UNCERTAINTIES; PALMER DROUGHT SEVERITY INDICES; PRECIPITATION DATA; PROBABILISTIC TREATMENTS; SPATIO-TEMPORAL VARIATION; STANDARDIZED PRECIPITATION INDEX;

HIDDEN MARKOV MODELS; LEARNING SYSTEMS; NASA; UNCERTAINTY ANALYSIS;

DROUGHT;

EID: 84881325618     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. WMO. International Meteorological Vocabulary, Publication No. 182, second edition, World Meteorological Organization, Geneva, Switzerland, 1992.
2. R. R. Heim. A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society, 83(8): 1149-1165, 2002.
3. T. T. Munger. Graphical method of representing and comparing drought intensities. Mon. Weather Review, 44, 642-643, 1916.
4. G. Blumenstock Jr. Drought in the United States analyzed by means of the theory of probability. USDA Tech. Bull., 819, 1942.
5. J. McQuigg. A simple index of drought conditions. Weatherwise, 7, 64-67, 1954.
6. W. C. Palmer. Meteorological drought. Res. Paper No. 45, Weather Bureau, Washington, D. C., 1965.
7. W. C. Palmer. Keeping track of crop moisture conditions, nationwide: The new Crop Moisture Index. Weatherwise, 21, 156-161, 1968.
8. B. A. Shafer and L. E. Dezman. Development of a surface water supply index (SWSI) to assess the severity of drought conditions in snowpack runoff areas. Proceedings of the Western Snow Conference, 164-175, Colorado State University, Fort Collins, Colorado, 1982.
9. T. B. McKee, N. J. Doesken and J. Kleist. The relationship of drought frequency and duration to time scales. Proceedings of the Eighth Conference on Applied Climatology, 179-184, Anaheim, CA, 1993.
10. G. Mallya, S. Tripathi and R.S. Govindaraju. Assessment of drought characteristics using graphical models. Frontiers of Interface Between Statistics and Sciences, Hyderabad, India, pp. 565-575, 2009.
11. L. R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2): 257-286, 1989.
12. M. Thyer and G. Kuczera. A hidden Markov model for modelling long-term persistence in multi-site rainfall time series 1. Model calibration using a Bayesian approach. Journal of Hydrology, 275(1-2): 12-26, 2003.
13. S. Tripathi and R. S. Govindaraju. On the identification of intra-seasonal changes in the Indian summer monsoon. Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data (Sensor-KDD 2009), Paris, France, pp. 62-70, 2009.
14. C. M. Bishop. Pattern Recognition and Machine Learning. Information Science and Statistics, first edition, Springer, New York, USA, 2006.
15. M. Rajeevan, J. Bhate and A.K. Jaswal. Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data. Geophysical Research Letters, 35, L18707, doi: 10.1029/2008GL035143, 2008.
16. M. Rajeevan, J. Bhate, J. D. Kale and B. Lal. High resolution daily gridded rainfall data for the Indian region: analysis of break and active monsoon spells. Current Science, 91(3): 296-306, 2006.
17. S. Gadgil, M. Rajeevan and R. Nanjundiah. Monsoon prediction-why yet another failure? Current Science 88: 1389-1400, 2005.