Drought forecasting using an aggregated drought index and artificial neural network

Journal of Water and Climate Change

Volumn 1, Issue 3, 2010, Pages 193-206

  Barua, S ^a   Perera, B J C ^a   Ng, A W M ^a   Tran, D ^a  

^a VICTORIA UNIVERSITY   (Australia)

Author keywords

Aggregated drought index; Artificial neural network; Drought; Drought forecasting; Water resources management; Yarra river catchment

Indexed keywords

DROUGHT CONDITIONS; DROUGHT INDICES; DRY PERIODS; MULTI-STEP; RAINFALL DEFICIENCY; RIVER CATCHMENT; TIME STEP; WATER RESOURCES MANAGEMENT;

AGGREGATES; CATCHMENTS; DROUGHT; NEURAL NETWORKS; WEATHER FORECASTING;

WATER RESOURCES;

EID: 84875895891     PISSN: 20402244     EISSN: None     Source Type: Journal    
DOI: 10.2166/wcc.2010.000     Document Type: Article

References (43)

1. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology 2000 Artificial neural networks in hydrology. I: preliminary concepts. J. Hydrol. Eng. 5 (2), 115-123.
2. Bacanli, U. G., Firat, M. & Dikbas, F. 2009 Adaptive neuro-fuzzy inference system for drought forecasting. Stoch. Environ. Res. Risk Assess. 23 (8), 1143-1154.
3. Barros, A. P. & Bowden, G. J. 2008 Toward long-lead operational forecasts of drought: an experimental study in the Murray-Darling River basin. J. Hydrol. 357 (3-4), 349-367.
4. Bhuiyan, C. 2004 Various drought indices for monitoring drought condition in Aravalli Terrain of India. In Proc. of the XXth ISPRS Congress, Istanbul, Turkey, pp. 12-23.
5. Bishop, C. M. 1995 Neural Networks for Pattern Recognition. Oxford University Press, Oxford.
6. Byun, H. R. & Wilhite, D. A. 1999 Objective quantification of drought severity and duration. J. Clim. 12 (9), 2747-2756.
7. Cutore, P., Mauro, G. D. & Cancelliere, A. 2009 Forecasting palmer index using neural networks and climatic indexes. J. Hydrol. Eng. 14 (6), 588-595.
8. Demuth, H. & Beale, M. 1994 Neural Network Toolbox: For use with MATLAB. The MathWorks, Natick, Massachusetts.
9. Fausett, L. V. 1994 Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall, Englewood Cliffs, New Jersey.
10. Gibbs, W. J. & Maher, J. V. 1967 Rainfall deciles as drought indicators, Bureau of Meteorology Bulletin No. 48. Commonwealth of Australia, Melbourne, Australia.
11. Hayes, M. J. 2003 Drought Indices, National Drought Mitigation Center, University of Nebraska-Lincoln, http://www.drought.unl.edu/whatis/indices.htm#spi (accessed 24 February 2009).
12. Hounam, C. E. 1975 Drought and Agriculture: Report of the CAgM Working Group on the Assessment of Drought, Tech. Note 138, Publ. WMO-392, Geneva, Switzerland.
13. Jeffrey, S. J., Carter, J. O., Moodie, K. B. & Beswick, A. R. 2001 Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Modell. Softw. 16 (4), 309-330.
14. Karunanithi, N., Grenney, W. J., Whitley, D. & Bovee, K. 1994 Neural networks for river flow prediction. J. Comput. Civ. Eng. 8 (2), 201-220.
15. Keating, J. 1992 The Drought Walked Through: A History of Water Shortage in Victoria. Department of Water Resources Victoria, Melbourne, Australia.
16. Keyantash, J. & Dracup, J. A. 2002 The quantification of drought: an evaluation of drought indices. Bull. Am. Meteorol. Soc. 83 (8), 1167-1180.
17. Keyantash, J. A. & Dracup, J. A. 2004 An aggregate drought index: assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resour. Res. 40 (W09304).
18. Kim, T. W. & Valdes, J. B. 2003 Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. J. Hydrol. Eng. 8 (6), 319-328.
19. Lorrai, M. & Sechi, G. M. 1995 Neural nets for modelling rainfallrunoff transformations. Water Resour. Manage. 9 (4), 299-313.
20. Maier, H. R. & Dandy, G. C. 1998 The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study. Environ. Modell. Softw. 13 (2), 193-209.
21. 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. Modell. Softw. 15 (1), 101-124.
22. Maier, H. R., Jain, A., Dandy, G. C. &Sudheer, K. P. 2010 Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ. Modell. Softw. 25 (8), 891-909.
23. McKee, T. B., Doesken, N. J. & Kleist, J. 1993 The relationship of drought frequency and duration to time scales. In Proc. 8th Conference on Applied Climatology, Anaheim, California, pp. 179-184.
24. Mishra, A. K. & Desai, V. R. 2005 Drought forecasting using stochastic models. Stoch. Environ. Res. Risk Assess. 19 (5), 326-339.
25. Mishra, A. K. & Desai, V. R. 2006 Drought forecasting using feedforward recursive neural network. Ecol. Model. 198 (1-2), 127-138.
26. Mishra, A. K. & Singh, V. P. 2010 A review of drought concepts. J. Hydrol. 391 (1-2), 202-216.
27. Mishra, A. K., Desai, V. R. & Singh, V. P. 2007 Drought forecasting using a hybrid stochastic and neural network model. J. Hydrol. Eng. 12 (6), 626-638.
28. Moreira, E. E., Coelho, C. A., Paulo, A. A., Pereira, L. S. & Mexia, J. T. 2008 SPI-based drought category prediction using loglinear models. J. Hydrol. 354 (1-4), 116-130.
29. Morid, S., Smakhtin, V. & Bagherzadeh, K. 2007 Drought forecasting using artificial neural networks and time series of drought indices. Int. J. Climatol. 27 (15), 2103-2111.
30. Ochoa-Rivera, J. C., Andreu, J. & García-Bartual, R. 2007 Influence of inflows modeling on management simulation of water resources system. J. Water Res. Plann. ASCE 133 (2), 106-116.
31. Palmer, W. C. 1965 Meteorological Drought, Research Paper 45. Weather Bureau, US Dep. of Commerce, Washington, DC.
32. Panu, U. S. & Sharma, T. C. 2002 Challenges in drought research: some perspectives and future directions. Hydrol. Sci. J. 47, S19-S30.
33. Paulo, A. A. & Pereira, L. S. 2007 Prediction of SPI drought class transitions using markov chains. Water Resour. Manage. 21 (10), 1813-1827.
34. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. 1986 Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition (ed. D. E. Rumelhart & J. L. McClelland), Vol. 1, pp. 318-362. Foundations MIT-Press, Cambridge, Massachusetts.
35. Sajikumar, N. & Thandaveswara, B. S. 1999 A non-linear rainfall - runoff model using an artificial neural network. J. Hydrol. 216 (1-2), 32-55.
36. Samarasinghe, S. 2006 Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition. Auerbach Publications, New York.
37. Smakhtin, V. U. & Hughes, D. A. 2004 Review, Automated Estimation and Analysis of Drought Indices in South Asia, Working Paper 83. International Water Management Institute, Sri Lanka.
38. Steinemann, A. 2003 Drought indicators and triggers: a stochastic approach to evaluation. J. Am. Water Resour. Assoc. 39 (5), 1217-1233.
39. Tan, K. S. & Rhodes, B. G. 2008 Implications of the 1997-2006 drought on water resources planning for Melbourne. In Proc. 31st Hydrology and Water Resources Symp., Adelaide, Australia, pp. 2016-2027.
40. Thiessen, A. H. 1911 Precipitation averages for large areas. Mon. Weather Rev. 39 (7), 1082-1084.
41. Tran, D. H., Perera, B. J. C. & Ng, A. W. M. 2009 Comparison of structural deterioration models for stormwater drainage pipes. Comput. Aided Civ. Infrastruct. Eng. 24 (2), 145-156.
42. Werbos, P. J. 1974 Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD dissertation thesis, Harvard University, Cambridge, Massachusetts.
43. Wilhite, D. A. 2000 Drought as a natural hazard: Concepts and definitions. In Hazards Disasters Series (ed. Wilhite, D. A), pp. 3-18. Routledge, New York.