A gene-wavelet model for long lead time drought forecasting

Journal of Hydrology

Volumn 517, Issue , 2014, Pages 691-699

  Danandeh Mehr, Ali ^a   Kahya, Ercan ^a   Özger, Mehmet ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Drought forecasting; El Nin tild; o Southern Oscillation; Hydrologic models; Linear genetic programing; Palmer's modified drought index; Wavelet transform

Indexed keywords

ATMOSPHERIC PRESSURE; CLIMATE CHANGE; CLIMATE MODELS; GENES; SENSITIVITY ANALYSIS; WATER CONSERVATION; WATER RESOURCES; WAVELET TRANSFORMS; WEATHER FORECASTING; FORECASTING; MATHEMATICAL TRANSFORMATIONS;

DROUGHT CONDITIONS; DROUGHT INDICES; FORECASTING MODELS; GENETIC PROGRAMING; HYDROLOGIC MODELS; POTENTIAL EFFECTS; SOUTHERN OSCILLATION; SUSTAINABLE WATER RESOURCES;

DROUGHT;

CLIMATE CHANGE; DROUGHT; FORECASTING METHOD; LINEAR PROGRAMING; RESOURCE MANAGEMENT; RISK; SENSITIVITY ANALYSIS; SOUTHERN OSCILLATION; WATER DEMAND; WATER RESOURCE; WAVELET ANALYSIS; EL NINO-SOUTHERN OSCILLATION; WAVELET; WEATHER FORECASTING;

TEXAS; UNITED STATES;

EID: 84904863365     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2014.06.012     Document Type: Article

References (57)

1. Aytek A., Kisi O. A genetic programming approach to suspended sediment modelling. J. Hydrol. 2008, 351(3-4):288-298.
2. Bacanli U.G., Firat M., Dikbas E.F. Adaptive neuro-fuzzy inference system for drought forecasting. Stoch. Environ. Res. Risk Assess. 2009, 23:1143-1154.
3. Banzhaf, W., Nordin, P., Keller, R. and Francone, F.D., 1998. Genetic programming-an introduction on the automatic evolution of computer programs and its application. dpunkt/Morgan Kaufmann: Heidelberg, San Francisco.
4. Barros A.P., Bowden G. Toward long-lead operational forecasts of drought: an experimental study in the Murray-Darling River Basin. J. Hydrol. 2008, 357(3-4):349-367.
5. Belayneh, A., Adamowski, J., 2012. Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression. Appl. Comput. Intell. Soft Comput. Article ID 794061, 13pages.
6. Belayneh A., Adamowski J., Khalil B., Ozga-Zielinski B. Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. J. Hydrol. 2014, 508:418-429.
7. Bogradi I., Matyasovsky I., Bardossy A., Duckstein L. A hydroclimotological model of arial drought. J. Hydrol. 1994, 153:245-264.
8. Cancelliere A., Mauro G.D., Bonaccorso B., Rossi G. Drought forecasting using the standardized precipitation index. Water Resour. Manage 2007, 21:801-819.
9. Danandeh Mehr A., Kahya E., Olyaie E. Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. J. Hydrol. 2013, 505:240-249.
10. Danandeh Mehr, A., Kahya, E., Bagheri, F., Deliktas, E. 2013b. Successive-station monthly streamflow prediction using neuro-wavelet technique. Earth Sci. Inform., doi: 10.1007/s12145-013-0141-3, in press.
11. Dolling O.R., Varas E.A. Artificial neural networks for streamflow prediction. J. Hydraul. Res. 2002, 40(5):547-554.
12. Dorado J., Rabuñal J.R., Pazos A., Rivero D., Santos A., Puertas J. Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ANN and GP. Appl. Artif. Intell. 2003, 17:329-343.
13. Dracup J.A., Kahya E. The relationships between U.S. streamflows and La Nina events. Water Resour. Res. 1994, 30:2133-2141.
14. Fernandez C., Vega J.A., Fonturbel T., Jimenez E. Streamflow drought time series forecasting: a case study in a small watershed in North West Spain. Stoch. Environ. Res. Risk Assess. 2009, 23:1063-1070.
15. TM with Notitia and solution analytics owner's manual 2010, Register Machine Learning Technologies Inc., Littleton, CO, USA.
16. Ghorbani M.A., Khatibi R., Aytek A., Makarynskyy O., Shiri J. Sea water level forecasting using genetic programming and artificial neural networks. Comput. Geosci. 2010, 36(5):620-627.
17. Heim Richard R. A review of twentieth-century drought indices used in the United States. Bull. Amer. Meteor. Soc. 2002, 83:1149-1165.
18. Kahya E., Dracup J.A. US streamflows patterns in relation to the El Nino/Southern Oscillation. Water Resourc. Res. 1993, 29:2491-2503.
19. Kahya E., Dracup J.A. The influence of type I EI Nino and La Nina events on streamflows in the Pacific Southwest of the United States. J. Clim. 1994, 7(6):965-976.
20. Kim T.W., Valdes J.B. Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. J. Hydrol. Eng. 2003, 8(6):319-328.
21. Kis¸i O¨. Stream flow forecasting using neuro-wavelet technique. Hydrol. Process. 2008, 22:4142-4152.
22. Kisi O., Guven A. Evapotranspiration modeling using linear genetic programming technique. J. Irrig. Drain. Eng. 2010, 136(10):715-723.
23. Koza J.R. Genetic programming: on the programming of computers by means of natural selection 1992, MIT Press, Cambridge, MA, USA.
24. Labat D. Recent advances in wavelet analyses: Part 1. A review of concepts. J. Hydrol. 2005, 314(1-4):275-288.
25. Labat D., Ababou R., Mangin A. Rainfall-runoff relations for karstic springs. Part II: continuous wavelet and discrete orthogonal multiresolution analyses. J. Hydrol. 2000, 238:149-178.
26. Lohani V.K., Loganathan G.V. An early warning system for drought management using the Palmer drought index. J. Am. Water Res. Assoc. 1997, 33(6):1375-1386.
27. Mallat S. A Wavelet Tour of Signal Processing 1998, Academic Press, San Diego, CA. 2nd Ed.
28. Mishra A.K., Desai V.R. Drought forecasting using stochastic models. J. Stoch. Environ. Res. Risk Assess. 2005, 19:326-339.
29. Mishra A.K., Desai V.R. Drought forecasting using feed forward recursive neural network. Ecol. Model. 2006, 198:127-138.
30. Mishra A.K., Singh V.P. A review of drought concepts. J. Hydrol. 2010, 391(1-2):202-216.
31. Mishra A.K., Singh V.P. Drought modelling - a review. J. Hydrol. 2011, 403:157-175.
32. Mishra A.K., Desai V.R., Singh V.P. Drought forecasting using a hybrid stochastic and neural network model. J. Hydrol. Eng. 2007, 12(6):626-638.
33. Modarres R. Streamflow drought time series forecasting. Stoch. Environ. Res. Risk Assess. 2007, 15(21):223-233.
34. Morid S., Smakhtin V., Bagherzadeh K. Drought forecasting using artificial neural networks and time series of drought indices. Int. J. Climatol. 2007, 27(15):2103-2111.
35. Nourani V., Alami M.T., Aminfar M.H. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng. Appl. Artif. Intell. 2009, 22(3):466-472.
36. Nourani V., Komasi M., Mano A. A multivariate ANN wavelet approach for rainfall-runoff modelling. Water Resour. Manage 2009, 23:2877-2894.
37. Nourani V., Kisi O., Komasi M. Two hybrid artificial intelligence approaches for modeling rainfall-runoff process. J. Hydrol. 2011, 402:41-59.
38. Nourani V., Komasi M., Alami M.T. Hybrid wavelet-genetic programming approach to optimize ANN modelling of rainfall-runoff process. J. Hydrol. Eng. 2012, 7(6):724-741.
39. Nourani V., Hosseini Baghanam A., Adamowski J., Gebremichael M. Using self-organizing maps and wavelet transforms for space-time pre-processing of satellite precipitation and runoff data in neural network based rainfall-runoff modeling. J. Hydrol. 2013, 476:228-243.
40. Nourani V., Komasi M., Alami M.T. Geomorphology-based genetic programming approach for rainfall-runoff modeling. J. Hydroinform. 2013, 15(2):427-445.
41. Nourani, V., Hosseini Baghanam, A., Adamowski, J., Kisi, O., 2014. Applications of hybrid Wavelet-Artificial Intelligence models in hydrology. A review. J. Hydrol. doi: http://dx.doi.org/10.1016/j.jhydrol.2014.03.057.
42. Onderka M., Banzhaf S., Scheytt T., Krein A. Seepage velocities derived from thermal records using wavelet analysis. J. Hydrol. 2013, 479:64-74.
43. O¨zger M. Significant wave height forecasting using wavelet fuzzy logic approach. Ocean Eng. 2010, 37(16):1443-1451.
44. O¨zger M., Mishra A.K., Singh V.P. Low frequency drought variability associated with climate indices. J. Hydrol. 2009, 364(1-2):152-162.
45. O¨zger M., Mishra A.K., Singh V.P. Long lead time drought forecasting using a wavelet and fuzzy logic combination model. J. Hydrometeorol. 2012, 13:284-297.
46. Partal T., Kis¸i O¨. Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J. Hydrol. 2007, 342(1-2):199-212.
47. Pesti G., Shrestha B., Duckstein L., Bogardi I. A fuzzy rule-based approach to drought assessment. Water Resour. Res. 1996, 32(6):1741-1747.
48. Piechota T.C., Dracup J.A. Drought and regional hydrologic variation in the United States: associations with the El Niño-Southern Oscillation. Water Resour. Res. 1996, 32(5):1359-1373.
49. Pongracz R., Bogardi B., Duckstein L. Application of fuzzy rule-based modeling technique to regional drought. J. Hydrol. 1999, 224:100-114.
50. Poli, R., Langdon, W.B. and McPhee, N.F., 2008. A Field Guide to Genetic Programming. Published via and freely available at (With contributions by J.R. Koza). http://www.gp-field-guide.org.uk.
51. Rabuñal J.R., Puertas J., Sua´rez J., Rivero D. Determination of the unit hydrograph of a typical urban basin using genetic programming and artificial neural networks. Hydrol. Process. 2007, 476-485.
52. Rajagopalan B., Cook E., Lall U., Ray B.K. Spatiotemporal variability of ENSO and SST teleconnections to summer drought over the United States during the twentieth century. J. Clim. 2000, 13:4244-4255.
53. Rao A.R., Padmanabhan G. Analysis and modeling of palmer's drought index series. J. Hydrol. 1984, 68:211-229.
54. Sen Z. Critical drought analysis by second order Markov chain. J. Hydrol. 1990, 120:183-202.
55. Webster P.J., Hoyos C.D. Prediction of monsoon rainfall and river discharge on 15-30-day time scales. Bull. Am. Meteorol. Soc. 2004, 85(11):1745-1765.
56. Whigham P.A., Crapper P.F. Modelling rainfall-runoff using genetic programming. Math. Comput. Model. 2001, 33:707-721.
57. Wu C.L., Chau K.W., Li Y.S. Methods to improve neural network performance in daily flows prediction. J. Hydrol. 2009, 372:80-93.