Water Resources Management

, Volume 31, Issue 15, pp 4855–4874 | Cite as

Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques

  • Sajjad Abdollahi
  • Jalil Raeisi
  • Mohammadreza Khalilianpour
  • Farshad Ahmadi
  • Ozgur Kisi
Article
  • 203 Downloads

Abstract

This study examines and compares the performance of four new attractive artificial intelligence techniques including artificial neural network (ANN), hybrid wavelet-artificial neural network (WANN), Genetic expression programming (GEP), and hybrid wavelet-genetic expression programming (WGEP) for daily mean streamflow prediction of perennial and non-perennial rivers located in semi-arid region of Zagros mountains in Iran. For this purpose, data of daily mean streamflow of the Behesht-Abad (perennial) and Joneghan (non-perennial) rivers as well as precipitation information of 17 meteorological stations for the period 1999–2008 were used. Coefficient of determination (R2) and root mean square error (RMSE) were used for evaluating the applicability of developed models. This study showed that although the GEP model was the most accurate in predicting peak flows, but in overall among the four mentioned models in both perennial and non-perennial rivers, WANN had the best performance. Among input patterns, flow based and coupled precipitation-flow based patterns with negligible difference to each other were determined to be the best patterns. Also this study confirmed that combining wavelet method with ANN and GEP and developing WANN and WGEP methods results in improving the performance of ANN and GEP models.

Keywords

Artificial neural networks Genetic expressing programming perennial and non-perennial rivers Streamflow prediction Wavelet analysis 

References

  1. Abghari H, Ahmadi H, Besharat S, Rezaverdinejad V (2012) Prediction of daily pan evaporation using wavelet neural networks. Water Resour Manag 26:3639–3652. doi: 10.1007/s11269-012-0096-z CrossRefGoogle Scholar
  2. Adamowski JF (2008a) Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. J Hydrol 353:247–266. doi: 10.1016/j.jhydrol.2008.02.013 CrossRefGoogle Scholar
  3. Adamowski JF (2008b) River flow forecasting using wavelet and cross-wavelet transform models. Hydrol Process 22:4877–4891. doi: 10.1002/hyp.7107 CrossRefGoogle Scholar
  4. Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40. doi: 10.1016/j.jhydrol.2011.06.013 CrossRefGoogle Scholar
  5. Adamowski J, Sun K (2010) Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J Hydrol 390:85–91. doi: 10.1016/j.jhydrol.2010.06.033 CrossRefGoogle Scholar
  6. Adamowski J, Chan HF, Prasher SO, Sharda VN (2012) Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data. J Hydroinf 14:731–744. doi: 10.2166/hydro.2011.044 CrossRefGoogle Scholar
  7. Agarwal A, Maheswaran R, Kurths J, Khosa R (2016) Wavelet Spectrum and self-organizing maps-based approach for hydrologic regionalization -a case study in the western United States. Water Resour Manag 30:4399–4413. doi: 10.1007/s11269-016-1428-1 CrossRefGoogle Scholar
  8. Akrami SA, Nourani V, Hakim SJS (2014) Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang gates dam. Water Resour Manag 28:2999–3018. doi: 10.1007/s11269-014-0651-x CrossRefGoogle Scholar
  9. Aksoy H (2001) Storage capacity for river reservoirs by wavelet-based generation of sequent-peak algorithm. Water Resour Manag 15:423–437. doi: 10.1023/A:1015525317135 CrossRefGoogle Scholar
  10. Aussem A, Campbell J, Murtagh F (1998) Wavelet-based feature extraction and decomposition strategies for financial forecasting. J Comput Intell Finance 6:5–12Google Scholar
  11. Beriro DJ, Abrahart RJ, Mount NJ, Nathanail CP (2012) Letter to the editor on “precipitation forecasting using wavelet-genetic programming and wavelet-Neuro-fuzzy conjunction models” by Ozgur Kisi & Jalal Shiri [water resources management 25 (2011) 3135–3152]. Water Resour Manag 26:3653–3662. doi: 10.1007/s11269-012-0049-6 CrossRefGoogle Scholar
  12. Bowden GJ, Dandy GC, Maier HR (2005a) Input determination for neural network models in water resources applications. Part 1—background and methodology. J Hydrol 301:75–92. doi: 10.1016/j.jhydrol.2004.06.021 CrossRefGoogle Scholar
  13. Bowden GJ, Maier HR, Dandy GC (2005b) Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river. J Hydrol 301:93–107. doi: 10.1016/j.jhydrol.2004.06.020 CrossRefGoogle Scholar
  14. Campisi-Pinto S, Adamowski J, Oron G (2013) Erratum to: forecasting urban water demand via wavelet-Denoising and neural network models. Case study: City of Syracuse, Italy. Water Resour Manag 27:319–321. doi: 10.1007/s11269-012-0122-1 CrossRefGoogle Scholar
  15. Cannas B, Fanni A, See L, Sias G (2006) Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Phys Chem Earth Parts ABC 31:1164–1171. doi: 10.1016/j.pce.2006.03.020 CrossRefGoogle Scholar
  16. Chou C (2011) A threshold based wavelet Denoising method for hydrological data Modelling. Water Resour Manag 25:1809–1830. doi: 10.1007/s11269-011-9776-3 CrossRefGoogle Scholar
  17. Christodoulou SE, Kourti E, Agathokleous A (2017) Waterloss detection in water distribution networks using wavelet change-point detection. Water Resour Manag 31:979–994. doi: 10.1007/s11269-016-1558-5 CrossRefGoogle Scholar
  18. Coulibaly P, Anctil F, Bobée B (1999) Prévision hydrologique par réseaux de neurones artificiels : état de l’art. Can J Civ Eng 26:293–304. doi: 10.1139/l98-069 CrossRefGoogle Scholar
  19. Coulibaly P, Anctil F, Bobée B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230:244–257. doi: 10.1016/S0022-1694(00)00214-6 CrossRefGoogle Scholar
  20. Danandeh Mehr A, Kahya E, Olyaie E (2013) Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. J Hydrol 505:240–249. doi: 10.1016/j.jhydrol.2013.10.003 CrossRefGoogle Scholar
  21. Daubechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory 36:961–1005. doi: 10.1109/18.57199 CrossRefGoogle Scholar
  22. Deo RC, Tiwari MK, Adamowski JF, Quilty JM (2016) Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stoch Env Res Risk A:1–30. doi: 10.1007/s00477-016-1265-z
  23. Djerbouai S, Souag-Gamane D (2016) Drought forecasting using neural networks, wavelet neural networks, and stochastic models: case of the Algerois Basin in North Algeria. Water Resour Manag 30:2445–2464. doi: 10.1007/s11269-016-1298-6 CrossRefGoogle Scholar
  24. Dökmen F, Aslan Z (2013) Evaluation of the parameters of water quality with wavelet techniques. Water Resour Manag 27:4977–4988. doi: 10.1007/s11269-013-0454-5 CrossRefGoogle Scholar
  25. Ferreira C (2006) Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. doi: 10.1007/3-540-32849-1
  26. Govindaraju RS (2000) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5:115–123. doi: 10.1061/(ASCE)1084-0699(2000)5:2(115) CrossRefGoogle Scholar
  27. Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5:989–993. doi: 10.1109/72.329697 CrossRefGoogle Scholar
  28. He Z, Zhang Y, Guo Q, Zhao X (2014) Comparative study of artificial neural networks and wavelet artificial neural networks for groundwater depth data forecasting with various curve fractal dimensions. Water Resour Manag 28:5297–5317. doi: 10.1007/s11269-014-0802-0 CrossRefGoogle Scholar
  29. Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31:2517–2530. doi: 10.1029/95WR01955 CrossRefGoogle Scholar
  30. Kalteh AM (2015) Wavelet genetic algorithm-support vector regression (wavelet GA-SVR) for monthly flow forecasting. Water Resour Manag 29:1283–1293. doi: 10.1007/s11269-014-0873-y CrossRefGoogle Scholar
  31. Karunanithi N, Grenney WJ, Whitley D, Bovee K (1994) Neural networks for river flow prediction. J Comput Civ Eng 8:201–220. doi: 10.1061/(ASCE)0887-3801(1994)8:2(201) CrossRefGoogle Scholar
  32. Khu ST, Liong S-Y, Babovic V et al (2001) Genetic programming and its application in real-time runoff Forecasting1. JAWRA J Am Water Resour Assoc 37:439–451. doi: 10.1111/j.1752-1688.2001.tb00980.x CrossRefGoogle Scholar
  33. Kiafar H, Babazadeh H, Marti P et al (2016) Evaluating the generalizability of GEP models for estimating reference evapotranspiration in distant humid and arid locations. Theor Appl Climatol:1–13. doi: 10.1007/s00704-016-1888-5
  34. Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12:532–539. doi: 10.1061/(ASCE)1084-0699(2007)12:5(532) CrossRefGoogle Scholar
  35. Kisi O (2010) Daily suspended sediment estimation using neuro-wavelet models. Int J Earth Sci 99:1471–1482. doi: 10.1007/s00531-009-0460-2 CrossRefGoogle Scholar
  36. Kisi O (2011) Wavelet regression model as an alternative to neural networks for river stage forecasting. Water Resour Manag 25:579–600. doi: 10.1007/s11269-010-9715-8 CrossRefGoogle Scholar
  37. Kisi O, Shiri J (2011) Precipitation forecasting using wavelet-genetic programming and wavelet-Neuro-fuzzy conjunction models. Water Resour Manag 25:3135–3152. doi: 10.1007/s11269-011-9849-3 CrossRefGoogle Scholar
  38. Kisi O, Nia AM, Gosheh MG et al (2012) Intermittent Streamflow forecasting by using several data driven techniques. Water Resour Manag 26:457–474. doi: 10.1007/s11269-011-9926-7 CrossRefGoogle Scholar
  39. Kisi O, Sanikhani H, Cobaner M (2016) Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques. Theor Appl Climatol:1–16. doi: 10.1007/s00704-016-1810-1
  40. Kumar S, Tiwari MK, Chatterjee C, Mishra A (2015) Reservoir inflow forecasting using ensemble models based on neural networks, wavelet analysis and bootstrap method. Water Resour Manag 29:4863–4883. doi: 10.1007/s11269-015-1095-7 CrossRefGoogle Scholar
  41. Labat D (2005) Recent advances in wavelet analyses: part 1. A review of concepts. J Hydrol 314:275–288. doi: 10.1016/j.jhydrol.2005.04.003 CrossRefGoogle Scholar
  42. Labat D, Ababou R, Mangin A (2000) Rainfall–runoff relations for karstic springs. Part II: continuous wavelet and discrete orthogonal multiresolution analyses. J Hydrol 238:149–178. doi: 10.1016/S0022-1694(00)00322-X
  43. Labat D, Ronchail J, Guyot JL (2005) Recent advances in wavelet analyses: part 2—Amazon, Parana, Orinoco and Congo discharges time scale variability. J Hydrol 314:289–311. doi: 10.1016/j.jhydrol.2005.04.004 CrossRefGoogle Scholar
  44. Lafrenière M, Sharp M (2003) Wavelet analysis of inter-annual variability in the runoff regimes of glacial and nival stream catchments, bow Lake, Alberta. Hydrol Process 17:1093–1118. doi: 10.1002/hyp.1187 CrossRefGoogle Scholar
  45. Lane SN (2007) Assessment of rainfall-runoff models based upon wavelet analysis. Hydrol Process 21:586–607. doi: 10.1002/hyp.6249 CrossRefGoogle Scholar
  46. Li L, Liu P, Rheinheimer DE et al (2014) Identifying explicit formulation of operating rules for multi-reservoir systems using genetic programming. Water Resour Manag 28:1545–1565. doi: 10.1007/s11269-014-0563-9 CrossRefGoogle Scholar
  47. Liu Q-J, Shi Z-H, Fang N-F et al (2013) Modeling the daily suspended sediment concentration in a hyperconcentrated river on the loess plateau, China, using the wavelet–ANN approach. Geomorphology 186:181–190. doi: 10.1016/j.geomorph.2013.01.012 CrossRefGoogle Scholar
  48. Maheswaran R, Khosa R (2013) Long term forecasting of groundwater levels with evidence of non-stationary and nonlinear characteristics. Comput Geosci 52:422–436. doi: 10.1016/j.cageo.2012.09.030 CrossRefGoogle Scholar
  49. Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124. doi: 10.1016/S1364-8152(99)00007-9 CrossRefGoogle Scholar
  50. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693. doi: 10.1109/34.192463 CrossRefGoogle Scholar
  51. Mehr AD, Kahya E, Olyaie E (2013) Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. J Hydrol 505:240–249CrossRefGoogle Scholar
  52. Miao J, Liu G, Cao B et al (2014) Identification of strong karst groundwater Runoff Belt by cross wavelet transform. Water Resour Manag 28:2903–2916. doi: 10.1007/s11269-014-0645-8 CrossRefGoogle Scholar
  53. Mirbagheri SA, Nourani V, Rajaee T, Alikhani A (2010) Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol Sci J 55:1175–1189. doi: 10.1080/02626667.2010.508871 CrossRefGoogle Scholar
  54. Moosavi V, Vafakhah M, Shirmohammadi B, Behnia N (2013) A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resour Manag 27:1301–1321. doi: 10.1007/s11269-012-0239-2 CrossRefGoogle Scholar
  55. Moosavi V, Vafakhah M, Shirmohammadi B, Ranjbar M (2014) Optimization of wavelet-ANFIS and wavelet-ANN hybrid models by Taguchi method for groundwater level forecasting. Arab J Sci Eng 39:1785–1796. doi: 10.1007/s13369-013-0762-3 CrossRefGoogle Scholar
  56. Moosavi V, Talebi A, Hadian MR (2017) Development of a hybrid wavelet packet- group method of data handling (WPGMDH) model for runoff forecasting. Water Resour Manag 31:43–59. doi: 10.1007/s11269-016-1507-3 CrossRefGoogle Scholar
  57. Ni Q, Wang L, Ye R et al (2010) Evolutionary modeling for Streamflow forecasting with minimal datasets: a case study in the west Malian River, China. Environ Eng Sci 27:377–385. doi: 10.1089/ees.2009.0082 CrossRefGoogle Scholar
  58. Nourani V, Parhizkar M (2013) Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall–runoff modeling. J Hydroinf 15:829–848. doi: 10.2166/hydro.2013.141 CrossRefGoogle Scholar
  59. Nourani V, Alami MT, Aminfar MH (2009a) A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intell 22:466–472. doi: 10.1016/j.engappai.2008.09.003 CrossRefGoogle Scholar
  60. Nourani V, Komasi M, Mano A (2009b) A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water Resour Manag 23:2877. doi: 10.1007/s11269-009-9414-5 CrossRefGoogle Scholar
  61. Nourani V, Kisi O, Komasi M (2011) Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. J Hydrol 402:41–59. doi: 10.1016/j.jhydrol.2011.03.002 CrossRefGoogle Scholar
  62. Nourani V, Komasi M, Alami MT (2012) Hybrid wavelet–genetic programming approach to optimize ANN modeling of rainfall–runoff process. J Hydrol Eng 17:724–741. doi: 10.1061/(ASCE)HE.1943-5584.0000506 CrossRefGoogle Scholar
  63. Noury M, Sedghi H, Babazedeh H, Fahmi H (2014) Urmia lake water level fluctuation hydro informatics modeling using support vector machine and conjunction of wavelet and neural network. Water Res 41:261–269. doi: 10.1134/S0097807814030129 CrossRefGoogle Scholar
  64. Parmar KS, Bhardwaj R (2015) River water prediction modeling using neural networks, fuzzy and wavelet coupled model. Water Resour Manag 29:17–33. doi: 10.1007/s11269-014-0824-7 CrossRefGoogle Scholar
  65. Partal T (2008) River flow forecasting using different artificial neural network algorithms and wavelet transform. Can J Civ Eng 36:26–38. doi: 10.1139/L08-090 CrossRefGoogle Scholar
  66. Ramana RV, Krishna B, Kumar SR, Pandey NG (2013) Monthly rainfall prediction using wavelet neural network analysis. Water Resour Manag 27:3697–3711. doi: 10.1007/s11269-013-0374-4 CrossRefGoogle Scholar
  67. Sahay RR, Sehgal V (2014) Wavelet-ANFIS models for forecasting monsoon flows: case study for the Gandak River (India). Water Res 41:574–582. doi: 10.1134/S0097807814050108 CrossRefGoogle Scholar
  68. Sahay RR, Srivastava A (2014) Predicting monsoon floods in rivers embedding wavelet transform, genetic algorithm and neural network. Water Resour Manag 28:301–317. doi: 10.1007/s11269-013-0446-5 CrossRefGoogle Scholar
  69. Sajikumar N, Thandaveswara BS (1999) A non-linear rainfall–runoff model using an artificial neural network. J Hydrol 216:32–55. doi: 10.1016/S0022-1694(98)00273-X CrossRefGoogle Scholar
  70. Sang Y-F, Wang Z, Liu C (2015) Wavelet neural modeling for hydrologic time series forecasting with uncertainty evaluation. Water Resour Manag 29:1789–1801. doi: 10.1007/s11269-014-0911-9 CrossRefGoogle Scholar
  71. Sattar AMA, Gharabaghi B, McBean EA (2016) Prediction of timing of Watermain failure using gene expression models. Water Resour Manag 30:1635–1651. doi: 10.1007/s11269-016-1241-x CrossRefGoogle Scholar
  72. Savic DA, Walters GA, Davidson JW (1999) A genetic programming approach to rainfall-runoff Modelling. Water Resour Manag 13:219–231. doi: 10.1023/A:1008132509589 CrossRefGoogle Scholar
  73. Schaefli B, Maraun D, Holschneider M (2007) What drives high flow events in the Swiss alps? Recent developments in wavelet spectral analysis and their application to hydrology. Adv Water Resour 30:2511–2525. doi: 10.1016/j.advwatres.2007.06.004 CrossRefGoogle Scholar
  74. Sehgal V, Sahay RR, Chatterjee C (2014) Effect of utilization of discrete wavelet components on flood forecasting performance of wavelet based ANFIS models. Water Resour Manag 28:1733–1749. doi: 10.1007/s11269-014-0584-4 CrossRefGoogle Scholar
  75. Tokar AS, Johnson PA (1999) Rainfall-runoff modeling using artificial neural networks. J Hydrol Eng. doi: 10.1061/(ASCE)1084-0699(1999)4:3(232
  76. Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteorol Soc 79:61–78. doi: 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2 CrossRefGoogle Scholar
  77. Traore S, Guven A (2012) Regional-specific numerical models of evapotranspiration using gene-expression programming Interface in Sahel. Water Resour Manag 26:4367–4380. doi: 10.1007/s11269-012-0149-3 CrossRefGoogle Scholar
  78. Wang W, Ding J (2003) Wavelet network model and its application to the prediction of hydrology. Nat Sci 1:67–71. doi: 10.7537/marsnsj010103.13 Google Scholar
  79. Wang W, Jin J, Li Y (2009) Prediction of inflow at three gorges dam in Yangtze River with wavelet network model. Water Resour Manag 23:2791–2803. doi: 10.1007/s11269-009-9409-2 CrossRefGoogle Scholar
  80. Xu J, Chen Y, Li W et al (2014) Integrating wavelet analysis and BPANN to simulate the annual runoff with regional climate change: a case study of Yarkand River, Northwest China. Water Resour Manag 28:2523–2537. doi: 10.1007/s11269-014-0625-z CrossRefGoogle Scholar
  81. Yarar A (2014) A hybrid wavelet and Neuro-fuzzy model for forecasting the monthly Streamflow data. Water Resour Manag 28:553–565. doi: 10.1007/s11269-013-0502-1 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Sajjad Abdollahi
    • 1
  • Jalil Raeisi
    • 2
  • Mohammadreza Khalilianpour
    • 2
  • Farshad Ahmadi
    • 1
  • Ozgur Kisi
    • 3
  1. 1.Department of Hydrology and Water Resources Engineering, Faculty of Water Sciences EngineeringShahid Chamran UniversityAhvazIran
  2. 2.Department of Civil Engineering, Faculty of EngineeringShahrekord UniversityShahrekordIran
  3. 3.Faculty of Natural Sciences and EngineeringIlia State UniversityTbilisiGeorgia

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