PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Novel hybrid approaches based on evolutionary strategy for streamfow forecasting in the Chellif River, Algeria

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this study, the feedforward neural networks (FFNNs) were proposed to forecast the multi-day-ahead streamfow. The parameters of FFNNs model were optimized utilizing genetic algorithm (GA). Moreover, discrete wavelet transform was utilized to enhance the accuracy of FFNNs model’s forecasting. Therefore, the wavelet-based feedforward neural networks (WFFNNs-GA) model was developed for the multi-day-ahead streamfow forecasting based on three evolutionary strategies [i.e., multi-input multi-output (MIMO), multi-input single-output (MISO), and multi-input several multi-output (MISMO)]. In addition, the developed models were evaluated utilizing fve diferent statistical indices including root mean squared error, signal-to-noise ratio, correlation coefcient, Nash–Sutclife efciency, and peak fow criteria. Results provided that the statistical values of WFFNNs-GA model based on MISMO evolutionary strategy were superior to those of WFFNNs-GA model based on MISO and MIMO evolutionary strategies for the multi-day-ahead streamfow forecasting. Results indicated that the performance of WFFNNs-GA model based on MISMO evolutionary strategy provided the best accuracy. Results also explained that the hybrid model suggested better performance compared with stand-alone model based on the corresponding evolutionary strategies. Therefore, the hybrid model can be an efcient and robust implement to forecast the multi-day-ahead streamfow in the Chellif River, Algeria.
Czasopismo
Rocznik
Strony
167--180
Opis fizyczny
Bibliogr. 71 poz.
Twórcy
  • URMER Laboratory, Department of Hydraulic, Faculty of Technology, University of Tlemcen, Tlemcen, Algeria
  • URMER Laboratory, Department of Hydraulic, Faculty of Technology, University of Tlemcen, Tlemcen, Algeria
  • Research Laboratory of Water Resources, Soil and Environment, Department of Civil Engineering, Faculty of Architecture and Civil Engineering, Amar Telidji University, Laghouat, Algeria
autor
  • Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju 36040, South Korea
Bibliografia
  • 1. Abdollahi S, Raeisi J, Khalilianpour M, Ahmadi F, Kisi O (2017) Daily mean streamflow prediction in perennial and non-perennial rivers using four data driven techniques. Water Resour Manage 31(15):4855–4874. https://doi.org/10.1007/s11269-017-1782-7
  • 2. 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(1–2):85–91. https://doi.org/10.1016/j.jhydrol.2010.06.033
  • 3. Asadi S, Shahrabi J, Abbaszadeh P, Tabanmehr S (2013) A new hybrid artificial neural networks for rainfall–runoff process modeling. Neurocomputing 121:470–480. https://doi.org/10.1016/j.neucom.2013.05.023
  • 4. Badrzadeh H, Sarukkalige R, Jayawardena AW (2013) Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting. J Hydrol 507:75–85. https://doi.org/10.1016/j.jhydrol.2013.10.017
  • 5. Baydaroğlu Ö, Koçak K, Duran K (2018) River flow prediction using hybrid models of support vector regression with the wavelet transform, singular spectrum analysis and chaotic approach. Meteorol Atmos Phys 130(3):349–359. https://doi.org/10.1007/s00703-017-0518-9
  • 6. Ben Taieb S, Sorjamaa A, Bontempi G (2010) Multiple-output modeling for multi-step-ahead time series forecasting. Neurocomputing 73(10–12):1950–1957. https://doi.org/10.1016/j.neucom.2009.11.030
  • 7. Benedetto F, Giunta G, Mastroeni L (2015) A maximum entropy method to assess the predictability of financial and commodity prices. Digit Signal Proc 46:19–31. https://doi.org/10.1016/j.dsp.2015.08.001
  • 8. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford, pp 116–160
  • 9. Bormann H (2005) Evaluation of hydrological models for scenario analyses: signal-to-noise-ratio between scenario effects and model uncertainty. Adv Geosci 5:43–48
  • 10. Bouchelkia H, Belarbi F, Remini B (2014) Quantification of suspended sediment load by double correlation in the watershed of Chellif (Algeria). J Water Land Dev 21:39–46. https://doi.org/10.2478/jwld-2014-0012
  • 11. Chang FJ, Chiang YM, Chang LC (2007) Multi-step-ahead neural networks for flood forecasting. Hydrol Sci J 52(1):114–130. https://doi.org/10.1623/hysj.52.1.114
  • 12. Chu H, Wei J, Li T, Jia K (2016) Application of support vector regression for mid- and long-term runoff forecasting in “Yellow river headwater” region. Proc Eng 154:1251–1257. https://doi.org/10.1016/j.proeng.2016.07.452
  • 13. 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. https://doi.org/10.1016/j.jhydrol.2013.10.003
  • 14. Dariane AB, Azimi S (2016) Forecasting streamflow by combination of a genetic input selection algorithm and wavelet transforms using ANFIS models. Hydrol Sci J 61(3):585–600. https://doi.org/10.1080/02626667.2014.988155
  • 15. Delafrouz H, Ghaheri A, Ghorbani MA (2018) A novel hybrid neural network based on phase space reconstruction technique for daily river flow prediction. Soft Comput 22(7):2205–2215. https://doi.org/10.1007/s00500-016-2480-8
  • 16. Deo RC, Tiwari MK, Adamowski JF, Quilty JM (2017) Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stoch Env Res Risk Assess 31(5):1211–1240. https://doi.org/10.1007/s00477-016-1265-z
  • 17. Ding S, Li H, Su C, Yu J, Jin F (2013) Evolutionary artificial neural networks: a review. Artif Intell Rev 39(3):251–260. https://doi.org/10.1007/s10462-011-9270-6
  • 18. Evrendilek F (2014) Assessing neural networks with wavelet denoising and regression models in predicting diel dynamics of eddy covariance-measured latent and sensible heat fluxes and evapotranspiration. Neural Comput Appl 24(2):327–337. https://doi.org/10.1007/s00521-012-1240-7
  • 19. Ghaemi A, Rezaie-Balf M, Adamowski J, Kisi O, Quilty J (2019) On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction. Agric Meteorol 278:107647. https://doi.org/10.1016/j.agrformet.2019.107647
  • 20. Ghorbani MA, Khatibi R, Mehr AD, Asadi H (2018) Chaos-based multigene genetic programming: a new hybrid strategy for river flow forecasting. J Hydrol 562:455–467. https://doi.org/10.1016/j.jhydrol.2018.04.054
  • 21. Goldberg DE, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. Found Genet Algorithms 1:69–93. https://doi.org/10.1016/B978-0-08-050684-5.50008-2
  • 22. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2–3):95–99. https://doi.org/10.1023/A:1022602019183
  • 23. Gowda CC, Mayya SG (2014) Comparison of back propagation neural network and genetic algorithm neural network for stream flow prediction. J Comput Environ Sci 2014:290127. https://doi.org/10.1155/2014/290127
  • 24. Guo J, Zhou J, Qin H, Zou Q, Li Q (2011) Monthly streamflow forecasting based on improved support vector machine model. Expert Syst Appl 38(10):13073–13081. https://doi.org/10.1016/j.eswa.2011.04.114
  • 25. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice-Hall, Upper Saddle River, pp 178–274
  • 26. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73
  • 27. Hu T, Wu F, Zhang X (2007) Rainfall–runoff modeling using principal component analysis and neural network. Hydrol Res 38(3):235–248. https://doi.org/10.2166/nh.2007.010
  • 28. Jain A, Srinivasulu S (2004) Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques. Water Resour Res 40(4):W04302. https://doi.org/10.1029/2003WR002355
  • 29. Jajarmizadeh M, Sidek LM, Mirzai M, Alaghmand S, Harun S, Majid MR (2016) Prediction of surface flow by forcing of climate forecast system reanalysis data. Water Resour Manage 30(8):2627–2640. https://doi.org/10.1007/s11269-016-1303-0
  • 30. Kalteh AM (2015) Wavelet genetic algorithm-support vector regression (wavelet GA-SVR) for monthly flow forecasting. Water Resour Manage 29(4):1283–1293. https://doi.org/10.1007/s11269-014-0873-y
  • 31. Karimi S, Shiri J, Kisi O, Xu T (2018) Forecasting daily streamflow values: assessing heuristic models. Hydrol Res 49(3):658–669. https://doi.org/10.2166/nh.2017.111
  • 32. Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J Hydrol 351(3–4):299–317. https://doi.org/10.1016/j.jhydrol.2007.12.014
  • 33. Kisi O, Shiri J (2012) Reply to discussion of “Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models”. Water Resour Manage 26(12):3663–3665. https://doi.org/10.1007/s11269-012-0060-y
  • 34. Kisi O, Latifoğlu L, Latifoğlu F (2014) Investigation of empirical mode decomposition in forecasting of hydrological time series. Water Resour Manage 28(12):4045–4057. https://doi.org/10.1007/s11269-014-0726-8
  • 35. Kline DM (2004) Methods for multi-step time series forecasting with neural networks. In: Neural networks in business forecasting, IGI Global, pp 226–250. https://doi.org/10.4018/978-1-59140-176-6.ch012
  • 36. Krishna B, Satyaji Rao YR, Nayak PC (2011) Time series modeling of river flow using wavelet neural networks. J Water Resour Prot 3:50–59. https://doi.org/10.4236/jwarp.2011.31006
  • 37. Legates DR, McCabe GJ (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241. https://doi.org/10.1029/1998WR900018
  • 38. Malika A, Abderrahman H, Aicha L, Laounia N, Habib M (2018) Use of high spatial resolution satellite data for monitoring and characterization of drought conditions in the northwestern Algeria. Min Sci 25:85–113. https://doi.org/10.5277/msc182507
  • 39. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693. https://doi.org/10.1109/34.192463
  • 40. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models I: a discussion of principles. J Hydrol 10:282–290
  • 41. Nason GP (2008) Wavelet methods in statistics with R. Springer Science and Business Media, New York, pp 15–78. https://doi.org/10.1007/978-0-387-75961-6
  • 42. Nourani V, Alami MT, Aminfar MH (2009) A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intell 22(3):466–472. https://doi.org/10.1016/j.engappai.2008.09.003
  • 43. Nourani V, Hosseini BA, Adamowski J, Gebremicheal M (2013) 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 476:228–243. https://doi.org/10.1016/j.jhydrol.2012.10.054
  • 44. Nourani V, Baghanam AH, Adamowski J, Kisi O (2014) Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. J Hydrol 514:358–377. https://doi.org/10.1016/j.jhydrol.2014.03.057
  • 45. Parmar KS, Bhardwaj R (2015) River water prediction modeling using neural networks, fuzzy and wavelet coupled model. Water Resour Manage 29(1):17–33. https://doi.org/10.1007/s11269-014-0824-7
  • 46. Quilty J, Adamowski J (2018) Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework. J Hydrol 563:336–353. https://doi.org/10.1016/j.jhydrol.2018.05.003
  • 47. Ravansalar M, Rajaee T, Zounemat-Kermani M (2016) A wavelet–linear genetic programming model for sodium (Na+) concentration forecasting in rivers. J Hydrol 537:398–407. https://doi.org/10.1016/j.jhydrol.2016.03.062
  • 48. Ravansalar M, Rajaee T, Kisi O (2017) Wavelet-linear genetic programming: a new approach for modeling monthly streamflow. J Hydrol 549:461–475. https://doi.org/10.1016/j.jhydrol.2017.04.018
  • 49. Ravikumar P, Somashekar RK (2017) Principal component analysis and hydrochemical facies characterization to evaluate groundwater quality in Varahi river basin, Karnataka state, India. Appl Water Sci 7(2):745–755. https://doi.org/10.1007/s13201-015-0287-x
  • 50. Rezaie-Balf M, Kisi O (2017) New formulation for forecasting streamflow: evolutionary polynomial regression versus extreme learning machine. Hydrol Res 49(3):939–953. https://doi.org/10.2166/nh.2017.283
  • 51. Rezaie-Balf M, Zahmatkesh Z, Kim S (2017) Soft computing techniques for rainfall-runoff simulation: local non–parametric paradigm versus model classification methods. Water Resour Manage 31(12):3843–3865. https://doi.org/10.1007/s11269-017-1711-9
  • 52. Rezaie-Balf M, Kim S, Fallah H, Alaghmand S (2019) Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: application on the perennial rivers in Iran and South Korea. J Hydrol 572:470–485. https://doi.org/10.1016/j.jhydrol.2019.03.046
  • 53. Sahay RR, Srivastava A (2014) Predicting monsoon floods in rivers embedding wavelet transform, genetic algorithm and neural network. Water Resour Manage 28(2):301–317. https://doi.org/10.1007/s11269-013-0446-5
  • 54. Samsudin R, Saad P, Shabri A (2011) River flow time series using least squares support vector machines. Hydrol Earth Syst Sci 15:1835–1852. https://doi.org/10.5194/hess-15-1835-2011
  • 55. Sang YF, Wang Z, Liu C (2013) Discrete wavelet-based trend identification in hydrologic time series. Hydrol Process 27(14):2021–2031. https://doi.org/10.1002/hyp.9356
  • 56. Santos CAG, Freire PKMM, Silva GBL, Silva RM (2014) Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir. In: Proceedings of the international association of hydrological sciences, Bologna, Italy, pp 100–105. https://doi.org/10.5194/piahs-364-100-2014
  • 57. Seo Y, Kim S (2016) River stage forecasting using wavelet packet decomposition and data-driven Models. Proc Eng 154:1225–1230. https://doi.org/10.1016/j.proeng.2016.07.439
  • 58. Seo Y, Kim S, Kisi O, Singh VP (2015) Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. J Hydrol 520:224–243. https://doi.org/10.1016/j.jhydrol.2014.11.050
  • 59. Seo Y, Kim S, Singh V (2018) Machine learning models coupled with variational mode decomposition: a new approach for modeling daily rainfall-runoff. Atmosphere 9(7):251. https://doi.org/10.3390/atmos9070251
  • 60. Shafaei M, Kisi O (2016) Lake level forecasting using wavelet-SVR, wavelet-ANFIS and wavelet-ARMA conjunction models. Water Resour Manage 30(1):79–97. https://doi.org/10.1007/s11269-015-1147-z
  • 61. Shoaib M, Shamseldin AY, Melville BW, Khan MM (2015) Runoff forecasting using hybrid wavelet gene expression programming (WGEP) approach. J Hydrol 527:326–344. https://doi.org/10.1016/j.jhydrol.2015.04.072
  • 62. Tayyab M, Zhou J, Dong X, Ahmad I, Sun N (2019) Rainfall-runoff modeling at Jinsha River basin by integrated neural network with discrete wavelet transform. Meteorol Atmos Phys 131(1):115–125. https://doi.org/10.1007/s00703-017-0546-5
  • 63. Tiwari MK, Chatterjee C (2010) Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach. J Hydrol 394(3–4):458–470. https://doi.org/10.1016/j.jhydrol.2010.10.001
  • 64. Uysal G, Şorman AÜ (2017) Monthly streamflow estimation using wavelet-artificial neural network model: a case study on Çamlıdere dam basin, Turkey. Proc Comput Sci 120:237–244. https://doi.org/10.1016/j.procs.2017.11.234
  • 65. Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374(3–4):294–306. https://doi.org/10.1016/j.jhydrol.2009.06.019
  • 66. Willmott CJ (1984) On the evaluation of model performance in physical geography. Spatial statistics and models. Springer, Dordrecht, pp 443–460
  • 67. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(1):1–41. https://doi.org/10.1142/S1793536909000047
  • 68. Yu H, Wen X, Feng Q, Deo RC, Si J, Wu M (2018) Comparative study of hybrid-wavelet artificial intelligence models for monthly groundwater depth forecasting in extreme arid regions, Northwest China. Water Resour Manage 32(1):301–323. https://doi.org/10.1007/s11269-017-1811-6
  • 69. Yuan X, Tan Q, Lei X, Yuan Y, Wu X (2017) Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine. Energy 129:122–137. https://doi.org/10.1016/j.energy.2017.04.094
  • 70. Zakhrouf M, Bouchelkia H, Stamboul M (2016) Neuro-wavelet (WNN) and neuro-fuzzy (ANFIS) systems for modeling hydrological time series in arid areas. A case study: the catchment of Aın Hadjadj (Algeria). Desalin Water Treat 57(37):17182–18194. https://doi.org/10.1080/19443994.2015.1085908
  • 71. Zakhrouf M, Bouchelkia H, Stamboul M, Kim S, Heddam S (2018) Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: sebaou River (Algeria). Phys Geogr 39(6):506–522. https://doi.org/10.1080/02723646.2018.1429245
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d487132e-3e40-44ba-9e45-cd885e0af477
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.