PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Simulating Caspian Sea surface water level by artifcial neural network and support vector machine models

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Reduction in sea water level can make services in nearshore structures difcult, and sea water level rise increases the risk to residential areas or the surrounding felds. For strategic planning, it is vital to take into account the present and future fuctuations of Caspian Sea water level. In this study, support vector machine and artifcial neural network are used to estimate water level of the Caspian Sea. A 34-year period dataset is used as input data for water level on the scale based at Anzali, Iran. Performances of these two models are compared according to some statistical indices. Results of this study indicate that support vector machine with an error of 4.782 mm and r=0.96 simulated the time series better, as compared with artifcial neural network with an error of 5.014 mm and r=0.957; furthermore, the uncertainty of this model is lower than that of the artifcial neural network, i.e., 0.04 verses 0.22.
Słowa kluczowe
Czasopismo
Rocznik
Strony
553--563
Opis fizyczny
Bibliogr. 54 poz.
Twórcy
  • Water Engineering Department, University of Guilan, Rasht, Iran
  • Department of Water Engineering and Environment, Caspian Sea Basin Research Center, 4199613776 Rasht, Iran
autor
  • Department of Water Engineering, University of Tabriz, Tabriz, Iran
autor
  • Department of Water Engineering, University of Tabriz, Tabriz, Iran
autor
  • Department of Hydraulic and Ocean Engineering, National Cheng-Kung University, Tainan 701, Taiwan
Bibliografia
  • 1. Abbaspour KC, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J, Srinivasan R (2007) Modeling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J Hydrol 333:413–430
  • 2. Aghelpour P, Mohammadi B, Biazar SM (2019) Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA. Theor Appl Climatol 138(3–4):1471–1480
  • 3. Aladin NB, Plotnikov IS (2004) The Caspian sea, lake basin management initiative. Casp Bull 4:112–126
  • 4. ASCE (2000) Task committee on application of artificial neural networks in hydrology artificial neural networks in hydrology. i: preliminary concepts. J Hydrol Eng 2:115–123
  • 5. Ashrafzadeh A, Ghorbani MA, Biazar SM, Yaseen ZM (2019) Evaporation process modelling over northern Iran: application of an integrative data-intelligence model with the krill herd optimization algorithm. Hydrol Sci J 64(15):1843–1856
  • 6. Awchi TA (2014) River discharges forecasting in northern Iraq using different ANN techniques. Water Resour Manag 28(3):801–814
  • 7. Charalambous C (1992) Conjugate gradient algorithm for efficient training of artificial neural networks. IEE Proc G (Circuits, Devices Syst) 139(3):301–310
  • 8. Cheng CT, Chau KW (2004) Flood control management system for reservoirs. Environ Model Softw 19(12):1141–1150
  • 9. Cheng CT, Wu XY, Chau KW (2005) Multiple criteria rainfall-runoff model calibration using a parallel genetic algorithm in a cluster of computer. Hydrol Sci J 50(6):1069–1087
  • 10. Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A (2019) An Ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ 651:2087–2096
  • 11. Coulibaly P, Anctil F, Bobée B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230:244–257
  • 12. Dawson CW, Abrahart RJ, Shamseldin AY, Wibly RL (2006) Flood estimation at ungauged sites using artificial neural networks. J Hydrol 319:391–409
  • 13. Deo RC, Ghorbani MA, Samadianfard S, Maraseni T, Bilgili M, Biazar M (2018) Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data. Renew Energy 116:309–323
  • 14. Dibike Y, Velickov S, Solomatine D, Abbott M (2001) Model induction with of support vector machines. introduction and applications. J Comput Civil Eng 15:208–216
  • 15. Durrant PJ (2001) Win_gamma TMA non-linear data analysis and modeling tool with applications to flood prediction. Ph.D. Thesis, Department of Computer Science, Cardiff University Wales, UK
  • 16. Eskandari A, Nouri R, Meraji H, Kiaghadi A (2012) Developing a proper model for online estimation of the 5-day biochemical oxygen demand based on artificial neural network and support vector machine. J Environ Stud 38(1):71–82 (in Persian)
  • 17. Evans D, Jone A (2002) A proof of the gamma test. Proc R Soc Ser A 458:2759–2799
  • 18. Fombellida M, Destiné J (1992) The extended quickprop. Artif Neural Netw. https://doi.org/10.1016/B978-0-444-89488-5.50032-4
  • 19. Fotovatikhah F, Herrera M, Shamshirband S, Chau KW, Ardabili SF, Piran MJ (2018) Survey of computational intelligence as basis to big flood management: challenges, research directions and future work. Eng Appl Comput Fluid Mech 12(1):411–437
  • 20. Ghorbani MA, Khatibi R, Aytek A, Makarynskyy O, Shiri J (2010) Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Comput Geosci 36(2010):620–627
  • 21. Ghorbani MA, Ahmad Zadeh H, Isazadeh M, Terzi O (2016) A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction. J Environ Earth Sci 75:1–14
  • 22. Guo B, Gunn SR, Damper RI, Nelson JD (2008) Customizing kernel functions for SVM-based hyperspectral image classification. IEEE Trans Image Process 17(4):622–629
  • 23. Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993
  • 24. Hajikhodaverdikhan P, Nazari M, Mohsenizadeh M, Shamshirband S, Chau KW (2018) Earthquake prediction with meteorological data by particle filter-based support vector regression. Eng Appl Comput Fluid Mech 12(1):679–688
  • 25. Imani M, You RJ, Kuo CY (2014a) Caspian Sea level prediction using satellite altimetry by artificial neural networks. Int J Env Sci Tech 11:1035–1042
  • 26. Imani M, You RJ, Kuo CY (2014b) Forecasting Caspian Sea level changes using satellite altimetry data (June 1992–December 2013) based on evolutionary support vector regression algorithms and gene expression programming. Glob Planet Change 121:53–63
  • 27. Isazadeh M (2015) Estimation of ZarrinehRoud River discharge using hybrid artificial intelligence models. MSc. thesis. Faculty of agricultural sciences. University of Tabriz. p 171. (in Persian)
  • 28. Isazadeh M, Ahmadzadeh H, Ghorbani MA (2016) Assessment of kernel functions performance in river flow estimation using support vector machine. J Water Soil Conserv 23(3):69–89 (in Persian)
  • 29. Isazadeh M, Biazar SM, Ashrafzadeh A (2017) Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters. Environ Earth Sci 76(17):6–10
  • 30. Jacobs RA (1988) Increased rates of convergence through learning rate adaptation. Neural Netw 1(4):295–307
  • 31. Karimi S, Kisi O, Shiri J, Makarynsky Y (2013) Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor Australia. Comput Geosci 52(2013):50–59
  • 32. Kavzoglu T, Colkesen I (2009) A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Obs Geoinf 11:352–359
  • 33. Kisi O, Shiri J, Karimi S, Shamshirband S, Motamedi S, Petkovi´c D, Hashim R (2015) A survey of water level fluctuation prediction in Urmia Lake using Support Vector Machine with firefly algorithm. Appl Math Comput 270:731–743
  • 34. Lagos-Avid MP, Bonilla CA (2017) Predicting the particle size distribution of eroded sediment using artificial neural networks. Sci Total Environ 581:833–839
  • 35. Lee TL, Makarynskyy O, Shao CC (2007) A combined harmonic analysis-artificial neural network methodology for tidal predictions. J Coast Res 23:764–770
  • 36. Li S, Kazemi H, Rockaway TD (2019) Performance assessment of stormwater GI practices using artificial neural networks. Sci Total Environ 651:2811–2819
  • 37. Lin JY, Cheng CT, Chau KW (2006) Using support vector machines for long-term discharge prediction. Hydrol Sci J 51(4):599–612
  • 38. Liu GQ (2011) Comparison of regression and ARIMA models with neural network models to forecast the daily streamflow of white clay creek. PhD Thesis, University of Delaware, Newark, USA
  • 39. Makarynska D, Makarynska O (2008) Predicting sea-level variations at Cocos (Keeling)islands with artificial neural networks. Comput Geosci 34:1910–1917
  • 40. Makarynska O, Makarynska D, Kuhn M, Featherstone WE (2009) Predicting sea level variations runoff modeling. Water Resour Manag 23:2877–2894
  • 41. Meyer FW (1989) Hydrogeology, ground-water movement, and subsurface storage in the Florida aquifer system in Southern Florida, United States Geological Survey Professional Paper 1403-G. US Government Printing Office, Washington, p 1989
  • 42. Misra D, Oommen T, Agarwal A, Mishra SK (2009) Application and analysis of Support Vector machine based simulation for runoff and sediment yield. J Biosyst Eng 103:527–535
  • 43. Moazenzadeh R, Mohammadi B, Shamshirband S, Chau KW (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Appl Comput Fluid Mech 12(1):584–597
  • 44. Naganna SR, Deka PC, Ghorbani MA, Biazar SM, Al-Ansari N, Yaseen ZM (2019) Dew point temperature estimation: application of artificial intelligence model integrated with nature-inspired optimization algorithms. Water 11(4):742
  • 45. Rajaei T, Shahabi A (2014) Application of ANN and wavelet conjunction model in forecasting short-term sea level variations (case study: chabahar port). Marine Technol 1(2):42–53 (in Persian)
  • 46. Ramezani Mouzirji F, Yaghoobi M, Ghanghermeh A (2011) Caspian Sea level prediction based on fuzzy regressor system. J Water Wastewater 22(3):90–98 (in Persian)
  • 47. Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. Cogn Model 5(3):1
  • 48. Sertel E, Cigizoglu HK, Sanli DU (2008) Estimating daily mean sea level heights using artificial neural network. J Coast Res 243:727–734
  • 49. Taormina R, Chau KW, Sivakumar B (2015) Neural network river forecasting through baseflow separation and binary-coded swarm optimization. J Hydrol 529(3):1788–1797
  • 50. Thain RH, Priestley AD, Davidson MA (2004) The formation of a tidal intrusion front at the mouth of a macro tidal, partially mixed estuary: a field study of the Dart Estuary UK. Estuar Coast Shelf Sci 61:161–172
  • 51. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–1000
  • 52. Vapnik V, Golowich SE, Smola AJ (1997) Support vector method for function approximation, regression estimation and signal processing. In: Mozer M, Jordan M, Petsche T (eds) Advances in neural information processing systems. MIT Press, Cambridge, MA, pp 281–287
  • 53. Wu CL, Chau KW (2011) Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. J Hydrol 399(3–4):394–409
  • 54. Wu CL, Chau KW, Li YS (2008) River stage prediction based on a distributed support vector regression. J Hydrol 358(1–2):96–111
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f5ee8bda-5afa-49ea-a189-1287c60c09ad
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.