Bivariate simulation of river fow using hybrid intelligent models in sub basins of Lake Urmia, Iran

• Autorzy: Eslamitabar Vahed , Ahmadi Farshad , Sharafati Ahmad , Rezaverdinejad Vahid

Identyfikatory

DOI

10.1007/s11600-022-00933-1

• Języki publikacji: EN

Abstrakty

EN

In this study, the performance of continuous autoregressive moving average (CARMA), CARMA-generalized autoregressive conditional heteroscedasticity (CARMA-GARCH), random forest, support vector regression and ant colony optimization (SVR-ACO), and support vector regression and ant lion optimizer (SVR-ALO) models in bivariate simulating of discharge based on the rainfall variables in monthly time scale was evaluated over four sub-basins of Lake Urmia, located in northwestern Iran. The models were assessed in two stages: train and test. The results showed that the CARMA-GARCH hybrid model offered better performance in all cases than the stand-alone CARMA. The improvement percentages of the error rate in the CARMA model compared to the CARMA-GARCH hybrid model in the Mahabad Chai, Nazlu Chai, Siminehrood, and Zola Chai sub-basins were 9, 20, 17, and 6.4%, respectively, in the training phase. Among the models, the hybrid SVR models integrated with ACO and ALO optimization algorithms presented the best performance based on the Taylor diagram and evaluation criteria. Considering the use of ant colony and ant lion optimization algorithms to optimize the support vector regression model’s parameters, these models offered the best performance in the study area to simulate the discharge. The improvement percentages of the error rate in the SVR-ACO model compared to the CARMA-GARCH hybrid model in the Mahabad Chai, Nazlu Chai, Siminehrood, and Zola Chai sub-basins were 11, 10, 19, and 21%, respectively, in the training phase. In contrast, the random forest model provided the lowest accuracy and the highest error in discharge simulation.

Słowa kluczowe

EN

ant lion; heteroscedasticity; random forest; support vector regression; conditional variance

PL

mrówkolew; heteroscedastyczność; las losowy; regresja wektorów nośnych

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2023
• Tom: Vol. 71, no. 2
• Strony: 873--892
• Opis fizyczny: Bibliogr. 49 poz.

Twórcy

autor

Eslamitabar Vahed

• Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

autor

Ahmadi Farshad

• Department of Hydrology and Water Resources Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

autor

Sharafati Ahmad

• Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

• https://orcid.org/0000-0003-0448-2871

autor

Rezaverdinejad Vahid

• Department of Water Engineering, Urmia University, Urmia, Iran

Bibliografia

• 1. Adnan RM, Yuan X, Kisi O, Anam R (2017) Improving accuracy of river flow forecasting using LSSVR with gravitational search algorithm. Adv Meteorol
• 2. Afan HA, Allawi MF, El-Shafie A, Yaseen ZM, Ahmed AN, Malek MA, Koting SB, Salih SQ, Mohtar WHMW, Lai SH, Sefelnasr A, Sherif M, El-Shafie A (2020) Input attributes optimization using the feasibility of genetic nature inspired algorithm: application of river flow forecasting. Sci Rep 10(1):1–15
• 3. Ahmadianfar I, Noori RM, Togun H et al (2022) Multi-strategy Slime Mould Algorithm for hydropower multi-reservoir systems optimization. Knowl Based Syst 109048
• 4. Ali M, Deo RC, Xiang Y et al (2022) Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction. Sci Rep 12:1–23
• 5. Anctil F, Rat A (2005) Evaluation of neural network streamflow forecasting on 47 watersheds. J Hydrol Eng 10(1):85–88
• 6. Barber LB, Faunce KE, Bertolatus DW et al (2022) Watershed-scale risk to aquatic organisms from complex chemical mixtures in the Shenandoah River. Environ Sci Technol 56:845–861
• 7. Barchiesi M, Chiavola A, Di Marcantonio C, Boni MR (2021) Presence and fate of microplastics in the water sources: focus on the role of wastewater and drinking water treatment plants. J Water Process Eng 40:101787
• 8. Bollerslev T, Chou RY, Kroner KF (1992) ARCH modeling in finance: a review of the theory and empirical evidence. J Econom 52(1–2):5–59
• 9. Bower LM, Peoples BK, Eddy MC, Scott MC (2022) Quantifying flow–ecology relationships across flow regime class and ecoregions in South Carolina. Sci Total Environ 802:149721
• 10. Breiman L (2001) Random forests. Mach Learn 45(1):5–32
• 11. Can I, Tosunoğlu F, Kahya E (2012) Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of Çoruh basin, Turkey. Water Environ J 26:567–576
• 12. Chau KW, Cheng CT, Wang WC (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 3(4):294–306
• 13. Chen F, Yuan Y, Trouet V et al (2022) Ecological and societal effects of Central Asian streamflow variation over the past eight centuries. Npj Clim Atmos Sci 5:1–8
• 14. Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. In: Proceedings of the first European conference on artificial life, vol 142, pp 134–142
• 15. Compton P, Dehkordi NR, Knapp M et al (2022a) Heterogeneous fenton-like catalysis of electrogenerated H2O2 for dissolved RDX removal. Front Chem Eng 47
• 16. Compton P, Dehkordi NR, Larese Casanova P, Alshawabkeh AN (2022b) Activated carbon modifications for hetero-geneous fenton-like catalysis. J Chem Eng Catal 1:1–19
• 17. Dalkiliç HY, Yeşilyurt SN, Samui P (2021) Daily flow modeling with random forest and K-nearest neighbor methods. Erzincan Univ J Sci Technol 14(3):914–925
• 18. Dehkordi NR, Knapp M, Compton P, Fernandez LA, Alshawabkeh AN, Larese-Casanova P (2022) Degradation of dissolved RDX, NQ, and DNAN by cathodic processes in an electrochemical flow-through reactor. J Environ Chem Eng 10(3):107865
• 19. Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econom J Econom Soc 987–1007.
• 20. Eslami P, Nasirian A, Akbarpour A, Nazeri Tahroudi M (2022) Groundwater estimation of Ghayen plain with regression-based and hybrid time series models. Paddy Water Environ 1–12
• 21. Estévez J, Bellido-Jiménez JA, Liu X, García-Marín AP (2020) Monthly precipitation forecasts using wavelet neural networks models in a semiarid environment. Water 12(7):1909
• 22. Fang Y, Ahmadianfar I, Samadi-Koucheksaraee A et al (2021) An accelerated gradient-based optimization development for multi-reservoir hydropower systems optimization. Energy Rep 7:7854–7877
• 23. Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, vol 1, no 10. Springer series in statistics, New York
• 24. Kerh T, Lee CS (2006) Neural networks forecasting of flood discharge at an unmeasured station using river upstream information. Adv Eng Softw 37:533–543
• 25. Khalili K, Tahoudi MN, Mirabbasi R, Ahmadi F (2016) Investigation of spatial and temporal variability of precipitation in Iran over the last half century. Stoch Environ Res Risk Assess 30(4):1205–1221
• 26. Kisi O (2010) Wavelet regression model for short-term streamflow forcasting. J Hydrol 344–353
• 27. Komornık J, Komornıkova M, Mesiar R, Szokeova D, Szolgay J (2006) Comparison of forecasting performance of nonlinear models of hydrological time series. Phys Chem Earth 31:1127–1145
• 28. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
• 29. Moffat IU, Akpan EA, Abasiekwere UA (2017) A time series evaluation of the asymmetric nature of heteroscedasticity: an EGARCH approach. Int J Stat Appl Math 2(6):111–117
• 30. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290
• 31. Nazeri-Tahroudi M, Ramezani Y (2020) Estimation of dew point temperature in different climates of Iran using support vector regression. IDŐJÁRÁS/Q J Hung Meteorol Serv 124(4):521–539
• 32. Nazeri Tahroudi M, Pourreza-Bilondi M, Ramezani Y (2019a) Toward coupling hydrological and meteorological drought characteristics in Lake Urmia Basin, Iran. Theor Appl Climatol 138(3):1511–1523
• 33. Nazeri Tahroudi M, Ramezani Y, Ahmadi F (2019b) Investigating the trend and time of precipitation and river flow rate changes in Lake Urmia basin, Iran. Arab J Geosci 12(6):1–13
• 34. Nazeri Tahroudi M, Khalili K, Ahmadi F, Mirabbasi R, Jhajharia D (2019c) Development and application of a new index for analyzing temperature concentration for Iran’s climate. Int J Environ Sci Technol 16(6):2693–2706
• 35. Nazeri-Tahroudi M, Ramezani Y, De Michele C, Mirabbasi R (2022) Bivariate simulation of potential evapotranspiration using copula-GARCH model. Water Resour Manag 36(3):1007–1024
• 36. Nelson DB (1991) Conditional heteroskedasticity in asset returns: a new approach. Econom J Econom Soc 347–370
• 37. Paliwal V, Ghare AD, Mirajkar AB et al (2021) Proposition of new metaphor-less algorithms for reservoir operation. Complexity 2021
• 38. Patiño S, Hernández Y, Plata C et al (2021) Influence of land use on hydro-physical soil properties of Andean páramos and its effect on streamflow buffering. CATENA 202:105227
• 39. Raji M, Tahroudi MN, Ye F, Dutta J (2022) Prediction of heterogeneous Fenton process in treatment of melanoidin-containing wastewater using data-based models. J Environ Manag 307:114518
• 40. Ramezani Y, Nazeri Tahroudi M, Ahmadi F (2019) Analyzing the droughts in Iran and its eastern neighboring countries using copula functions. IDŐJÁRÁS/Q J Hung Meteorol Serv 123(4):435–453
• 41. Salas JD (1993) Analysis and modeling of hydrological time series. Maidment, McGraw-Hill Publications, New York
• 42. Shahidi A, Ramezani Y, Nazeri-Tahroudi M, Mohammadi S (2020) Application of vector autoregressive models to estimate pan evaporation values at the Salt Lake Basin, Iran. IDŐJÁRÁS/Q J Hung Meteorol Serv 124(4):463–482
• 43. Smalling KL, Devereux OH, Gordon SE et al (2021) Environmental and anthropogenic drivers of contaminants in agricultural watersheds with implications for land management. Sci Total Environ 774:145687
• 44. Tahroudi MN, Mirabbasi R, Ramezani Y, Ahmadi F (2021) Probabilistic assessment of monthly river flow discharge using copula And OSVR approaches. Water Resour Manag 36:2027–2043. https://doi.org/10.1007/s11269-022-03125-0
• 45. Wang W, Van Gelder PH, Vrijling JK, Ma J (2005) Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes. Nonlinear Processes Geophys 12:55–66
• 46. Wang WC, Chau KW, Xu DM (2015) Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resour Manag 29:2655–2675
• 47. Wen Q, Sun P, Zhang Q, Li H (2021) Nonstationary ecological instream flow and relevant causes in the Huai river basin, China. Water 13:484
• 48. Yusof F, Kane IL (2013) Volatility modeling of rainfall time series. Theoret Appl Climatol 113(1):247–258
• 49. Zhiyong L, Ping Zh, Gang C, Ledong G (2014) Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting. J Hydrol

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).