Abstract
As the importance of freshwater preservation, a new hybrid approach is applied in this study. The wavelet support vector regression based on the teaching learning-based algorithm (WSVR-TLBO) is used as the proposed approach for the water level prediction of the Zayanderud dam in Iran. This model is promoted by the wavelet transform and the optimization algorithm which have prompted the error reduction and the accuracy promotion of the SVR model. In the hybrid model, the correlation coefficient (R) and mean square error (MSE) are improved by 12% and 94%, respectively, rather than SVR. The four error criteria are employed for evaluation, and their results are ameliorated in the use of the WSVR-TLBO model. Besides the SVR model, the feed-forward neural network (FFNN), autoregressive integrated moving average (ARIMA), and generalized regression neural network (GRNN) models are also applied to forecast the reservoir water level. The comparison of these models with the hybrid one is performed, and the results show the superiority of the hybrid model. The current approach's error criteria (MSE) are decreased by 67%, 94%, 92%, 92%, and 90% rather than the WSVR, SVR, ARIMA, GRNN, and FFNN models, respectively. All the error criteria reveal that the hybrid approach of this study significantly forecasted the reservoir water level with high accuracy and is outperformed by other compared models.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
Enquiries about data availability should be directed to the authors.
References
Adnan RM, Liang Z, Trajkovic S, Zounemat-Kermani M, Li B, Kisi O (2019) Daily streamflow prediction using optimally pruned extreme learning machine. J Hydrol 577:123981. https://doi.org/10.1016/j.jhydrol.2019.123981
Barge JT, Sharif HO (2016) An ensemble empirical mode decomposition, self-organizing map, and linear genetic programming approach for forecasting river streamflow. Water 8(6):247. https://doi.org/10.3390/w8060247
Chen Y, Gan M, Pan S, Pan H, Zhu X, Tao Z (2020) Application of auto-regressive (AR) analysis to improve short-term prediction of water levels in the Yangtze Estuary. J Hydrol 590:125386. https://doi.org/10.1016/j.jhydrol.2020.125386
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
El-Diasty M, Al-Harbi S, Pagiatakis S (2018) Hybrid harmonic analysis and wavelet network model for sea water level prediction. Appl Ocean Res 70:14–21. https://doi.org/10.1016/j.apor.2017.11.007
Farzad F, El-Shafie AH (2016) Performance enhancement of rainfall pattern—water level prediction model utilizing self-organizing-map clustering method. Water Resour Manag 31:945–959. https://doi.org/10.1007/s11269-016-1556-7
Flores JJ, Ortiz J, Cedeno Gonzalez JR (2015) FNN a fuzzy version of the nearest neighbor time series forecasting technique. IEEE Int Autumn Meet Power Electron Comput 4(6):15773562. https://doi.org/10.1109/ROPEC.2015.7395125
Ghorbani MA, Deo RC, Kim S, Hasanpour Kashani M, Karimi V, Izadkhah M (2020) Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia. Soft Comput 24:12079–12090. https://doi.org/10.1007/s00500-019-04648-2
Hrnjica B, Bonacci O (2019) Lake level prediction using feed forward and recurrent neural networks. Water Resour Manag 33:2471–2484. https://doi.org/10.1007/s11269-019-02255-2
Jafari H, Rajaee T, Kisi O (2020) Improved water quality prediction with hybrid wavelet-genetic programming model and Shannon entropy. Nat Resour Res. https://doi.org/10.1007/s11053-020-09702-7
Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140. https://doi.org/10.1016/j.jhydrol.2010.12.041
Kumar V, Yadav SM (2018) Optimization of reservoir operation with a new approach in evolutionary computation using TLBO algorithm and Jaya algorithm. Water Resour Manag 32:4375–4391. https://doi.org/10.1007/s11269-018-2067-5
Ladanu AA, Akanmu S, Adeyemo J (2020) Enhancing artificial neural network with multi-objective evolutionary algorithm for optimizing real time reservoir operations: a review. Am J Water Resour 8(3):118–127. https://doi.org/10.12691/ajwr-8-3-2
Malekpour MM, Tabari MM (2020) Implementation of supervised intelligence committee machine method for monthly water level prediction. Arab J Geosci 13:1049. https://doi.org/10.1007/s12517-020-06034-x
Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693. https://doi.org/10.1109/34.192463
Meng E, Huang Sh, Huang Q, Fang W, Wu L, Wang L (2019) A robust method for non-stationary streamflow prediction based on improved EMD-SVM model. J Hydrol 568:462–478. https://doi.org/10.1016/j.jhydrol.2018.11.015
Muslim TO, Ahmed AN, Malek MA, Afan HA, Ibrahim RK, El-Shafie A, Sapitang M, Sherif M, Sefelnasr A, El-Shafie A (2020) Investigating the influence of meteorological parameters on the accuracy of sea-level prediction models in Sabah, Malaysia. Sustainability 12(3):1193. https://doi.org/10.3390/su12031193
Nath A, Mthethwa F, Saha G (2020) Runoff estimation using modified adaptive neuro-fuzzy inference system. Environ Ment Eng Res 25(4):545–553. https://doi.org/10.4491/eer.2019.166
Phan TTH, Nguyen XH (2020) Combining statistical machine learning models with ARIMA for water level forecasting: the case of the red river. Adv Water Resour. https://doi.org/10.1016/j.advwatres.2020.103656
Quang Dat N, Nguyen Thi NA, Solanki VK, Le An N (2020) Prediction of water level using time series, wavelet and neural network approaches. Int J Inf REtr Res 10(3):19. https://doi.org/10.4018/IJIRR.2020070101
Rao RV, Rao VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315. https://doi.org/10.1016/j.cad.2010.12.015
Ren T, Liu X, Niu J, Lei X, Zhang Zh (2020) Real-time water level prediction of cascaded channels based on multilayer perception and recurrent neural network. J Hydrol 585:124783. https://doi.org/10.1016/j.jhydrol.2020.124783
Sahoo BB, Jha R, Singh A, Kumar D (2018) Application of Support Vector Regression for Modeling Low Flow Time Series. KSCE J Civ Eng 23:923–934. https://doi.org/10.1007/s12205-018-0128-1
Samadianfard S, Jarhan S, Salwana E, Mosavi A, Shamshirband S, Akib S (2019) Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin. Water 11(9). https://doi.org/10.3390/w11091934
Sang Y-F (2012) A practical guide to discrete wavelet decomposition of hydrologic time series. Water Resour Manag 26:3345–3365. https://doi.org/10.1007/s11269-012-0075-4
Sayari S, Mahdavi-Meymand A, Zounemat-Kermani M (2019) Prediction of critical velocity in pipeline flow of slurries using TLBO algorithm: a comprehensive study. J Pipeline Syst Eng Pract. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000439
Shirzad A, Tabesh M, Farmani R (2014) A comparison between performance of support vector regression and artificial neural network in prediction of pipe burst rate in water distribution networks. KSCE J Civ Eng 18(4):941–948. https://doi.org/10.1007/s12205-014-0537-8
Shoaib M, Shamseldin AY, Melville BW (2014) Comparative study of different wavelet-based neural network models for rainfall-runoff modeling. J Hydrol 515:47–58. https://doi.org/10.1016/j.jhydrol.2014.04.055
Sun W, Trevor B (2017) Combining k-nearest-neighbor models for annual peak breakup flow forecasting. Cold Reg Sci Technol 143:59–69. https://doi.org/10.1016/j.coldregions.2017.08.009
Thikhamarine Y, Souag-Gamane D, Kisi O (2019) A new intelligent method for monthly streamflow prediction: hybrid wavelet support vector regression based on grey wolf optimizer (WSVR–GWO). Arab J Geosci 12:540. https://doi.org/10.1007/s12517-019-4697-1
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Wang B, Wang B, Wu w, Ci C, Wang J (2020) Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information. Acta Oceanol Sin 39:157–167. https://doi.org/10.1007/s13131-020-1569-1
Weesakul U, Singhratta N, Luangdilok N (2014) Rainfall forecast in northeast of Thailand using modified k-nearest neighbor. KKU Eng J 41(2):253–261
Yadav B, Mathur S, Ch S, Adamowski J (2017) Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction. J Water Land Dev 32:103–112. https://doi.org/10.1515/jwld-2017-0012
Zaynoddin M, Bonakdari H, Ebtehaj I, Azari A, Gharabaghi B (2020) A generalized linear stochastic model for lake level prediction. Sci Total Environ 723:138015. https://doi.org/10.1016/j.scitotenv.2020.138015
Zhao G, Pang B, Xu Z, Xu L (2020) A hybrid machine learning framework for real-time water level prediction in high sediment load reaches. J Hydrol 581:124422. https://doi.org/10.1016/j.jhydrol.2019.124422
Zhong C, Jiang Z, Chu X, Guo T, Wen Q (2017) Water level forecasting using a hybrid algorithm of artificial neural networks and local Kalman filtering. J Eng Marit Environ. https://doi.org/10.1177/1475090217727135
Zhu S, Hrnjica B, Ptak M, Choiński A, Sivakumar B (2020) Forecasting of water level in multiple temperate lakes using machine learning models. J Hydrol 585:124819. https://doi.org/10.1016/j.jhydrol.2020.124819
Funding
The authors have not disclosed any funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Malekpour, M.M., Malekpoor, H. Reservoir water level forecasting using wavelet support vector regression (WSVR) based on teaching learning-based optimization algorithm (TLBO). Soft Comput 26, 8897–8909 (2022). https://doi.org/10.1007/s00500-022-07296-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-022-07296-1