Skip to main content
SpringerLink
Log in

Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Streamflow plays a major role in the optimal management and allocation of available water resources in each region. Reliable techniques are therefore needed to be developed for streamflow modeling. In the present study, the performance of streamflow modeling is improved via developing novel boosted models. The daily streamflows of four hydrometric stations comprising of the Brantford and Galt stations located on the Grand River, Canada, as well as Macon and Elkton stations respectively, located on the Ocmulgee and Umpqua rivers, United States, are used. Three different types of boosted models are implemented and proposed by coupling the classical multi-layer perceptron (MLP) with the optimization algorithms, including particle swarm optimization (PSO) and coupled particle swarm optimization-multi-verse optimizer (PSOMVO) and a time series model, namely the bi-linear (BL). So, the boosted MLP-PSO, MLP-PSOMVO, and MLP-BL models are developed. The accuracy of all the boosted models is compared with the classical MLP and BL by the statistical metrics used. It is concluded that all the boosted models developed at the studied stations lead to superior modeling results of the daily streamflows to the classical MLP; however, the boosted MLP-BL models generally outperformed the MLP-PSO and MLP-PSOMVO ones.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • 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 Manege 31(15):4855–4874

    Google Scholar 

  • Abudu S, Cui C-l, King JP, Abudukadeer K (2010) Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River, China. Water Sci Eng 3(3):269–281

    Google Scholar 

  • Adamowski A, 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(3):731–744

    Google Scholar 

  • 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

    Google Scholar 

  • Aghelpour P, Bahrami-Pichaghchi H, Kisi O (2020) Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes. Comput Electron Agric 170:105279

    Google Scholar 

  • Ainkaran P (2004) Analysis of some linear and nonlinear time series models. A thesis submitted in fulfillment of the requirements for the degree of Master of Science, School of Mathematics and Statistics, University of Sydney

  • Al-Sudani ZA, Salih SQ, Sharafati A, Yaseen ZM (2019) Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation. J Hydrol 573:1–12

    Google Scholar 

  • Anghileri D, Voisin N, Castelletti A, Pianosi F, Nijssen B, Lettenmaier DP (2016) Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments. Water Resour Res 52(6):4209–4225

    Google Scholar 

  • Assareh E, Behrang MA, Assari MR, Ghanbarzadeh A (2010) Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy 35(12):5223–5229

    Google Scholar 

  • Bayazit M (2015) Nonstationarity of hydrological records and recent trends in trend analysis: a state-of-the-art review. Environ Process 2(3):527–542

    Google Scholar 

  • Choong SM, El-Shafie A, Mohtar WW (2017) Optimisation of multiple hydropower reservoir operation using artificial bee colony algorithm. Water Resour Manag 31(4):1397–1411

    Google Scholar 

  • Di C, Yang X, Wang X (2014) A four-stage hybrid model for hydrological time series forecasting. PLoS One 9(8):e104663

    Google Scholar 

  • Eberhart R, Kennedy J (1995) Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks 4:1942–1948

  • Fan J, Yao Q (2003) Nonlinear time series, nonparametric and parametric methods. Springer-Verlag, NewYork, Inc.

    Google Scholar 

  • Fang W, Huang S, Ren K, Huang Q, Huang G, Cheng G, Li K (2019) Examining the applicability of different sampling techniques in the development of decomposition-based streamflow forecasting models. J Hydrol 568:534–550

    Google Scholar 

  • Faris H, Aljarah I, Mirjalili S (2016) Training feedforward neural networks using multi-verse optimizer for binary classification problems. Appl Intell 45:322–332

    Google Scholar 

  • Fathian F, Mehdizadeh S, Kozekalani Sales A, Safari MJS (2019) Hybrid models to improve the monthly river flow prediction: integrating artificial intelligence and non-linear time series model. J Hydrol 575:1200–1213

    Google Scholar 

  • Garcia M, Portney K, Islam S (2016) A question driven socio-hydrological modeling process. Hydrol Earth Syst Sci 20(1):73–92

    Google Scholar 

  • Granger CWJ, Andersen A (1978) Non-linear time series modelling. In applied time series analysis I (pp. 25-38). Academic press

  • Hadi SJ, Tombul M (2018) Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination. J Hydrol 561:674–687

    Google Scholar 

  • He Z, Wen X, Liu H, Du J (2014) A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J Hydrol 509:379–386

    Google Scholar 

  • Jahani B, Mohammadi B (2019) A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran. Theor Appl Climatol 137(1–2):1257–1269

    Google Scholar 

  • Kisi O, Sanikhani H (2012) River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches. Water Resour Manag 26:1715–1729

    Google Scholar 

  • Kisi O, Nia AM, Gosheh MG, Tajabadi MRJ, Ahmadi A (2012) Intermittent streamflow forecasting by using several data driven techniques. Water Resour Manag 26(2):457–474

    Google Scholar 

  • Kisi O, Genc O, Dinc S, Zounemat-Kermani M (2016) Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree. Comput Electron Agric 122:112–117

    Google Scholar 

  • Kisi O, Sanikhani H, Cobaner M (2017) Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques. Theor Appl Climatol 129(3–4):833–848

    Google Scholar 

  • Liu Z, Zhou P, Chen G, Guo L (2014) Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting. J Hydrol 519(D):2822–2831

    Google Scholar 

  • Maroufpoor S, Bozorg-Haddad O, Maroufpoor E (2020) Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligent model: hybridization of artificial neural network with grey wolf optimizer algorithm Journal of Hydrology, 125060. 10.1016/j.jhydrol.2020.125060, 588

  • Mehdizadeh S (2018) Estimation of daily reference evapotranspiration (ETo) using artificial intelligence methods: offering a new approach for lagged ETo data-based modeling. J Hydrol 559:794–812

    Google Scholar 

  • Mehdizadeh S (2020) Using AR, MA, and ARMA time series models to improve the performance of MARS and KNN approaches in monthly precipitation modeling under limited climatic data. Water Resour Manag 34:263–282 https://doi.org/10.1007/s11269-019-02442-1

  • Mehdizadeh S, Kozekalani Sales A (2018) A comparative study of autoregressive, autoregressive moving average, gene expression programming and Bayesian networks for estimating monthly streamflow. Water Resour Manag 32(9):3001–3022

    Google Scholar 

  • Mehdizadeh S, Behmanesh J, Khalili K (2017) A comparison of monthly precipitation point estimates using integration of soft computing methods and GARCH time series model. J Hydrol 554:721–742

    Google Scholar 

  • Mehdizadeh S, Behmanesh J, Khalili K (2018) New approaches for estimation of monthly rainfall based on GEP-ARCH and ANN-ARCH hybrid models. Water Resour Manag 32(2):527–545

    Google Scholar 

  • Mehdizadeh S, Fathian F, Safari MJS, Adamowski JF (2019a) Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: a local and external data analysis approach. J Hydrol 579:124225

    Google Scholar 

  • Mehdizadeh S, Fathian F, Adamowski JF (2019b) Hybrid artificial intelligence-time series models for monthly streamflow modeling. Appl Soft Comput 80:873–887

    Google Scholar 

  • Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Google Scholar 

  • Moazenzadeh R, Mohammadi B (2019) Assessment of bio-inspired metaheuristic optimisation algorithms for estimating soil temperature. Geoderma 353:152–171

    Google Scholar 

  • 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

    Google Scholar 

  • Mohammadi B (2019a) Predicting total phosphorus levels as indicators for shallow lake management. Ecol Indic 107:105664

    Google Scholar 

  • Mohammadi B (2019b) “Prediction of effective climate change indicators using statistical downscaling approach and impact assessment on pearl millet (Pennisetum glaucum L.) yield through genetic algorithm in Punjab, Pakistan” by Asmat Ullah, Nasrin Salehnia, Sohrab Kolsoumi, Ashfaq Ahmad, Tasneem Khaliq. Ecol Indic 101:973–974

    Google Scholar 

  • Mohammadi B, Aghashariatmadari Z (2020) Estimation of solar radiation using neighboring stations through hybrid support vector regression boosted by krill herd algorithm. Arab J Geosci 13:363. https://doi.org/10.1007/s12517-020-05355-1

    Article  Google Scholar 

  • Mohammadi B, Mehdizadeh S (2020) Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm. Agric Water Manag 237:106145. https://doi.org/10.1016/j.agwat.2020.106145

    Article  Google Scholar 

  • Mohammadi B, Linh NTT, Pham QB, Ahmed AN, Vojteková J, Guan Y, Abba SI, El-Shafie A (2020) Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series. Hydrol Sci J:1–14. https://doi.org/10.1080/02626667.2020.1758703

  • Peugeot C, Cappelaere B, Vieux BE, Séguis L, Maia A (2003) Hydrologic process simulation of a semiarid, endoreic catchment in Sahelian West Niger. 1. Model-aided data analysis and screening. J Hydrol 279(1–4):224–243

    Google Scholar 

  • Pham QB, Afan HA, Mohammadi B, Ahmed AN, Linh NTT, Vo ND, ... El-Shafie A (2020) Hybrid model to improve the river streamflow forecasting utilizing multilayer perceptron-based intelligent water drop optimization algorithm. https://doi.org/10.1007/s00500-020-05058-5

  • Siqueira H, Boccato L, Luna I, Attux R, Lyra C (2018) Performance analysis of unorganized machines in streamflow forecasting of Brazilian plants. Appl Soft Comput 68:494–506

    Google Scholar 

  • Terzi O, Ergin G (2014) Forecasting of monthly river flow with autoregressive modeling and data-driven techniques. Neural Comput Applic 25:179–188

    Google Scholar 

  • Tikhamarine Y, Souag-Gamane D, Ahmed AN, Kisi O, El-Shafie A (2019a) Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey wolf optimization (GWO) algorithm. J Hydrol 582:124435

    Google Scholar 

  • Tikhamarine Y, Malik A, Kumar A, Souag-Gamane D, Kisi O (2019b) Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrol Sci J 64(15):1824–1842

    Google Scholar 

  • Tongal H, Booij MJ (2018) Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. J Hydrol 564:266–282

    Google Scholar 

  • Uysal G, Forman AA, Fensoy A (2016) Streamflow forecasting using different neural network models with satellite data for a snow dominated region in Turkey. Procedia Eng 154:1185–1192

    Google Scholar 

  • Vaheddoost B, Guan Y, Mohammadi B (2020) Application of hybrid ANN-whale optimization model in evaluation of the field capacity and the permanent wilting point of the soils. Sci Pollut Res 27:13131–13141. https://doi.org/10.1007/s11356-020-07868-4

    Article  Google Scholar 

  • Wang S, Huang G, Baetz B, Huang W (2016) Probabilistic inference coupled with possibilistic reasoning for robust estimation of hydrologic parameters and piecewise characterization of interactive uncertainties. J Hydrometeorol 17(4):1243–1260

    Google Scholar 

  • Yaseen ZM, Kisi O, Demir V (2016) Enhancing long-term streamflow forecasting and predicting using periodicity data component: application of artificial intelligence. Water Resour Manag 30(12):4125–4151

    Google Scholar 

  • Zhang H, Singh VP, Wang B, Yu Y (2016) CEREF: a hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system. J Hydrol 540:246–256

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China

    Babak Mohammadi & Yiqing Guan

  2. Department of Hydrology & Water Resources Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Farshad Ahmadi

  3. Water Engineering Department, Urmia University, Urmia, Iran

    Saeid Mehdizadeh

  4. Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam

    Quoc Bao Pham

  5. Faculty of Environmental and Chemical Engineering, Duy Tan University, Danang, 550000, Vietnam

    Quoc Bao Pham

  6. Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam

    Nguyen Thi Thuy Linh

  7. Sustainable Management of Natural Resources and Environment Research Group, Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Doan Quang Tri

Authors
  1. Babak Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farshad Ahmadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saeid Mehdizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yiqing Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Quoc Bao Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nguyen Thi Thuy Linh
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Doan Quang Tri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Doan Quang Tri.

Ethics declarations

Conflict of Interest

The authors declare no conflicts 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohammadi, B., Ahmadi, F., Mehdizadeh, S. et al. Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling. Water Resour Manage 34, 3387–3409 (2020). https://doi.org/10.1007/s11269-020-02619-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-020-02619-z

Keywords

Search

Navigation