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A stepwise-cluster forecasting approach for monthly streamflows based on climate teleconnections

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Abstract

In this study, a stepwise cluster forecasting (SCF) framework is proposed for monthly streamflow prediction in Xiangxi River, China. The developed SCF method can capture discrete and nonlinear relationships between explanatory and response variables. Cluster trees are generated through the SCF method to reflect complex relationships between independent (i.e. explanatory) and dependent (i.e. response) variables in the hydrologic system without determining specific linear/nonlinear functions. The developed SCF method is applied for monthly streamflow prediction in Xiangxi River based on the local meteorological records as well as some climate index. Comparison among SCF, multiple linear regression, generalized regression neural network, and least square support vector machine methods would be conducted. The results indicate that the SCF method would produce good predictions in both training and testing periods. Besides, the inherent probabilistic characteristics of the SCF predictions are further analyzed. The results obtained by SCF can presented as intervals, formulated by the minimum and maximum predictions as well as the 5 and 95 % percentile values of the predictions, which can reflect the variations in streamflow forecasts. Therefore, the developed SCF method can be applied for monthly streamflow prediction in various watersheds with complicated hydrologic processes.

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References

  • Adamowski J, Karapataki C (2008) Comparison of multivariate regression and artificial neural networks for peak urban water demand forecasting: the evaluation of different ANN learning algorithms. J Hydrol Eng (ASCE) 15(10):729–743

    Article  Google Scholar 

  • Adamowski J, Chan HF, Prasher SO, Ozga-Zielinski B, Sliusarieva A (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour Res 48:W01528

    Article  Google Scholar 

  • Anctill F, Rat A (2005) Evaluation of neural network streamflow forecasting on 47 watersheds. J Hydrol Eng 10:85–88

    Article  Google Scholar 

  • Chen Y, Cheng SY, Liu L, Guo XR, Wang Z, Qin CH, Hao RX, Lu J, Gao JJ (2013) Assessing the effects of land use changes on non-point source pollution reduction for the Three Gorges Watershed using the SWAT model. J Environ Inf 22(1):13–26

    Article  Google Scholar 

  • Chen X, Hao Z, Devineni N, Lall U (2014) Climate information based on streamflow and rainfall forecasts for Huai River basin using hierarchical Bayesian modelling. Hydrol Earth Syst Sci 18:1539–1548

    Article  Google Scholar 

  • Chiew FHS, McMahon TA (2002) Global ENSO-streamflow teleconnection, streamflow forecasting and interannual variability. Hydrol Sci J 47:505–522

    Article  Google Scholar 

  • Cigizoglu HK (2005) Generalized regression neural network in monthly flow forecasting. Civ Eng Environ Syst 22(2):71–84

    Article  Google Scholar 

  • Cooley WW, Lohnes PR (1971) Multivariate data analysis. Wiley, New York

    Google Scholar 

  • Dehghani M, Saghafian B, Rivaz F, Khodadadi A (2014) Monthly stream flow forecasting via dynamic spatio-temporal models. Stoch Environ Risk Assess. doi:10.1007/s00477-014-0967-3

    Google Scholar 

  • Deng J (1982) Control problems of grey systems. Syst Control Lett 1(5):288–294

    Article  Google Scholar 

  • Fan YR, Huang GH, Veawab A (2012) A generalized fuzzy linear programming approach for environmental management under uncertainty. J Air Waste Manag Assoc 62(1):72–86

    Article  CAS  Google Scholar 

  • Fan YR, Huang W, Huang GH, Huang K, Zhou X (2014) A PCM-based stochastic hydrological model for uncertainty quantification in watershed systems. Stoch Environ Risk Assess. doi:10.1007/s00477-014-0954-8

    Google Scholar 

  • Gao C, Gemmer M, Zeng X, Liu B, Su B, Wen Y (2010) Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network. Stoch Environ Risk Assess 24:685–697

    Article  Google Scholar 

  • Gu H, Yu Z, Wang G, Wang J, Ju Q, Yang C, Fan C (2014) Impact of climate change on hydrological extremes in the Yangtze River Basin. Stoch Environ Risk Assess, China. doi:10.1007/s00477-014-0957-5

    Google Scholar 

  • Han JC, Huang GH, Zhang H, Li Z, Li YP (2014) Bayesian uncertainty analysis in hydrological modeling associated with watershed subdivision level: a case study of SLURP model applied to the Xiangxi River watershed, China. Stoch Environ Res Risk Assess. doi:10.1007/s00477-013-0792-0

    Google Scholar 

  • He L, Huang GH, Lu HW, Zeng GM (2008) Optimization of surfactant-enhanced aquifer remediation for a Laboratory BTEX system under parameter uncertainty. Environ Sci Technol 42(6):2009–2014

    Article  CAS  Google Scholar 

  • Hu Q, Huang GH, Liu Z, Fan YR, Li W (2012) Inexact fuzzy two-stage programming for water resources management in an environment of fuzziness and randomness. Stoch Environ Res Risk Assess 26(2):261–280

    Article  Google Scholar 

  • Huang G (1992) A stepwise cluster analysis method for predicting air quality in an urban environment. Atmos Environ B 26(3):349–357

    Article  Google Scholar 

  • Huang GH, Cohen SJ, Yin YY (1996) Incorporation of inexact dynamic optimization with fuzzy relation analysis for integrated climate change impact study. J Environ Manag 48(1):45–68

    Article  Google Scholar 

  • Huang GH, Huang YF, Wang GQ, Xiao HN (2006) Development of a forecasting system for supporting remediation design and process control based on NAPL-biodegradation simulation and stepwise-cluster analysis. Water Resour Res 42:W06413

    Google Scholar 

  • Jin L, Huang GH, Fan YR, Nie XH, Cheng GH (2012) A hybrid dynamic dual interval programming for irrigation water allocation under uncertainty. Water Resour Manag 26(5):1183–1200

    Article  Google Scholar 

  • Kennedy WJ, Gentle JE (1981) Statistics: textbooks and monographs. Marcel Dekker, New York

    Google Scholar 

  • Kisi O, Akbari N, Sanatipour M, Hashemi A, Teimourzadeh K, Shiri J (2013) Modeling the dissolved oxygen in river water using artificial intelligence techniques. J Environ Inf 22(2):92–101

    Article  Google Scholar 

  • Kong XM, Huang GH, Fan YR, Li YP (2014) Maximum entropy-Gumbel-Hougaard copula method for simulation of monthly streamflow in Xiangxi River, China. Stoch Environ Res Risk Assess. doi:10.1007/s00477-014-0978-0

    Google Scholar 

  • Lai CH, Tseng MH (2010) Comparison of regression models, grey models, and supervised learning models for forecasting flood stage caused by typhoon events. J Chin Inst Eng 33(4):629–634

    Article  Google Scholar 

  • Li YP, Huang GH, Nie SL (2008) Inexact multistage stochastic integer programming for water resources management under uncertainty. J Environ Manag 88(1):93–107

    Article  CAS  Google Scholar 

  • Lv Y, Huang GH, Li YP (2010) Planning regional water resources system using an interval fuzzy bi-level programming method. J Environ Inf 16(2):43–56

    Article  Google Scholar 

  • Ma ZZ, Wang ZJ, Xia T, Gippel CJ, Speed R (2014) Hydrograph-based hydrologic alteration assessment and its application to the Yellow River. J Environ Inf 23(1):1–13

    Article  Google Scholar 

  • Mehr AD, Kahya E, Olyaie E (2013) Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. J Hydrol 505:240–249

    Article  Google Scholar 

  • Moriasi DN, Amold JG, Van Liew MW, Binger RL, Harmel RD, Veith T (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900

    Article  Google Scholar 

  • Morrison DF (1967) Multivariate statistical methods. McGraw-Hill, New York

    Google Scholar 

  • Okkan U, Serbes ZA (2012) Rainfall-runoff modeling using least squares support vector machines. Environmetrics 23:549–564

    Article  Google Scholar 

  • Overall JE, Klett CJ (1972) Applied multivariate analysis. McGraw-Hill, New York

    Google Scholar 

  • Pagano TC, Garen DC, Perkins TR, Pasteris PA (2009) Daily updating of operational statistical seasonal water supply forecasts for the western U.S. J Am Water Resour Assoc 45:767–778

    Article  Google Scholar 

  • Rao CR (1965) Linear statistical inference and its applications. Wiley, Hoboken

    Google Scholar 

  • Rasouli K, Hsieh WW, Cannon AJ (2012) Daily streamflow forecasting by machine learning methods with weather and climate inputs. J Hydrol 414–415:284–293

    Article  Google Scholar 

  • Robertson DE, Wang QJ (2012) A Bayesian approach to predictor selection for seasonal streamflow forecasting. J Hydrometeorol 13:155–171

    Article  Google Scholar 

  • Robertson DE, Pokhrel P, Wang QJ (2013) Improving statistical forecasts of seasonal streamflows using hydrological model output. Hydrol Earth Syst Sci 17:579–593

    Article  Google Scholar 

  • Rosenberg EA, Wood AW, Steinemann AC (2011) Statistical applications of physically based hydrologic models to seasonal streamflow forecasts. Water Resour Res 47:W00H14. doi:10.1029/2010wr010101

    Article  Google Scholar 

  • Sadri S, Burn DH (2012) Nonparametric methods for drought severity estimation at ungauged sites. Water Resour Res 48:W12505

    Article  Google Scholar 

  • Shiri J, Kisi O (2010) Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. J Hydrol 39:486–493

    Article  Google Scholar 

  • Tatsuoka MM (1971) Multivariate analysis. Wiley, Hoboken

    Google Scholar 

  • Turan EM, Yurdusev AM (2009) River flow estimation from upstream flow records by artificial intelligence methods. J Hydrol 369:71–77

    Article  Google Scholar 

  • Wang W, Van Gelder PH, Vrijling JK, Ma J (2006) Forecasting daily streamflow using hybrid ANN model. J Hydrol 324:383–399

    Article  Google Scholar 

  • 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 374:294–306

    Article  Google Scholar 

  • Wang S, Huang GH, He L (2012) Development of a clusterwise-linear-regression-based forecasting system for characterizing DNAPL dissolution behaviors in porous media. Sci Total Environ 433:141–150

    Article  CAS  Google Scholar 

  • Wang X, Huang G, Lin Q, Nie X, Cheng G, Fan Y, Li Z, Yao Y, Suo M (2013) A stepwise cluster analysis approach for downscaled climate projection—a Canadian case study. Environ Model Softw 49:141–151

    Article  Google Scholar 

  • Wang LZ, Huang YF, Wang L, Wang GQ (2014) Pollutant flushing characterization of stormwater runoff and their correlation with land use in a rapidly urbanizing watershed. J Environ Inf 23(1):37–43

    Article  Google Scholar 

  • Wilks SS (1962) Mathematical statistics. Wiley, New York

    Google Scholar 

  • Wu CL (2010) Hydrological predictions using data-driven models coupled with data preprocessing techniques. PhD Thesis, The Hong Kong Polytechnic University

  • Xu TY, Qin XS (2013) Solving water quality management problem through combined genetic algorithm and fuzzy simulation. J Environ Inf 22(1):39–48

    Article  Google Scholar 

  • Xu K, Brown C, Kown HH, Lall U, Zhang J, Hayashi S, Chen Z (2007) Climate teleconnections to Yangtze River seasonal streamflow at the Three Gorges Dam, China. Int J Climatol 27:771–780

    Article  Google Scholar 

  • Xu HM, Taylor RG, Kingston DG, Jiang T, Thompson JR, Todd MC (2010) Hydrological modeling of River Xiangxi using SWAT2005: a comparison of model parameterizations using station and gridded meteorological observations. Quatern Int 226:54–59

    Article  Google Scholar 

  • Yang AL, Huang GH, Qin XS, Fan YR (2012) Evaluation of remedial options for a benzene-contaminated site through a simulation-based fuzzy-MCDA approach. J Hazard Mater 213:421–433

    Article  Google Scholar 

  • Zhang Y, Yang D (2013) Simultaneous estimation of relative permeability and capillary pressure for tight formations using ensemble-based history matching method. Comput Fluid 71:446–460

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Natural Sciences Foundation (51190095, 51225904), the 111 Project (B14008), the Natural Science and Engineering Research Council of Canada, and the International Cooperation and Communication Project of XMUT (E201400200).

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Authors and Affiliations

  1. Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada

    Y. R. Fan, Z. Li, X. Q. Wang & G. H. Cheng

  2. Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada

    W. Huang

  3. Institute for Energy, Environment and Sustainability Research, UR-NCEPU, University of Regina, Regina, SK, S4S 0A2, Canada

    G. H. Huang

  4. MOE Key Laboratory of Regional Energy and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206, China

    G. H. Huang & Y. P. Li

  5. College of Environmental Science and Engineering, Xiamen University of Technology, Xiamen, 361024, Fujian, China

    L. Jin

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  1. Y. R. Fan
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  4. Z. Li
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Correspondence to G. H. Huang.

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Fan, Y.R., Huang, W., Huang, G.H. et al. A stepwise-cluster forecasting approach for monthly streamflows based on climate teleconnections. Stoch Environ Res Risk Assess 29, 1557–1569 (2015). https://doi.org/10.1007/s00477-015-1048-y

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