Advertisement

Forecasting riverine total nitrogen loads using wavelet analysis and support vector regression combination model in an agricultural watershed

Research Article
  • 31 Downloads

Abstract

In the context of non-point source pollution management and algal blooms control, the reliable nutrient forecasting is of critical importance. Considering the highly stochastic, non-linear, and non-stationary natures involved in riverine total nitrogen (TN) load time series data, some traditional statistical and artificial intelligence models are inherently unable to give accurate nutrient forecasts due to their mechanism and structure characteristics. In this study, based on the wavelet analysis (WA) and support vector regression (SVR), a promising combined WA-SVR model was proposed for forecasting riverine TN loads. The data pro-processing tool WA was employed to decompose the time series data of riverine TN load for revealing its dominator. Subsequently, all wavelet components were used as inputs to SVR for WA-SVR model. The continuous riverine TN loads during 2004–2012 in the ChangLe River watershed of eastern China were estimated by using a calibrated Load Estimator model. Performance criteria, namely, determination coefficient (R2), Nash-Sutcliffe model efficiency (NS), and mean square error (MSE) were applied to assess the performance of the developed models. The effects of different mother wavelets on the efficiency of the conjunction model were investigated. The results demonstrated that the mother wavelet played a crucial role for the successful implementation of the WA-SVR model. Among the 23 selected mother wavelet functions, dmey wavelet performed best in forecasting the daily and monthly TN loads. Furthermore, the performance of the optimal WA-SVR model was compared with that of single SVR model without wavelet decomposition. The comparison indicated that the hybrid model provided better accuracy than that of single SVR model. For daily riverine TN loads, the R2, NS, and MSE values of WA-SVR model during the test stage were 0.9699, 0.9658, and 0.4885 × 107 kg/day, respectively. For monthly riverine TN loads, the R2, NS, and MSE values of the model during the test stage were 0.9163, 0.9159, and 0.3237 × 1010 kg/month, respectively. The overall results strongly suggested that the combined WA-SVR method can successfully forecast riverine TN loads in agricultural watersheds.

Keywords

Forecasting Riverine TN loads Non-point source pollution Wavelet analysis Support vector regression model 

Notes

Acknowledgements

The authors would like to express appreciation to hydrological bureau of Zhejiang Province for the data provided for the ChangLe River.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 41571216) and the Chinese National Key Technology R&D Program (Grant No. 2016YFD0801103).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407(1–4):28–40CrossRefGoogle Scholar
  2. Adamowski J, Sun K (2010) Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J Hydrol 390:85–91CrossRefGoogle Scholar
  3. 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:W01528CrossRefGoogle Scholar
  4. Antanasijević DZ, Pocajt VV, Povrenović DS, Perić-Grujić AA, Ristić MD (2014) Modelling of dissolved oxygen content Danube River using artificial neural networks and Monte Carlo simulation uncertainty analysis. J Hydrol 519:1895–1907CrossRefGoogle Scholar
  5. Arnold JG, Fohrer N (2005) SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrol Process 19:563–572CrossRefGoogle Scholar
  6. Aussem A, Campbell J, Murtagh F (1998) Wavelet-based feature extraction and decomposition strategies for financial forecasting. J Comput Intell Finance 6(2):5–12Google Scholar
  7. Belayneh A, Adamowski J, Khalil B, Ozga-Zielinski B (2014) Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. J Hydrol 508:418–429CrossRefGoogle Scholar
  8. Borah DK, Bera M (2004) Watershed-scale hydrologic and nonpoint-source pollution models: reviews of application. Trans ASAE 47:789–803CrossRefGoogle Scholar
  9. Bowes MJ, Neal C, Jarvie HP, Smith JT, Davies HN (2010) Predicting phosphorus concentrations in British rivers resulting from the introduction of improved phosphorus removal from sewage effluent. Sci Total Environ 408:4239–4250CrossRefGoogle Scholar
  10. Cannas B, Fanni A, See L, Sias G (2006) Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Phys Chem Earth 31:1164–1171CrossRefGoogle Scholar
  11. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27CrossRefGoogle Scholar
  12. Chau KW (2017) Use of meta-heuristic techniques in rainfall-runoff modeling. Water 9(3):186–191CrossRefGoogle Scholar
  13. Chau KW, Wu CL (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinf 12(4):458–473CrossRefGoogle Scholar
  14. Chen XY, Chau KW (2016) A hybrid double feedforward neural network for suspended sediment load estimation. Water Resour Manag 30(7):2179–2194CrossRefGoogle Scholar
  15. Chen DJ, Lu J, Shen YN, Gong DQ, Deng OP (2011) Spatio-temporal variations of nitrogen in an agricultural watershed in eastern China: catchment export, stream attenuation and discharge. Environ Pollut 159:2989–2995CrossRefGoogle Scholar
  16. Chen DJ, Dahlgren RA, Lu J (2013) A modified load apportionment model for identifying point and diffuse source nutrient inputs to rivers from stream monitoring data. J Hydrol 501:25–34CrossRefGoogle Scholar
  17. Chou CM, Wang RY (2002) On-line estimation of unit hydrographs using the wavelet-based LMS algorithm. Hydrol Sci J 47(5):721–738CrossRefGoogle Scholar
  18. Chou WS, Lee TC, Lin JY, Shaw LY (2007) Phosphorus load reduction goals for Feitsui Reservoir watershed, Taiwan. Environ Monit Assess 131:395–408CrossRefGoogle Scholar
  19. Coffey SW, Line DE (1998) Simulation of dairy best management practices using the AGNPS model. J Lake Reserv Manage 14(4):417–427CrossRefGoogle Scholar
  20. Cristianine N, Taylor JS (2000) An introduction to support vector machine and other kernel based learning methods. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  21. Daubechies I (1992) Ten lectures on wavelets (CBMS-NSF regional conference series in applied mathematics). Soc Indust Appl MathGoogle Scholar
  22. Dibike YB, Velickov S, Solomatine DP, Abbott MB (2001) Model induction with support vector machines: introduction and applications. J Comput Civ Eng 15:208–216CrossRefGoogle Scholar
  23. Donigian AS, Imhoff JC, Bicknell BR, Kittle JL (1984) Application guide for the hydrological simulation program FORTRAN. GA7 Environmental Research Laboratory, US Environmental Protection Agency, AthensGoogle Scholar
  24. Feng Q, Wen XH, Li JG (2015) Wavelet analysis-support vector machine coupled models for monthly rainfall forecasting in arid regions. Water Resour Manag 29:1049–1065CrossRefGoogle Scholar
  25. Gao C, Zhang TL (2010) Eutrophication in a Chinese context: understanding various physical and socio-economic aspects. Ambio 39:385–393CrossRefGoogle Scholar
  26. Haar A (1910) Zur Theories der orthogonalen Funktionensysteme. Math Ann 69(3):331–371CrossRefGoogle Scholar
  27. He ZB, Wen XH, 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 509:379–386CrossRefGoogle Scholar
  28. Helsel DR, Hirsch RM (2002) Statistical methods in water resources. Techniques of water resources investigations, book 4, chapter A3. US Geological Survey, RestonGoogle Scholar
  29. Houser JN, Richardson WB (2010) Nitrogen and phosphorous in the Upper Mississippi River: transport, processing, and effects on the river ecosystem. Hydrobiologia 640:71–88CrossRefGoogle Scholar
  30. Howarth RW, Swaney D, Billen G, Garnier J, Hong B, Humborg C, Johnes P, Mörth CM, Marino R (2012) Nitrogen fluxes from the landscape are controlled by net anthropogenic nitrogen inputs and by climate. Front Ecol Environ 10:37–43CrossRefGoogle Scholar
  31. Hsu CW, Chang CC (2003) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
  32. Huang WR, Xu B, Chan-Hilton A (2004) Forecasting flows in Apala-chicola River using neural networks. Hydrol Process 18:2545–2564CrossRefGoogle Scholar
  33. Kalteh AM (2013) Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Comput Geosci-UK 54:1–8CrossRefGoogle Scholar
  34. Kalteh AM (2016) Improving forecasting accuracy of streamflow time series using least squares support vector machine coupled with data-preprocessing techniques. Water Resour Manag 30:747–766CrossRefGoogle Scholar
  35. Kasiviswanathan KS, He J, Sudheer KP, Tay JH (2016) Potential application of wavelet neural network ensemble to forecast streamflow for flood management. J Hydrol 536:161–173CrossRefGoogle Scholar
  36. Keerthi SS, Lin CJ (2001) Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput 15:1667–1689CrossRefGoogle Scholar
  37. Khan MS, Coulibaly P (2006) Application of support vector machine in lake water level prediction. J Hydrol Eng 11:199–205CrossRefGoogle Scholar
  38. Kisi O (2010) Daily suspended sediment estimation using neuro-wavelet models. Int J Earth Sci 99:1471–1482CrossRefGoogle Scholar
  39. Kisi O (2012) Modeling discharge-suspended sediment relationship using least square support vector machine. J Hydrol 456-457:110–120CrossRefGoogle Scholar
  40. Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J Hydrol 399:132–140CrossRefGoogle Scholar
  41. Kisi O, Shiri J (2011) Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resour Manag 25(13):3135–3152CrossRefGoogle Scholar
  42. Kothyari UC, Singh VP (1999) A multiple-input single-output model for flow forecasting. J Hydrol 220:12–26CrossRefGoogle Scholar
  43. Legates DR, McCabe GJ Jr (1999) Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241CrossRefGoogle Scholar
  44. Li S, Zhang Q (2010) Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques. J Hazard Mater 176:579–588CrossRefGoogle Scholar
  45. Lin JY, Cheng CT, Chau KW (2006) Using support vector machines for long term discharge prediction. Hydrol Sci J 51:599–612CrossRefGoogle Scholar
  46. Liu KJ, Shi ZH, Fang NF, Zhu HD, Ai L (2013) Modeling the daily suspended sediment concentration in a hyper concentrated river on the Loess Plateau, China, using the Wavelet-ANN approach. Geomorphology 186:181–190CrossRefGoogle Scholar
  47. Lu RY (2002) Decomposition of interdecadal and interannual components for north China rainfall in rainy season. Chinese J Atmos 26:611–624 (In Chinese)Google Scholar
  48. Meyer Y (1985) Principe d’incerlitude, bases hilbertiennes et algebres d’operateurs., Asterisque, Societe Mathematique de France, Paris, FranceGoogle Scholar
  49. Minu KK, Lineesh MC, John CJ (2010) Wavelet neural networks for nonlinear time series analysis. Appl Math Sci 4(50):2485–2495Google Scholar
  50. Mishra AK, Desai VR (2005) Drought forecasting using stochastic models. Stoch Env Res Risk A 19:326–339CrossRefGoogle Scholar
  51. Mohammadpour R, Shaharuddin S, Chang CK, Zakaria NA, Ghani AA, Chan NW (2015) Prediction of water quality index in constructed wetlands using support vector machine. Environ Sci Pollut Res 22:6208–6219CrossRefGoogle Scholar
  52. Morse NB, Wollheim WM (2014) Climate variability masks the impacts of land use change on nutrient export in a suburbanizing watershed. Biogeochemistry 121:45–59CrossRefGoogle Scholar
  53. Nabavi-Pelesaraei A, Bayat B, Hosseinzadeh-Bandbafha H, Afrasyabi H, Chau KW (2017) Modeling of energy consumption and environmental life cycle assessment for incineration and landfill systems of municipal solid waste management—a case study in Tehran Metropolis of Iran. J Clean Prod 148:427–440CrossRefGoogle Scholar
  54. Najah A, El-Shafie A, Karim OA, El-Shafie AH (2014) Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring. Environ Sci Pollut Res 21(13):1658–1670Google Scholar
  55. Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A, Ghafari Gousheh M (2011) Assessment of input variables determination on the SVM model performance using PCA, gamma test, and forward selection techniques for monthly stream flow prediction. J Hydrol 401:177–189CrossRefGoogle Scholar
  56. Noori R, Yeh HD, Abbasi M, Kachoosangi FT (2015) Uncertainty analysis of support vector machine for online prediction of five-day biochemical oxygen demand. J Hydrol 527:833–843CrossRefGoogle Scholar
  57. Nourani V, Alami MT, Aminfar MH (2009a) A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intell 22:466–472CrossRefGoogle Scholar
  58. Nourani V, Komasi M, Mano A (2009b) A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water Resour Manag 23(14):2877–2894CrossRefGoogle Scholar
  59. Nourani V, Baghanam AH, Adamowski J, Kisi O (2014) Applications of hybrid wavelet-artificial intelligence models in hydrology: a review. J Hydrol 514:358–377CrossRefGoogle Scholar
  60. Olyaie E, Banejad H, Chau KW, Melesse AM (2015) A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. Environ Monit Assess 187:189CrossRefGoogle Scholar
  61. Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342(1):199–212CrossRefGoogle Scholar
  62. Partal T, Kucuk M (2006) Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey). Phys Chem Earth 31:1189–1200CrossRefGoogle Scholar
  63. Raghavendra NS, Deka PC (2014) Support vector machine applications in the field of hydrology: a review. Appl Soft Comput 19:372–386CrossRefGoogle Scholar
  64. Ravansalar M, Rajaee T, Kisi O (2017) Wavelet-linear genetic programming: a new approach for modeling monthly streamflow. J Hydrol 549:461–475CrossRefGoogle Scholar
  65. Runkel RL, Crawford CG, Cohn TA (2004) Load Estimator (LOADEST): A FORTRAN program for estimating constituent loads in streams and rivers, techniques and methods report 4-A5 U.S. Geological Survey, RestonGoogle Scholar
  66. Ryusuke H, Takuro S, Zheng TG, Masahiko O, Li ZW (2002) Nitrogen budgets and environmental capacity in farm systems in a large-scale karst region, southern China. Nutr Cycl Agroecosyst 63:139–149CrossRefGoogle Scholar
  67. Sefeedpari P, Rafiee S, Akram A, Chau KW, Pishgar-Komleh SH (2016) Prophesying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach. Comput Electron Agric 131:10–19CrossRefGoogle Scholar
  68. Seo WM, Kim SW, Kisi O, Singh VP (2015) Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. J Hydrol 520:224–243CrossRefGoogle Scholar
  69. Shen ZY, Qiu JL, Hong Q, Chen L (2014) Simulation of spatial and temporal distributions of non-point source pollution load in the three gorges reservoir region. Sci Total Environ 493:138–146CrossRefGoogle Scholar
  70. Shiri J, Kisi O (2010) Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. J Hydrol 394:486–493CrossRefGoogle Scholar
  71. Shoaib M, Shamseldin AY, Melville BW (2014) Comparative study of different wavelet based neural network models for rainfall–runoff modeling. J Hydrol 515:47–58CrossRefGoogle Scholar
  72. Shoaib M, Shamseldin AY, Melville BW, Khan MM (2016) A comparison between wavelet based static and dynamic neural network approaches for runoff prediction. J Hydrol 535:211–225CrossRefGoogle Scholar
  73. Shu C, Ouarda TBMJ (2008) Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J Hydrol 349:31–43CrossRefGoogle Scholar
  74. Simith LC, Turcotte DL, Isacks B (1998) Stream flow characterization and feature detection using a discrete wavelet transform. Hydrol Process 12:233–249CrossRefGoogle Scholar
  75. Singh KP, Basant N, Gupta S (2011) Support vector machines in water quality management. Anal Chim Acta 703:152–162CrossRefGoogle Scholar
  76. Singh KP, Gupta S, Rai P (2014) Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data. Environ Monit Assess 186:2749–2765CrossRefGoogle Scholar
  77. State Environment Protection Bureau of China (2002) Water and wastewater analysis method. China Environmental Science Press, Beijing (in Chinese)Google Scholar
  78. Stolojescu CL (2012) A wavelets based approach for time series mining, Ph.D. Dissertation, Telecom Bretagne, FranceGoogle Scholar
  79. Tiwari MK, Chatterjee C (2010) Development of an accurate and reliable hourly flood forecasting model using wavelet-bootstrap-ANN (WBANN) hybrid approach. J Hydrol 394(3–4):458–470CrossRefGoogle Scholar
  80. Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkCrossRefGoogle Scholar
  81. Vapnik VN (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar
  82. Wang W, Ding J (2003) Wavelet network model and its application to the prediction of hydrology. Nat Sci 1:67–71Google Scholar
  83. Wang J, Du HY, Liu HX, Yao XJ, Hu ZD, Fan BT (2007) Prediction of surface tension for common compounds based on novel methods using heuristic method and support vector machine. Talanta 73:147–156CrossRefGoogle Scholar
  84. Wang FE, Tian P, Yu J, Lao GM, Shi TC (2011) Variations in pollutant fluxes of rivers surrounding Taihu Lake in Zhejiang Province in 2008. Phys Chem Earth 36(9–11):366–371CrossRefGoogle Scholar
  85. Wang WC, Xu DM, Chau KW, Lei GJ (2014) Assessment of river water quality based on theory of variable fuzzy sets and fuzzy binary comparison method. Water Resour Manag 28(12):4183–4200CrossRefGoogle Scholar
  86. Wei S, Yang H, Song JX, Abbaspour K, Xu ZX (2013) A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrol Sci J 58:374–389CrossRefGoogle Scholar
  87. Wu CL, Chau KW, Li YS (2009a) Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques. Water Resour Res 45:W08432CrossRefGoogle Scholar
  88. Wu CL, Chau KW, Li YS (2009b) Methods to improve neural network performance in daily flows prediction. J Hydrol 372(1):80–93CrossRefGoogle Scholar
  89. Wu ZZ, Xu EB, Long J, Wang F, Xu XM, Jin ZY, Jiao AQ (2015) Rapid measurement of antioxidant activity and γ-aminobutyric acid content of Chinese rice wine by fourier-transform near infrared spectroscopy. Food Anal Methods 8:2541–2553CrossRefGoogle Scholar
  90. Yaseen ZM, Jaafar O, Deo RC, Kisi O, Adamowski J, Quilty J, EI-Shafie A (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. J Hydrol 542:603–614CrossRefGoogle Scholar
  91. Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396:128–138CrossRefGoogle Scholar
  92. Zhang Y, Cong Q, Xie YF, Yang JX, Zhao B (2008) Quantitative analysis of routine chemical constituents in tobacco by near-infrared spectroscopy and support vector machine. Spectrichimica Acta Part A 71:1408–1413CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Environment and Natural ResourcesZhejiang UniversityHangzhouChina
  2. 2.China Ministry of Education Key Lab of Environment Remediation and Ecological HealthZhejiang UniversityHangzhouChina

Personalised recommendations