Volume 14 Issue 3
Sep.  2021
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Jin-xiao Zhao, Wen-hao Ding, Shi-kai Xu, Shi-ping Ruan, Yong Wang, Sen-lin Zhu. 2021: Prediction of sediment resuspension in Lake Taihu using support vector regression considering cumulative effect of wind speed. Water Science and Engineering, 14(3): 228-236. doi: 10.1016/j.wse.2021.08.002
Citation: Jin-xiao Zhao, Wen-hao Ding, Shi-kai Xu, Shi-ping Ruan, Yong Wang, Sen-lin Zhu. 2021: Prediction of sediment resuspension in Lake Taihu using support vector regression considering cumulative effect of wind speed. Water Science and Engineering, 14(3): 228-236. doi: 10.1016/j.wse.2021.08.002

Prediction of sediment resuspension in Lake Taihu using support vector regression considering cumulative effect of wind speed

doi: 10.1016/j.wse.2021.08.002
Funds:

This work was supported by the National Natural Science Foundation of China (Grant No. 42001028), the Jiangsu Provincial Major Engineering Consultant Project (Grants No. Hj120058 and Hj920019), and the Central Public-Interest Scientific Institution Basal Research Fund (Grants No. Y120014 and Y119013).

  • Received Date: 2020-11-20
  • Accepted Date: 2021-05-19
  • Available Online: 2021-10-11
  • Sediment resuspension is critical to ecosystem function in shallow lakes. Turbidity is one of the most commonly used indicators of sediment resuspension and has proven to be strongly related with wind. However, it is still difficult to predict sediment resuspension due to its complicated mechanisms. In this study, a support vector regression (SVR) model considering the cumulative effect of wind speed was trained to predict sediment resuspension based on intensified field observations at two sites in Lake Taihu. The accuracy of the SVR model was evaluated, and the initial turbidity was introduced to the model to illustrate its contribution to sediment resuspension. The critical wind speed was also evaluated based on this model. The results indicate that the SVR model considering the cumulative effect of wind speed can increase the accuracy of prediction in comparison with traditional fitting methods. The root-mean-square error (RMSE) of the predicted turbidity dropped to 11.36 NTU at one site and 16.78 NTU at the other, and the maximal information coefficient (cimax) for the relationship between wind speed and turbidity increased to 0.56 for both observation sites. The introduction of initial turbidity significantly improved the performance of the SVR model, with an RMSE value lower than 8.00 NTU and a cimax value higher than 0.95. Analysis of the critical wind speed using the SVR model shows that the critical wind speed generally increased with the rise of initial turbidity. The critical wind speeds at initial turbidities of 30, 40, 50, and 60 NTU were 5, 6, 7, and 7 m/s, respectively.

     

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  • Anderson, E.J., Schwab, D.J., 2011. Relationships between wind-driven and hydraulic flow in Lake St. Clair and the St. Clair River Delta. J. Great Lake. Res. 37(1), 147-158. https://doi.org/10.1016/j.jglr.2010.11.007.
    Bessell-Browne, P., Negri, A.P., Fisher, R., Clode, P.L., Duckworth, A., Jones, R., 2017. Impacts of turbidity on corals: The relative importance of light limitation and suspended sediments. Mar. Pollut. Bull. 117(1-2), 161-170. https://doi.org/10.1016/j.marpolbul.2017.01.050.
    Cervi, E.C., Hudson, M., Rentschler, A., Burton Jr., G.A., 2019. Metal toxicity during short-term sediment resuspension and redeposition in a tropical reservoir. Environ. Toxicol. Chem. 38(7), 1476-1485. https://doi.org/10.1002/etc.4434.
    Chang, M.J., Lin, G.F., Lee, F.Z., Wang, Y.C., Chen, P.A., Wu, M.C., Lai, J.S., 2020. Outflow sediment concentration forecasting by integrating machine learning approaches and time series analysis in reservoir desilting operation. Stoch. Environ. Res. Risk Assess. 34, 849-866. https://doi.org/10.1007/s00477-020-01802-3.
    Cózar, A., Gálvez, A.J., Hull, V., Carcia, M.C., Loiselle, S.A., 2005. Sediment resuspension by wind in a shallow lake of Esteros del iberá (Argentina): A model based on turbidimetry. Ecol. Model. 186(1), 63-76. https://doi.org/10.1016/j.ecolmodel.2005.01.020.
    Ding, W.H., Zhao, J.X., Qin, B.Q., Wu, T.F., Zhu, S.L., Li, Y., Xu, S.K., Ruan, S.P., Wang, Y., 2021. Exploring and quantifying the relationship between instantaneous wind speed and turbidity in a large shallow lake:Case study of Lake Taihu in China. Environ. Sci. Pollut. Res. Int. 28(13), 16616-16632. https://doi.org/10.1007/S11356-020-11544-Y.
    Donelan, M.A., Dobson, F.W., Smith, S.D., Anderson, R.J., 1993. On the dependence of sea surface roughness on wave development. J. Phys. Oceanogr. 23(9) 2143-2149. https://doi.org/10.1175/1520-0485(1993)023<2143:OTDOSS>2.0.CO.
    Goldstein, E.B., Goco, G., 2014. A machine learning approach for the prediction of settling velocity. Water Resour. Res. 50(4), 3595-3601. https://doi.org/10.1002/2013WR015116.
    Han, H.J., Hu, W.P., Jin, Y.Q., 2008. Numerical experiments of influence of wind speed on current in lake. Oceanol. Limnol. Sinica 39(6), 567-576 (in Chinese). https://doi.org/10.3321/j.issn:0029-814X.2008.06.005.
    Jalil, A., Li, Y.P., Du, W., Wang, W.C., Wang, J.W., Gao, X.M., Khan, H.O.S., Pan, B.Z., Acharya, K., 2018. The role of wind field induced flow velocities in destratification and hypoxia reduction at Meiling Bay of large shallow Lake Taihu, China. Environ. Pollut. 232, 591-602. https://doi.org/10.1016/j.envpol.2017.09.095.
    Jalil, A., Li, Y.P., Zhang, K., Gao, X.M., Wang, W.C., Khan, H.O.S., Pan, B., Ali, S., Acharya, K., 2019. Wind-induced hydrodynamic changes impact on sediment resuspension for large, shallow Lake Taihu, China. Int. J. Sediment Res. 34(3), 205-215. https://doi.org/10.1016/j.ijsrc.2018.11.003.
    Johnson, H.K., Vested, H.J., Hjstrup, J., Larsen, S.E., 1996. On the dependence of sea surface roughness on wind waves. J. Phys. Oceanogr. 28(9) 1702-1716. https://doi.org/10.1175/1520-0485(1998)028<1702:OTDOSS>2.0.CO.
    Li, L., Wang, C., 2019. Comparison and application of three prediction models based on BP, Elman and PSO-SVR in Shiyang River Basin. China Rural Water Hydropower (9), 28-32 (in Chinese).
    Li, Y.M., Sun, H.J., Yan, W.Z., Zhang, X.Q., 2020. Multi-output parameterinsensitive kernel twin SVR model. Neural Netw. 121, 276-293. https://doi.org/10.1016/j.neunet.2019.09.022.
    Li, Y.P., Jalil, A., Du, W., Gao, X.M., Wang, J.W., Luo, L.C., Li, H.Y., Dai, S.J., Hashim, S., Yu, Z.B., et al., 2017a. Wind induced reverse flow and vertical profile characteristics in a semi-enclosed bay of large shallow Lake Taihu, China. Ecol. Eng. 102, 224-233. https://doi.org/10.1016/j.ecoleng.2017.02.022.
    Li, Y.P., Tang, C.Y., Wang, J.W., Acharya, K., Du, W., Gao, X.M., Luo, L.C., Li, H.Y., Dai, S.J., Mercy, J., et al., 2017b. Effect of wave-current interactions on sediment resuspension in large shallow Lake Taihu, China. Environ. Sci. Pollut. Control Ser. 24(4), 4029-4039. https://doi.org/10.1007/s11356-016-8165-0.
    Liu, X.D., Li, L.Q., Wang, P., Hua, Z.L., Gu, L., 2019. Numerical simulation of wind-driven circulation and pollutant transport in Taihu Lake based on a quadtree grid. Water Sci. Eng. 12(2), 108-114. https://doi.org/10.1016/j.wse.2019.05.001.
    Pérez, G.L., Lagomarsino, L., Zagarese, H.E., 2013. Optical properties of highly turbid shallow lakes with contrasting turbidity origins: The ecological and water management implications. J. Environ. Manag. 130(30), 207-220. https://doi.org/10.1016/j.jenvman.2013.09.001.
    Qian, J., Zheng, S.S., Wang, P.F., Wang, C., 2011. Experimental study on sediment resuspension in Taihu Lake under different hydrodynamic disturbances. J. Hydrodyn. 23(6), 826-833. https://doi.org/10.1016/S1001-6058(10)60182-5.
    Reshef, D.N., Reshef, Y.A., Finucane, H.K., Grossman, S.R., McVean, G., Turnbaugh, P.J., Lander, E.S., Mitzenmacher, M., Sabeti, P.C., 2011. Detecting novel associations in large data sets. Science 334(6062), 1518-1524. https://doi.org/10.1126/science.1205438.
    Sharifian, S., Barati, M., 2019. An ensemble multiscale wavelet-GARCH hybrid SVR algorithm for mobile cloud computing workload prediction. Int. J. Mach. Learn. Cybern. 10(11), 3285-3300. https://doi.org/10.1007/s13042-019-01017-1.
    Tang, C.Y., Li, Y.P., He, C., Acharya, K., 2019. Dynamic behavior of sediment resuspension and nutrients release in the shallow and wind-exposed Meiliang Bay of Lake Taihu. Sci. Total Environ. 708, 131-135. https://doi.org/10.1016/j.scitotenv.2019.135131.
    Wang, J.J., Pang, Y., Li, Y.P., Huang, Y., Luo, J., 2015. Experimental study of wind-induced sediment suspension and nutrient release in Meiliang Bay of Lake Taihu, China. Environ. Sci. Pollut. Res. Int. 22(14), 10471-10479. https://doi.org/10.1007/s11356-015-4247-7.
    Wu, J., Wu, Z.Y., Lin, H.J., Ji, H.P., Liu, M., 2020. Hydrological response to climate change and human activities: A case study of Taihu Basin, China.
    Water Sci. Eng. 13(2), 83-94. https://doi.org/10.1016/j.wse.2020.06.006.
    Xu, H., Paerl, H.W., Qin, B.Q., Zhu, G.W., Gaoa, G., 2010. Nitrogen and phosphorus inputs control phytoplankton growth in eutrophic Lake Taihu, China. Limnol. Oceanogr. 55(1), 420-432. https://doi.org/10.4319/lo.2010.55.1.0420.
    Yang, T.M., Fan, S.K., Fan, C., Hsu, N.S., 2014. Establishment of turbidity forecasting model and early-warning system for source water turbidity management using back-propagation artificial neural network algorithm and probability analysis. Environ. Monit. Assess. 186(8), 4925-4934.
    https://doi.org/10.1007/s10661-014-3748-z.
    You, B.S., Zhong, J.C., Fan, C.X., Wang, T.C., Zhang, L., Ding, S.M., 2007. Effects of hydrodynamics processes on phosphorus fluxes from sediment in large shallow Lake Taihu. J. Environ. Sci. 19(9), 1055-1060. https://doi.org/10.1016/S1001-0742(07)60172-7.
    Yu, T., Zhang, Y., Zhang, Y., 2012. Distribution and bioavailability of heavy metals in different particle-size fractions of sediments in Taihu Lake, China. Chem. Speciat. Bioavailab. 24(4), 205-215. https://doi.org/10.3184/095422912X13488240379124.
    Zheng, S.S., Wang, P.F., Wang, C., Hou, J., 2015. Sediment resuspension under action of wind in Taihu Lake, China. Int. J. Sediment Res. 30(1), 48-62. https://doi.org/10.1016/s1001-6279(15)60005-1.
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