A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

Environmental Research

Volumn 160, Issue , 2018, Pages 269-281

  Wang, Dong ^a   Borthwick, Alistair G ^b   He, Handan ^a   Wang, Yuankun ^a   Zhu, Jieyu ^a   Lu, Yuan ^a   Xu, Pengcheng ^a   Zeng, Xiankui ^a   Wu, Jichun ^a   Wang, Lachun ^a   Zou, Xinqing ^a   Liu, Jiufu ^c   Zou, Ying ^c   He, Ruimin ^c  

^a NANJING UNIVERSITY   (China)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

^c NANJING HYDRAULIC RESEARCH INSTITUTE   (China)

Author keywords

Data driven model; Forecasting; Hydro meteorological series; Rank Set Pair Analysis; Wavelet de noising

Indexed keywords

FORECASTING METHOD; HYDROMETEOROLOGY; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; TIME SERIES ANALYSIS; WAVELET ANALYSIS;

ANALYSIS; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTO REGRESSIVE INTEGRATED MOVING AVERAGE METHOD; FORECASTING; HYDRO METEOROLOGICAL TIME SERIES; METHODOLOGY; PHYSICAL PHENOMENA; PRIORITY JOURNAL; RANK SET PAIR ANALYSIS; WAVELET DE NOISING ANALYSIS; CHINA; DROUGHT; EVALUATION STUDY; FLOODING; PROCEDURES; RIVER; WAVELET ANALYSIS;

CHINA; DROUGHTS; FLOODS; FORECASTING; RIVERS; WAVELET ANALYSIS;

EID: 85033384606     PISSN: 00139351     EISSN: 10960953     Source Type: Journal    
DOI: 10.1016/j.envres.2017.09.033     Document Type: Article

References (69)

1. Adamowski, J., Chan, H.F., Prasher, S.O., Ozga-Zielinski, B., Sliusarieva, A., 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(1), 2012, W01528.
2. Adamowski, J., Sun, K., Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J. Hydrol. 390:1-2 (2010), 85–91.
3. Bardossy, A., Plate, E.J., Space-time model for daily rainfall using atmospheric circulation patterns. Water Resour. Res. 28:5 (1992), 1247–1259.
4. Bardossy, A., Li, J., Geostatistical interpolation using copulas. Water Resour. Res., 44(7), 2008, W07412.
5. Belayneh, A., Adamowski, J., Drought forecasting using new machine learning methods/Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod. J. Water Land Dev. 18:9 (2013), 3–12.
6. Belayneh, A., Adamowski, J., Khalil, B., Ozga-Zielinski, B., Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. J. Hydrol. 508:2 (2014), 418–429.
7. Belayneh, A., Adamowski, J., Khalil, B., Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods. Sustain. Water Resour. Manag. 2:1 (2016), 87–101.
8. Cai, X.M., Wang, D.B., Zhu, T.J., Ringler, C., Assessing the regional variability of GCM simulations. Geophys. Res. Lett., 36, 2009, L02706, 10.1029/2008GL036443.
9. Chanerley, A.A., Alexander, N.A., Correcting data from an unknown accelerometer using recursive least squares and wavelet de-noising. Comput. Struct. 85 (2007), 1679–1692.
10. Chen, Q.W., Li, J., Li, R.N., Wei, W.D., Wang, L.M., River basin water resource compensation characteristics by set pair analysis: the Dongjiang example. Front. Earth Sci. 8:1 (2014), 64–69, 10.1007/s11707-013-0389-4.
11. Chen, Y., Li, Z., Fan, Y., Wang, H., Deng, H., Progress and prospects of climate change impacts on hydrology in the arid region of northwest china. Environ. Res. 139 (2015), 11–19.
12. Chong, T., Yi, S., Heng, C., Application of set pair analysis method on occupational hazard of coal mining. Saf. Sci. 92 (2017), 10–16, 10.1016/j.ssci.2016.09.005.
13. Chou, C.M., Application of set pair analysis-based similarity forecast model and wavelet denoising for runoff forecasting. Water 6:4 (2014), 912–928, 10.3390/w6040912.
14. Donoho, D.L., De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41 (1995), 613–627, 10.1109/18.382009.
15. Du, C.Y., Yu, J.J., Zhong, H.P., Wang, D.D., Operating mechanism and set pair analysis model of a sustainable water resources system. Front. Environ. Sci. Eng. 9:2 (2015), 288–297, 10.1007/s11783-014-0642-4.
16. Elshorbagy, A., Simonovic, S.P., Panu, U.S., Noise reduction in chaotic hydrologic time series: facts and doubts. J. Hydrol. 256 (2002), 147–165.
17. Feng, L.H., Sang, G.S., Hong, W.H., Statistical prediction of changes in water resources trends based on set pair analysis. Water Resour. Manag. 28:6 (2014), 1703–1711, 10.1007/s11269-014-0581-7.
18. Gao, X., Sorooshian, S., A stochastic precipitation disaggregation scheme for GCM application. J. Clim. 7:2 (1994), 238–247.
19. Ghosh, S., Mujumdar, P.P., Statistical downscaling of GCM simulations to streamflow using relevance vector machine. Adv. Water Resour. 31:1 (2008), 132–146.
20. Guo, E.L., Zhang, J.Q., Ren, X.H., Zhang, Q., Sun, Z.Y., Integrated risk assessment of flood disaster based on improved set pair analysis and the variable fuzzy set theory in central Liaoning Province, China. Nat. Hazards 74:2 (2014), 947–965, 10.1007/s11069-014-1238-9.
21. Han, D., Bray, M., Automated Thiessen polygon generation. Water Resour. Res., 42(11), 2006, W11502.
22. Han, D., Kwong, T., Li, S., Uncertainties in real-time flood forecasting with neural networks. Hydrol. Process. 21:2 (2007), 223–228.
23. He, Z., Wen, X., Liu, H., Du, J., 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 (2014), 379–386.
24. Hong, M., Wang, D., Wang, Y., Zeng, X., Ge, S., Yan, H., Singh, V.P., Mid- and long-term runoff predictions by an improved phase-space reconstruction model. Environ. Res. 148 (2016), 560–573.
25. Hou, Z., Lu, W., Xue, H., Lin, J., A comparative research of different ensemble surrogate models based on set pair analysis for the DNAPL-contaminated aquifer remediation strategy optimization. J. Contam. Hydrol. 203 (2017), 28–37, 10.1016/j.jconhyd.2017.06.003.
26. Jiang, X., Xu, S.G., Liu, Y.Y., Wang, X.D., River ecosystem assessment and application in ecological restorations: a mathematical approach based on evaluating its structure and function. Ecol. Eng. 76 (2015), 151–157, 10.1016/j.ecoleng.2014.04.027.
27. Jin, J.L., Fu, J., Wei, Y.M., Jiang, S.M., Zhou, Y.L., Liu, L., Wang, Y.Z., Wu, C.G., Integrated risk assessment method of waterlog disaster in Huaihe River Basin of China. Nat. Hazards 75:s2 (2015), S155–S178, 10.1007/s11069-014-1307-0.
28. Jin, J.L., Wei, Y.M., Zou, L.L., Liu, L., Zhang, W.W., Zhou, Y.L., Forewarning of sustainable utilization of regional water resources: a model based on BP neural network and set pair analysis. Nat. Hazards 62:1 (2012), 115–127, 10.1007/s11069-011-0037-9.
29. Kavvas, M., Kure, S., Chen, Z., Ohara, N., Jang, S., WEHY-HCM for modeling interactive atmospheric-hydrologic processes at watershed scale. I: model description. J. Hydrol. Eng. 18:10 (2013), 1262–1271, 10.1061/(ASCE)HE.1943-5584.0000724.
30. Labat, D., Recent advances in wavelet analyses: Part I. A review of concepts. J. Hydrol. 314 (2005), 275–288, 10.1016/j.jhydrol.2005.04.003.
31. Leung, L.Y.R., Ringler, T., Collins, W.D., Taylor, M., Ashfaq, M., A hierarchical evaluation of regional climate simulations. EOS 94:34 (2013), 297–298, 10.1002/2013EO340001.
32. Li, C., Sun, L., Jia, J., Cai, Y., Wang, X., Risk assessment of water pollution sources based on an integrated k-means clustering and set pair analysis method in the region of Shiyan, China. Sci. Total Environ. 557 (2016), 307–316.
33. Liu, D., Wang, D., Wang, Y., Wang, L., Zou, X., Wd-rbf model and its application of hydrologic time series prediction. J. Risk Anal. Crisis Response 3:4 (2013), 185–191.
34. Mehran, A., Mazdiyasni, O., AghaKouchak, A., A hybrid framework for assessing socioeconomic drought: linking climate variability, local resilience, and demand. J. Geophys. Res. D (Atmos.) 120 (2015), 7520–7533, 10.1002/2015JD023147.
35. Minville, M., Brissette, F., Leconte, R., Uncertainty of the impact of climate change on the hydrology of a nordic watershed. J. Hydrol. 358 (2008), 70–83, 10.1016/j.jhydrol.2008.05.033.
36. Nalley, D., Adamowski, J., Khalil, B., Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008). J. Hydrol. 475 (2012), 204–228.
37. Nourani, V., Baghanam, A.H., Adamowski, J., Gebremichael, M., Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling. J. Hydrol. 476 (2013), 228–243.
38. Ou, Y., Zhang, Q., Wang, W.S., Song, X.B., Annual runoff forecasting model based on Rank-Set Pair Analysis. Yangtze River 40:3 (2009), 63–65.
39. Pan, Z., Wang, Y., Jin, J., Liu, X., Set pair analysis method for coordination evaluation in water resources utilizing conflict. Phys. Chem. Earth Parts A/B/C 05:09 (2017), 1–8.
40. Pulido-Calvo, I., Portela, M.M., Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds. J. Hydrol. 332 (2007), 1–15.
41. Qian, Y., Leung, L.R., A long-term regional simulation and observations of the hydroclimate in China. J. Geophys. Res D (Atmos.), 112, 2007, D14, 10.1029/2006JD008134.
42. Schaefli, B., Maraun, D., Holschneider, M., What drives high flow events in the Swiss Alps? Recent developments in wavelet spectral analysis and their application to hydrology. Adv. Water Resour. 30 (2007), 2511–2525.
43. Shoaib, M., Shamseldin, A.Y., Melville, B.W., Khan, M.M., Runoff forecasting using hybrid Wavelet Gene Expression Programming (WGEP) approach. J. Hydrol. 527 (2015), 326–344.
44. Sivakumar, B., Phoon, K.K., Liong, S.Y., Liaw, C.Y., A systematic approach to noise reduction in chaotic hydrological time series. J. Hydrol. 219 (1999), 103–135, 10.1016/s0022-1694(99)00051-7.
45. Stevenson, J.A., Sun, X., Mitchell, N.C., Despeckling SRTM and other topographic data with a denoising algorithm. Geomorphology 114 (2010), 238–252.
46. Su, H.Z., Qin, P., Qin, Z.H., A method for evaluating sea dike safety. Water Resour. Manag. 27:15 (2013), 5157–5170, 10.1007/s11269-013-0459-0.
47. Su, M.R., Yang, Z.F., Chen, B., Ulgiati, S., Urban ecosystem health assessment based on emergy and set pair analysis - a comparative study of typical Chinese cities. Ecol. Model. 220:18 (2009), 2341–2348, 10.1016/j.ecolmodel.2009.06.010.
48. Sun, A.Y., Wang, D., Xu, X., Monthly streamflow forecasting using Gaussian process regression. J. Hydrol. 511 (2014), 72–81.
49. Valipour, M., Banihabib, M.E., Behbahani, S.M.R., Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J. Hydrol. 476 (2013), 433–441.
50. Wang, D., Ding, H., Singh, V.P., Shang, X.S., Liu, D.F., Wang, Y.K., Zeng, X.K., Wu, J.C., Wang, L.C., Zou, X.Q., A hybrid wavelet analysis–cloud model data-extending approach formeteorologic and hydrologic time series. J. Geophys. Res. D (Atmos.) 120 (2015), 4057–4071.
51. Wang, T., Chen, J.S., Wang, T., Wang, S., Entropy weight-set pair analysis based on tracer techniques for dam leakage investigation. Nat. Hazards 76:2 (2015), 747–767, 10.1007/s11069-014-1515-7.
52. Wang, D., Singh, V.P., Shang, X.S., Ding, H., Wu, J.C., Wang, L.C., Zou, X.Q., Chen, Y.F., Chen, X., Wang, S.C., Wang, Z.L., Sample entropy-based adaptive wavelet de-noising approach for meteorologic and hydrologic time series. J. Geophys. Res. D (Atmos) 119 (2014), 8726–8740.
53. Wang, W.S., Li, Y.Q., Hazard degree assessment of landslide using set pair analysis method. Nat. Hazards 60:2 (2012), 367–379, 10.1007/s11069-011-0017-0.
54. Wang, W.S., Jin, J.L., Ding, J., Li, Y.Q., A new approach to water resources system assessment - set pair analysis method. Sci. China Ser. E 52:10 (2009), 3017–3023, 10.1007/s11431-009-0099-z.
55. Wang, W.S., Hu, S.X., Li, Y.Q., Cao, S.Y., How to select a reference basin in the ungauged regions. J. Hydrol. Eng. 18:8 (2013), 941–947, 10.1061/(ASCE)HE.1943-5584.0000680.
56. Wang, Y., Wang, J., Zhang, Y., Annual runoff forecasting model based on weighted rank set pair analysis method. Water Resour. Power 30:3 (2012), 17–19.
57. Whitworth, K.L., Baldwin, D.S., Kerr, J.L., Drought, floods and water quality: drivers of a severe hypoxic blackwater event in a major river system (the southern Murray–Darling Basin, Australia). J. Hydrol. 450 (2012), 190–198.
58. Wu, C.L., Chau, K.W., Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis. J. Hydrol. 399:3 (2011), 394–409.
59. Xu, F., Zhao, Y.W., Yang, Z.F., Zhang, Y., Multi-scale evaluation of river health in Liao River Basin, China. Front. Environ. Sci. Eng. 5:2 (2011), 227–235, 10.1007/s11783-010-0219-9.
60. Yang, F.G., Liang, Y., Singh, V.P., Wang, W.S., Zhou, X.Q., Liu, X.N., Cao, S.Y., Huang, E., Wu, Y.H., Debris flow hazard assessment using set pair analysis models: Take Beichuan county as an example. J. Mt. Sci. 11:4 (2014), 1015–1022, 10.1007/s11629-013-2495-x.
61. Yang, X.H., He, J., Di, C.L., Li, J.Q., Vulnerability of assessing water resources by the improved set pair analysis. Therm. Sci. 18:5 (2014), 1531–1535, 10.2298/TSCI1405531Y.
62. Yang, S.S., Yang, X.H., Jiang, R., Zhang, Y.C., New optimal weight combination model for forecasting precipitation. Math. Probl. Eng., 2012, 376010, 10.1155/2012/376010.
63. Zhang, J.P., Guo, B.T., Ding, Z.H., Research on the drought index of irrigation district with multi-time scales. Environ. Monit. Assess. 185:10 (2013), 8749–8757, 10.1007/s10661-013-3209-0.
64. Zhang, Y., Yang, X.H., Zhang, L., Ma, W.Y., Qiao, L.X., Set pair analysis based on phase space reconstruction model and its application in forecasting extreme temperature. Math. Probl. Eng., 2013, 516150, 10.1155/2013/516150.
65. Zhang, J.Y., Wang, L.C., Assessment of water resource security in Chongqing City of China: what has been done and what remains to be done?. Nat. Hazards 75:3 (2015), 2751–2772, 10.1007/s11069-014-1460-5.
66. Zhang, Q., Wang, B.D., He, B., Peng, Y., Ren, M.L., Singular spectrum analysis and ARIMA hybrid model for annual runoff forecasting. Water Resour. Manag. 25 (2011), 2683–2703.
67. Zhao, J., Huang, Z.P., Jin, J.L., Lu, B.H., Zhang, X.M., Chen, Y.Q., Risk assessment of regional water resources and forewarning model at different time scales. J. Hydrol. Eng. 18:9 (2013), 1114–1121, 10.1061/(ASCE)HE.1943-5584.0000490.
68. Zhao, K.Q., Set Pair Analysis and its Preliminary Applications. 2000, Science and Technology Press of Zhejiang, Hangzhou.
69. Zou, Q., Zhou, J.Z., Zhou, C., Song, L.X., Guo, J., Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP. Stoch. Environ. Res. Risk A 27:2 (2013), 525–546, 10.1007/s00477-012-0598-5.