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Volumn 118, Issue 1-2, 2014, Pages 25-34

Monthly rainfall prediction using wavelet regression and neural network: an analysis of 1901–2002 data, Assam, India

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EID: 84887638501     PISSN: 0177798X     EISSN: 14344483     Source Type: Journal    
DOI: 10.1007/s00704-013-1029-3     Document Type: Article
Times cited : (47)

References (45)
  • 1
    • 84855893978 scopus 로고    scopus 로고
    • Development of stage discharge rating curve using model tree and neural networks: an application to peachtree creek in Atlanta. Expert Systems With Applications
    • Ajmera TK, Goyal MK (2012) Development of stage discharge rating curve using model tree and neural networks: an application to peachtree creek in Atlanta. Expert Systems With Applications, Elsevier Ltd. 39(5):5702–5710
    • (2012) Elsevier Ltd , vol.39 , Issue.5 , pp. 5702-5710
    • Ajmera, T.K.1    Goyal, M.K.2
  • 2
    • 85016677124 scopus 로고    scopus 로고
    • Antonios A, Constantine EV (2003) Wavelet exploratory analysis of the FTSE ALL SHARE index. Non-linear systems and Chaos, Athens
    • Antonios A, Constantine EV (2003) Wavelet exploratory analysis of the FTSE ALL SHARE index. In proceedings of the 2nd WSEAS international conference on non-linear analysis. Non-linear systems and Chaos, Athens.
    • In proceedings of the 2nd WSEAS international conference on non-linear analysis
  • 5
    • 0242585368 scopus 로고    scopus 로고
    • Application of an ensemble technique based on singular spectrum analysis to daily rainfall forecasting
    • Baratta D, Cicioni G, Masulli F, Studer L (2003) Application of an ensemble technique based on singular spectrum analysis to daily rainfall forecasting. Neural Networks 16:375–387
    • (2003) Neural Networks , vol.16 , pp. 375-387
    • Baratta, D.1    Cicioni, G.2    Masulli, F.3    Studer, L.4
  • 6
    • 49449098130 scopus 로고    scopus 로고
    • Levenberg-Marquardt Algorithm for Karachi Stock Exchange Share Rates Forecasting
    • Burney SMA, Jilani TA, Ardil C (2005) Levenberg-Marquardt Algorithm for Karachi Stock Exchange Share Rates Forecasting. World Acad Sci Eng Technol 3:171–176
    • (2005) World Acad Sci Eng Technol , vol.3 , pp. 171-176
    • Burney, S.M.A.1    Jilani, T.A.2    Ardil, C.3
  • 7
    • 84864809578 scopus 로고    scopus 로고
    • Wavelet-based multi-scale entropy analysis of complex rainfall time series
    • Chien-Ming C (2011) Wavelet-based multi-scale entropy analysis of complex rainfall time series. Entropy 13:241–253. doi:10.3390/e13010241
    • (2011) Entropy , vol.13 , pp. 241-253
    • Chien-Ming, C.1
  • 8
    • 84876398010 scopus 로고    scopus 로고
    • Enhanced accuracy of rainfall–runoff modeling with wavelet transform
    • Chien-ming C (2013) Enhanced accuracy of rainfall–runoff modeling with wavelet transform. J Hydroinf 15(2):392–404
    • (2013) J Hydroinf , vol.15 , Issue.2 , pp. 392-404
    • Chien-ming, C.1
  • 9
    • 18244416162 scopus 로고    scopus 로고
    • On-line estimation of unit hydrographs using the wavelet-based LMS algorithm
    • Chou CM, Wang RY (2002) On-line estimation of unit hydrographs using the wavelet-based LMS algorithm. Sci J Hydrol 47(5):721–738
    • (2002) Sci J Hydrol , vol.47 , Issue.5 , pp. 721-738
    • Chou, C.M.1    Wang, R.Y.2
  • 10
    • 2042453329 scopus 로고    scopus 로고
    • Wavelet analysis of variability in annual Canadian streamflows
    • Coulibaly P, Burn HD (2004) Wavelet analysis of variability in annual Canadian streamflows. Water Resour Res 40, W03105
    • (2004) Water Resour Res , vol.40 , pp. W03105
    • Coulibaly, P.1    Burn, H.D.2
  • 11
    • 60649110244 scopus 로고    scopus 로고
    • Fernando DAK, Shamseldin AY (2009) Investigation of the internal functioning of the radial basis function neural network river flow forecasting models. 14(3):286–292
    • Fernando DAK, Shamseldin AY (2009) Investigation of the internal functioning of the radial basis function neural network river flow forecasting models. J Hydrol Eng 14(3):286–292. doi:10.1061/(ASCE)1084-0699(2009)14:3(286)
    • J Hydrol Eng
  • 12
    • 78751579126 scopus 로고    scopus 로고
    • Ministry of Environment and Forests, Government of India:
    • FSI, 2009. State of Forest Report, 2009. Forest Survey of India (FSI), Ministry of Environment and Forests, Government of India
    • (2009) Forest Survey of India (FSI)
  • 13
    • 0000562670 scopus 로고
    • Decomposition of hardy functions into square integrable wavelets of constant shape
    • Grossman A, Morlet J (1984) Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 15:723–736
    • (1984) SIAM J Math Anal , vol.15 , pp. 723-736
    • Grossman, A.1    Morlet, J.2
  • 14
    • 0028543366 scopus 로고
    • Training feed forward networks with the Marquardt algorithm
    • Hagan MT, Menhaj MB (1994) Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 6:861–867
    • (1994) IEEE Trans Neural Netw , vol.6 , pp. 861-867
    • Hagan, M.T.1    Menhaj, M.B.2
  • 15
    • 0029413797 scopus 로고
    • Artificial neural network modeling of the rainfall-runoff process
    • Hsu K-L, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517–2530
    • (1995) Water Resour Res , vol.31 , Issue.10 , pp. 2517-2530
    • Hsu, K.-L.1    Gupta, H.V.2    Sorooshian, S.3
  • 16
    • 2442639370 scopus 로고    scopus 로고
    • Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms, and artificial neural network techniques
    • Jain A, Srinivasulu S (2004) Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms, and artificial neural network techniques. Water Resour Res 40(4):W04302 doi:10.1029/2003WR002355
    • (2004) Water Resour Res , vol.40 , Issue.4 , pp. W04302
    • Jain, A.1    Srinivasulu, S.2
  • 17
    • 84860875534 scopus 로고    scopus 로고
    • Trend analysis of rainfall and temperature data for India
    • Jain SK, Kumar V (2012) Trend analysis of rainfall and temperature data for India. Curr Sci 102(1):37–49
    • (2012) Curr Sci , vol.102 , Issue.1 , pp. 37-49
    • Jain, S.K.1    Kumar, V.2
  • 18
    • 0033197895 scopus 로고    scopus 로고
    • Application of ANN for reservoir inflow prediction and operation
    • Jain SK, Das A, Srivastava DK (1999) Application of ANN for reservoir inflow prediction and operation. J Water Resour Plan Manag ASCE 125(5):263–271
    • (1999) J Water Resour Plan Manag ASCE , vol.125 , Issue.5 , pp. 263-271
    • Jain, S.K.1    Das, A.2    Srivastava, D.K.3
  • 19
    • 71649094330 scopus 로고    scopus 로고
    • Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting
    • Kisi O (2009) Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting. Hydrol Process 23:3583–3597
    • (2009) Hydrol Process , vol.23 , pp. 3583-3597
    • Kisi, O.1
  • 20
    • 78751591071 scopus 로고    scopus 로고
    • Wavelet regression model as an alternative to neural networks for river stage forecasting
    • Kisi O (2011) Wavelet regression model as an alternative to neural networks for river stage forecasting. Water Resour Manag 25(2):579–600
    • (2011) Water Resour Manag , vol.25 , Issue.2 , pp. 579-600
    • Kisi, O.1
  • 21
    • 0034610444 scopus 로고    scopus 로고
    • Rainfall–runoff relations for karstic springs. Part II. Continuous wavelet and discrete orthogonal multiresolution analyses
    • Labat D, Ababou R, Mangin A (2000) Rainfall–runoff relations for karstic springs. Part II. Continuous wavelet and discrete orthogonal multiresolution analyses. J Hydrol 238(3–4):149–178
    • (2000) J Hydrol , vol.238 , Issue.3-4 , pp. 149-178
    • Labat, D.1    Ababou, R.2    Mangin, A.3
  • 22
    • 28444486059 scopus 로고    scopus 로고
    • Recent advances in wavelet analyses. Part 2—Amazon, Parana, Orinoco and Congo discharges time scale variability
    • Labat D, Ronchail J, Guyot JL (2005) Recent advances in wavelet analyses. Part 2—Amazon, Parana, Orinoco and Congo discharges time scale variability. J Hydrol 314(1–4):289–311
    • (2005) J Hydrol , vol.314 , Issue.1-4 , pp. 289-311
    • Labat, D.1    Ronchail, J.2    Guyot, J.L.3
  • 23
    • 0030148352 scopus 로고    scopus 로고
    • The discrete wavelet transform and the scale analysis of the surface properties of sea ice
    • Lindsay RW, Percival DB, Rothrock DA (1996) The discrete wavelet transform and the scale analysis of the surface properties of sea ice. IEEE Trans Geosci Remote Sens 34:771–787
    • (1996) IEEE Trans Geosci Remote Sens , vol.34 , pp. 771-787
    • Lindsay, R.W.1    Percival, D.B.2    Rothrock, D.A.3
  • 24
    • 0347825808 scopus 로고    scopus 로고
    • Mahanta et al. (2003). Accessed 20 July 2013
    • Mahanta et al. (2003) Assam Human Development Report 2003, available at http://hdr.undp.org/en/reports/nationalreports/asiathepacific/india/name,3268,en.html. Accessed 20 July 2013
    • Assam Human Development Report 2003
  • 25
    • 0033957764 scopus 로고    scopus 로고
    • Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
    • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124
    • (2000) Environ Model Softw , vol.15 , pp. 101-124
    • Maier, H.R.1    Dandy, G.C.2
  • 26
    • 0024700097 scopus 로고
    • A theory for multi resolution signal decomposition: the wavelet representation
    • Mallat SG (1989) A theory for multi resolution signal decomposition: the wavelet representation. IEEE T Pattern Anal Mach Intell 11(7):674–693
    • (1989) IEEE T Pattern Anal Mach Intell , vol.11 , Issue.7 , pp. 674-693
    • Mallat, S.G.1
  • 27
    • 0030159380 scopus 로고    scopus 로고
    • Artificial neural networks as rainfall runoff models
    • Minns AW, Hall MJ (1996) Artificial neural networks as rainfall runoff models. Hydrol Sci J 41(3):399–418
    • (1996) Hydrol Sci J , vol.41 , Issue.3 , pp. 399-418
    • Minns, A.W.1    Hall, M.J.2
  • 28
    • 33751399443 scopus 로고    scopus 로고
    • Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region
    • Turkey
    • Partal T, Kucuk M (2006) Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region. Phys Chem Earth 31:1189–1200, Turkey
    • (2006) Phys Chem Earth , vol.31 , pp. 1189-1200
    • Partal, T.1    Kucuk, M.2
  • 30
    • 0030224015 scopus 로고    scopus 로고
    • Deriving a general operating policy for reservoirs using neural network
    • Raman H, Chandramauli V (1996) Deriving a general operating policy for reservoirs using neural network. J Water Resour Plan Manag ASCE 122(5):342–347
    • (1996) J Water Resour Plan Manag ASCE , vol.122 , Issue.5 , pp. 342-347
    • Raman, H.1    Chandramauli, V.2
  • 31
    • 2442443238 scopus 로고    scopus 로고
    • Signal separation with almost periodic components: a wavelets based method
    • Rosso OA, Figliola A, Blanco S, Jacovkis PM (2004) Signal separation with almost periodic components: a wavelets based method. Rev Mex Fis 50(1):179–186
    • (2004) Rev Mex Fis , vol.50 , Issue.1 , pp. 179-186
    • Rosso, O.A.1    Figliola, A.2    Blanco, S.3    Jacovkis, P.M.4
  • 33
    • 84876984605 scopus 로고    scopus 로고
    • Improved wavelet modeling framework for hydrologic time series forecasting
    • Sang Y (2013) Improved wavelet modeling framework for hydrologic time series forecasting. Water Resour Manag 27(8):2807–2821
    • (2013) Water Resour Manag , vol.27 , Issue.8 , pp. 2807-2821
    • Sang, Y.1
  • 34
    • 84899923662 scopus 로고    scopus 로고
    • Analysis of Precipitation Time Series of Urban Centers of Northeastern Brazil using Wavelet Transform
    • Santos Celso AG, Freire Paula KMM (2012) Analysis of Precipitation Time Series of Urban Centers of Northeastern Brazil using Wavelet Transform. World Acad Sci Eng Technol 67:845–850
    • (2012) World Acad Sci Eng Technol , vol.67 , pp. 845-850
    • Santos Celso, A.G.1    Freire Paula, K.M.M.2
  • 35
    • 84859363621 scopus 로고    scopus 로고
    • Modelling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic and decision tree algorithms
    • Senthil Kumar AR, Ojha CSP, Goyal MK, Singh RD, Swamee PK, (2012) Modelling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic and decision tree algorithms. ASCE's J Hydrol Eng 17(3):394–404
    • (2012) ASCE's J Hydrol Eng , vol.17 , Issue.3 , pp. 394-404
    • Senthil Kumar, A.R.1    Ojha, C.S.P.2    Goyal, M.K.3    Singh, R.D.4    Swamee, P.K.5
  • 36
    • 16444365723 scopus 로고    scopus 로고
    • Rainfall-runoff modelling using artificial neural networks: comparison of network types
    • Senthil Kumar AR, Sudheer KP, Jain SK, Agarwal PK (2005) Rainfall-runoff modelling using artificial neural networks: comparison of network types. Hydrol Process 19:1277–1291
    • (2005) Hydrol Process , vol.19 , pp. 1277-1291
    • Senthil Kumar, A.R.1    Sudheer, K.P.2    Jain, S.K.3    Agarwal, P.K.4
  • 37
    • 57949116748 scopus 로고    scopus 로고
    • River flow prediction using an integrated approach
    • Srinivasulu S, Jain A (2009) River flow prediction using an integrated approach. J Hydrol Engrg, ASCE 14(1):75–83
    • (2009) J. Hydrol. Engrg., ASCE , vol.14 , Issue.1 , pp. 75-83
    • Srinivasulu, S.1    Jain, A.2
  • 38
    • 0037197571 scopus 로고    scopus 로고
    • A data-driven algorithm for constructing artificial neural network rainfall-runoff models
    • Sudheer KP, Gosain AK, Ramasastri KS (2002) A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16:1325–1330
    • (2002) Hydrol Process , vol.16 , pp. 1325-1330
    • Sudheer, K.P.1    Gosain, A.K.2    Ramasastri, K.S.3
  • 40
    • 0031898654 scopus 로고    scopus 로고
    • River stage forecasting using artificial neural networks
    • Thirumalaiah K, Deo MC (1998) River stage forecasting using artificial neural networks. J Hydrol Engrg 3(1):26–32
    • (1998) J Hydrol Engrg , vol.3 , Issue.1 , pp. 26-32
    • Thirumalaiah, K.1    Deo, M.C.2
  • 41
    • 1542680533 scopus 로고    scopus 로고
    • A practical guide to wavelet analysis
    • Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteorol Soc 79(1):61–78. doi:10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
    • (1998) Bull Am Meteorol Soc , vol.79 , Issue.1 , pp. 61-78
    • Torrence, C.1    Compo, G.P.2
  • 42
    • 53849113979 scopus 로고    scopus 로고
    • Multiobjective training of artificial neural networks for rainfall-runoff modelling
    • Vos NJ, Rientjes THM (2008) Multiobjective training of artificial neural networks for rainfall-runoff modelling. Water Resour Res 44(W08434):1–15
    • (2008) Water Resour Res , vol.44 , Issue.W08434 , pp. 1-15
    • Vos, N.J.1    Rientjes, T.H.M.2
  • 43
    • 33845543385 scopus 로고    scopus 로고
    • Wavelet network model and its application to the prediction of the hydrology
    • Wang W, Ding J (2003) Wavelet network model and its application to the prediction of the hydrology. Nature and Science 1(1):67–71
    • (2003) Nature and Science , vol.1 , Issue.1 , pp. 67-71
    • Wang, W.1    Ding, J.2
  • 44
    • 19544366488 scopus 로고    scopus 로고
    • Multiscale characteristics of the rainy season rainfall and interdecadal decaying of summer monsoon in North China
    • Xingang D, Ping W, Jifan C (2003) Multiscale characteristics of the rainy season rainfall and interdecadal decaying of summer monsoon in North China. Chin Sci Bull 48:2730–2734
    • (2003) Chin Sci Bull , vol.48 , pp. 2730-2734
    • Xingang, D.1    Ping, W.2    Jifan, C.3
  • 45
    • 38149027113 scopus 로고    scopus 로고
    • The research of monthly discharge predictor–corrector model based on wavelet decomposition
    • Zhou HC, Peng Y, Liang G-H (2008) The research of monthly discharge predictor–corrector model based on wavelet decomposition. Water Resour Manag 22(2):217–227
    • (2008) Water Resour Manag , vol.22 , Issue.2 , pp. 217-227
    • Zhou, H.C.1    Peng, Y.2    Liang, G.-H.3


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