182
Views
12
CrossRef citations to date
0
Altmetric
Articles

Generalized wavelet neural networks for evapotranspiration modeling in India

, , &
Pages 119-131 | Received 30 Jan 2017, Accepted 01 May 2017, Published online: 20 May 2017

References

  • Adamala, S., Raghuwanshi, N.S., and Mishra, A. (2015a). “Generalized quadratic synaptic neural networks for ETo modeling.” Environ. Processes, 2(2), 309–329.10.1007/s40710-015-0066-6
  • Adamala, S., Raghuwanshi, N.S., Mishra, A., and Tiwari, M.K. (2014a). “Evapotranspiration modeling using second-order neural networks.” J. Hydrol. Eng., 19(6), 1131–1140.10.1061/(ASCE)HE.1943-5584.0000887
  • Adamala, S., Raghuwanshi, N.S., Mishra, A., and Tiwari, M.K. (2014b). “Development of generalized higher-order synaptic neural based ETo models for different agro-ecological regions in India.” J. Irrig. Drain. Eng., 140, 04014038. doi:10.1061/IR.1943-4774.0000784.
  • Adamala, S., Raghuwanshi, N.S., Mishra, A., and Tiwari, M.K. (2015b). “Closure to ‘evapotranspiration modeling using second-order neural networks’ by Sirisha Adamala, N. S. Raghuwanshi, Ashok Mishra, and Mukesh K. Tiwari.” J. Hydrol. Eng., 20(9), 07015015.10.1061/(ASCE)HE.1943-5584.0001207
  • Allen, R.G., Pereira, L.S., Raes, D., Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements. Irrigation and drainage paper no. 56, FAO, Rome.
  • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). “Artificial neural networks in hydrology-I: Preliminary concepts.” J. Hydrol. Eng., 5(2), 115–123.
  • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b). “Artificial neural networks in hydrology-II: Hydrologic applications.” J. Hydrol. Eng., 5(2), 124–137.
  • Cannas, B., Fanni, A., See, L., and Sias, G. (2006). “Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning.” Phys. Chem. Earth, Parts A/B/C, 31, 1164–1171.10.1016/j.pce.2006.03.020
  • Cobaner, M. (2013). “Reference evapotranspiration based on class A pan evaporation via wavelet regression technique.” Irrig. Sci., 31, 119–134.10.1007/s00271-011-0297-x
  • Coulibaly, P., and Burn, D.H. (2004). “Wavelet analysis of variability in annual Canadian streamflows.” Water Resour. Res., 40(3), 1–14.
  • Dai, X., Shi, H., Li, Y., Ouyang, Z., and Huo, Z. (2009). “Artificial neural network models for estimating regional reference evapotranspiration based on climate factors.” Hydrol. Process., 23, 442–450.10.1002/hyp.v23:3
  • Dixit, P., Londhe, S., and Deo, M.C. (2016). “Review of applications of neuro wavelet techniques in water flows.” INAE Lett., 1, 99–104. doi:10.1007/s41403-016-0015-3.
  • Drago, A.F., and Boxall, S.R. (2002). “Use of the wavelet transform on hydro-meteorological data.” Phys. Chem. Earth, Parts A/B/C, 27, 1387–1399.10.1016/S1474-7065(02)00076-1
  • Droogers, P., and Allen, R.G. (2002). “Estimating reference evapotranspiration under inaccurate data conditions.” Irrig. Drain. Syst., 16, 33–45.10.1023/A:1015508322413
  • Ebadi, L., and Shafri, H.Z.M. (2014). “Compression of remote sensing data using second-generation wavelets: A review.” Environ. Earth Sci., 71, 1379–1387.10.1007/s12665-013-2544-3
  • Evrendilek, F. (2014). “Assessing neural networks with wavelet denoising and regression models in predicting diel dynamics of eddy covariance-measured latent and sensible heat fluxes, and evapotranspiration.” Neural Comput. Appl., 24, 327–337.10.1007/s00521-012-1240-7
  • Falamarzi, Y., Palizdan, N., Huang, Y.F., and Lee, T.S. (2014). “Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs).” Agric. Water Manage., 140, 26–36.10.1016/j.agwat.2014.03.014
  • Haykin, S. (1994). Neural networks: A comprehensive foundation, Macmillan, New York, NY.
  • Irmak, S., Allen, R.G., and Whitty, E.B. (2003). “Daily grass and alfalfa-reference evapotranspiration estimates and alfalfa-to-grass evapotranspiration ratios in Florida.” J. Irrig. Drain. Eng., 129(5), 360–370.10.1061/(ASCE)0733-9437(2003)129:5(360)
  • Izadifar, Z. (2010). “Modeling and analysis of actual evapotranspiration using data driven and wavelet techniques.” PhD thesis, University of Saskatchewan, Saskatoon.
  • Jahanbani, H., and El-Shafie, A.H. (2011). “Application of artificial neural network in estimating monthly time series reference evapotranspiration with minimum and maximum temperatures.” Paddy Water Environ., 9(2), 207–220.10.1007/s10333-010-0219-1
  • Jain, S.K., Nayak, P.C., and Sudheer, K.P. (2008). “Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation.” Hydrol. Process., 22, 2225–2234.10.1002/(ISSN)1099-1085
  • Kisi, O. (2008). “The potential of different ANN techniques in evapotranspiration modelling.” Hydrol. Process., 22, 2449–2460.10.1002/hyp.v22:14
  • Kisi, O. (2011a). “Modeling reference evapotranspiration using evolutionary neural networks.” J. Irrig. Drain. Eng., 137, 636–643.10.1061/(ASCE)IR.1943-4774.0000333
  • Kisi, O. (2011b). “Evapotranspiration modeling using a wavelet regression model.” Irrig. Sci., 29, 241–252.
  • Kumar, M., Bandyopadhyay, A., Raghuwanshi, N.S., and Singh, R. (2008). “Comparative study of conventional and artificial neural network-based ETo estimation models.” Irrig. Sci., 26, 531–545.10.1007/s00271-008-0114-3
  • Kumar, M., Raghuwanshi, N.S., and Singh, R. (2009). “Development and validation of GANN model for evapotranspiration estimation.” J. Hydrol. Eng., 14, 131–140.10.1061/(ASCE)1084-0699(2009)14:2(131)
  • Kumar, M., Raghuwanshi, N.S., and Singh, R. (2011). “Artificial neural networks approach in evapotranspiration modeling: A review.” Irrig. Sci., 29(1), 11–25.10.1007/s00271-010-0230-8
  • Kumar, M., Raghuwanshi, N.S., Singh, R., Wallender, W.W., and Pruitt, W.O. (2002). “Estimating evapotranspiration using artificial neural network.” J. Irrig. Drain. Eng, 128(4), 224–233.10.1061/(ASCE)0733-9437(2002)128:4(224)
  • Labat, D. (2005). “Recent advances in wavelet analyses: Part 1. A review of concepts.” J. Hydrol., 314(1–4), 275–288.
  • Labat, D., Ronchail, J., and Guyot, J.L. (2005). “Recent advances in wavelet analyses: Part 2-Amazon, Parana, Orinoco and Congo discharges time scale variability.” J. Hydrol., 314(1–4), 289–311.10.1016/j.jhydrol.2005.04.004
  • Liu, Y., Yu, M., Ma, X., Xing, X. (2016). “Estimating models for reference evapotranspiration with core meteorological parameters via path analysis.” Hydrol. Res., nh2016240.
  • Marti, P., Royuela, A., Manzano, J., and Palau-Salvador, G. (2010). “Generalization of ETo ANN models through data supplanting.” J. Irrig. Drain. Eng., 136(3), 161–174.10.1061/(ASCE)IR.1943-4774.0000152
  • Partal, T. (2009). “Modelling evapotranspiration using discrete wavelet transform and neural networks.” Hydrol. Process., 23, 3545–3555.10.1002/hyp.v23:25
  • Partal, T. (2016). “Comparison of wavelet based hybrid models for daily evapotranspiration estimation using meteorological data.” KSCE J. Civ. Eng., 20, 2050. doi:10.1007/s12205-015-0556-0.
  • Rahimikhoob, A. (2010). “Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran.” Theor. Appl. Climatol., 101, 83–91.
  • Sahgal, J.L., Mandal, D.K., Mandal, C., and Vadivelu, S. (1990). Agro-ecological regions of India map, National Bureau of Soil Survey and Land Use Planning, Indian Council of Agricultural Research, Nagpur.
  • Sang, Y.-F. (2013). “A review on the applications of wavelet transform in hydrology time series analysis.” Atmos. Res., 122, 8–15.10.1016/j.atmosres.2012.11.003
  • Shrivastava, R.K., and Chauhan, S. (2009). “Performance evaluation of reference evapotranspiration estimation using climate based methods and artificial neural networks.” Water Resour. Res., 23, 825–837.
  • Sudheer, K.P., Gosain, A.K., and Ramasastri, K.S. (2003). “Estimating actual evapotranspiration from limited climatic data using neural computing technique.” J. Irrig. Drain. Eng., 129(3), 214–218.10.1061/(ASCE)0733-9437(2003)129:3(214)
  • Thornthwaite, C.W., and Mather, J.R. (1955). “The water balance.” Publication in climatology, 8(1), Laboratory of Climatology, Centerton, NJ, 104 pp.
  • Wang, W., and Ding, J. (2003). “Wavelet network model and its application to the prediction of hydrology.” Nat. Sci., 1, 67–71.
  • Zanetti, S.S., Sousa, E.F., Oliveira, V.P.S., Almeida, F.T., and Bernardo, S. (2007). “Estimating evapotranspiration using artificial neural network and minimum climatological data.” J. Irrig. Drain. Eng., 133, 83–89.10.1061/(ASCE)0733-9437(2007)133:2(83)

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.