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Time-Series Analysis

Forecasting drought using neural network approaches with transformed time series data

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Pages 2591-2606 | Received 16 Apr 2020, Accepted 18 Dec 2020, Published online: 31 Dec 2020

References

  • J. Adamowski, River flow forecasting using wavelet and cross-wavelet transform models, Hydrol. Process. 22 (2008), pp. 4877–4891.
  • Y.A. Al-Sbou and K.M. Alawasa, Nonlinear autoregressive recurrent neural network model for solar radiation prediction, Int. J. Appl. Eng. Res. 12 (2017), pp. 4518–4527.
  • A.A. Alsumaiei and M.S. Alrashidi, Hydrometeorological drought forecasting in hyper-arid climates using nonlinear autoregressive neural networks, Water 12 (2020), pp. 2611.
  • P. Anjoy and R.K. Paul, Comparative performance of wavelet-based neural network approaches, Neural. Comput. Appl. 31 (2019), pp. 3443–3453.
  • A.M. Belayneh and J. Adamowski, Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression, Appl. Comput. Intell. Soft. Comput. 6 (2012), pp. 1–13.
  • A.M. Belayneh, J. Adamowski, and B. Khalil, Short-term SPI drought forecasting in the awash river basin in Ethiopia using wavelet transforms and machine learning methods, Sustain. Water. Resour. Manag. 2 (2016), pp. 87–101.
  • C. Cacciamani, A. Morgillo, S. Marchesi, and V. Pavan, Monitoring and forecasting drought on a regional scale: Emilia-Romagna region, Water Sci. Technol. Libr. 62 (2007), pp. 29–48.
  • A. Cancelliere, G. di Mauro, B. Bonaccorso, and G. Rossi, Stochastic forecasting of drought indices, in Methods and Tools For Drought Analysis and Management, G. Rossi, T. Vega, and B. Bonaccorso, eds., Springer, Netherlands, Dordrecht, 2007, pp. 83–100.
  • R: A language and environment for statistical computing. R Core Team, R Foundation for Statistical Computing, Vienna, Austria, 2017; software available at http://www.R-project.org/.
  • E. Diaconescu, The use of NARX neural networks to predict chaotic time series, WSEAS Trans. Comput. Res. 3 (2008), pp. 182–191.
  • G.J. Husak, C.C. Funk, J. Michaelsen, T. Magadzire, and K.P. Goldsberry, Developing seasonal rainfall scenarios for food security early warning, Theor. Appl. Climatol. 114 (2013), pp. 291–302.
  • A. Katip, Meteorological drought analysis using artificial neural networks for Bursa city, Turkey, Appl. Ecol. Env. Res. 16 (2018), pp. 3315–3332.
  • M.M.H. Khan, N.S. Muhammad, and A. el-Shafie, Capability of meteorological drought indices for detecting soil moisture droughts, J. Hydrol. 590 (2020), pp. 125380.
  • T.W. Kim and J.B. Valdes, Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks, J. Hydrol. Eng. 6 (2003), pp. 319–328.
  • B. Lloyd-Hughes, The impracticality of a universal drought definition, Theor. Appl. Climatol. 117 (2014), pp. 607–611.
  • MATLAB Release 2019b. The MathWorks, Inc., Natick, Massachusetts, United States., 2019.
  • T. McKee, N. Doesken, and J. Kleist, The relationship of drought frequency and duration to time scales, 8th Conference on Applied Climatology, January 12–17, 1993, pp. 179–184
  • A.D. Mehr, E. Kahya, and M. Özger, A gene-wavelet model for long lead time drought forecasting, J. Hydrol. 517 (2014), pp. 691–699.
  • A.K. Mishra and V.R. Desai, Drought forecasting using feedforward recursive neural network, Ecol. Modell. 198 (2006), pp. 127–138.
  • N. Mishra, H.K. Soni, S. Sharma, and A.K. Upadhyay, Development and analysis of artificial neural network models for rainfall prediction by using time-series data, Int. J. Intell. Syst. Appl. 1 (2018), pp. 16–23.
  • L.B. Mohammed, M.A. Hamdan, E.A. Abdelhafez, and W. Shaheen, Hourly solar radiation prediction based on nonlinear autoregressive exogenous (NARX) neural network, Jordan J. Mech. Ind. Eng. 7 (2013), pp. 11–18.
  • G.P. Nason and R. von Sachs, Wavelets in time-series analysis, Philos. Trans. R. Soc. B Biol. Sci. 357 (1999). Available at https://researchgate.net/publication/5068418_Wavelets_in_Time_Series_Analysis.
  • V. Nourani, A. Tahershamsi, J. Shahrabi, and E. Hadavandi, A new hybrid algorithm for rainfall-runoff process modeling based on the wavelet transform and genetic fuzzy system, J. Hydroinform. 16 (2014), pp. 1004–1024.
  • P. Pamukcu, Y. Serengil, and I. Yurtseven, Role of forest cover, land use change and climate change on water resources in Marmara basin of Turkey, Forest 8 (2014), pp. 480–486.
  • D.B. Percival and A.T. Walden, Wavelet Methods for Time Series Analysis, London, Cambridge University Press, 2000.
  • O. Renaud, J.L. Starck, and F. Murtagh, Prediction based on a multiscale decomposition, Int. J. Wavelets. Multiresolut. Inf. Process. 1 (2003), pp. 217–232.
  • A. Stepchenko and J. Chizhov, NDVI short-term forecasting using recurrent neural networks, Proceedings of the 10th International Scientific and Practical Conference, Vol. 3, 2015, pp. 180–185.
  • H.C.S. Thom, A note on gamma distribution, Mon. Wea. Rev. 86 (1958), pp. 117–122.
  • D.S. Wilks, Statistical Methods in the Atmospheric Sciences An Introduction, San Diego, CA, USA, Academic Press, 1995.

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