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Articles

Application of neural network to model rainfall pattern of Ethiopia

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Pages 69-84 | Received 16 Feb 2022, Accepted 02 Oct 2022, Published online: 31 Oct 2022

References

  • Abbot, J., & Marohasy, J. (2018). Forecasting of medium-term rainfall using Artificial Neural Networks: Case studies from Eastern Australia. In T. V. Hromadka & P. Rao (Eds.), Engineering and Mathematical Topics in Rainfall (pp. 33–56). Books on Demand. https://doi.org/10.5772/intechopen.72619.
  • Berhanu, B., Seleshi, Y., & Melesse, A. M. (2014). Surface water and groundwater resources of ethiopia: Potentials and challenges of water resources development. In A. Melesse, W. Abtew, & S. Setegn (Eds.), Nile River Basin (pp. 97–117). Springer. https://doi.org/10.1007/978-3-319-02720-3_6
  • Box, G. E. P., & Jenkins, G. M (1976). Time series analysis. Forecasting and control. In G. C. Reinsel (Ed.), Holden-day series in time series analysis (Revised ed.) Holden-Day.
  • Brownlee, J. (2016). Time series prediction with lstm recurrent neural networks in Python with Keras. Available at: machinelearningmastery.com.
  • Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1), 1–32. https://doi.org/10.18637/jss.v076.i01
  • Chen, C., & Liu, L.-M. (1993). Joint estimation of model parameters and outlier effects in time series. Journal of the American Statistical Association, 88(421), 284–297. https://doi.org/10.2307/2290724
  • Dabakoglu, C. (Jun 2019). Time series forecasting—Arima, LSTM, Prophet with python. Online. Retrieved June 30, 2019.
  • Golden, R. M. (1996). Mathematical methods for neural network analysis and design. MIT Press.
  • Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2017). Lstm: A search space Odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924
  • Haining, R. P., & Haining, R. (2003). Spatial data analysis: Theory and practice. Cambridge University Press.
  • Hijmans, R. J. (2017). Geosphere: Spherical trigonometry. R package version 1.5-7.
  • Hochreiter, S. (1998). Recurrent neural net learning and vanishing gradient. International Journal of Uncertainity, Fuzziness and Knowledge-Based Systems, 6(2), 107–116. https://doi.org/10.1142/S0218488598000094
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Hyndman, R. J. (2013). FPP data for “forecasting: Principles and practice”. R package version 0.5.
  • Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O'Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., & Yasmeen, F. (2018). forecast: Forecasting functions for time series and linear models. R package version 8.4.
  • Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3), 1–22. https://doi.org/10.18637/jss.v027.i03
  • Jiang, Y., & Zhang, Y. (2018). Exploration of predicting power of Arima, Facebook Prophet and lstm on time series. Stanford University. Online. Retrieved June 30, 2019.
  • Lin, J., Keogh, E., Lonardi, S., & Patel, P. (2002). Finding motifs in time series. In Proc. of the 2nd workshop on temporal data mining (pp. 53–68).
  • Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing sax: A novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107–144. https://doi.org/10.1007/s10618-007-0064-z
  • Lin, J., & Li, Y. (2009). Finding structural similarity in time series data using bag-of-patterns representation. In M. Winslett (Ed.), International conference on scientific and statistical database management (pp. 461–477). Springer. https://doi.org/10.1007/978-3-642-02279-1_33
  • Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2005). Geographic information systems and science (2nd ed.). John Wiley & Sons.
  • Shen, S. S. P. (2017). R programming for climate data analysis and visualization: Computing and plotting for NOAA data applications. John Wiley & Sons.
  • Strauß, M. (2018). Time series forecasting with LSTMS and Prophet, nov. Online. Retrieved June 30, 2019.
  • Taylor, S. J., & Letham, B. (2018a). Forecasting at scale. The American Statistician, 72(1), 37–45. https://doi.org/10.1080/00031305.2017.1380080
  • Taylor, S. J., & Letham, B. (2018b). Prophet: Automatic forecasting procedure. R package version 0.4.
  • Tsidu, G. M. (2012). High-resolution monthly rainfall database for ethiopia: Homogenization, reconstruction, and gridding. Journal of Climate, 25(24), 8422–8443. https://doi.org/10.1175/JCLI-D-12-00027.1
  • Veenstra, J. Q. (2012). Persistence and anti-persistence: Theory and software [PhD thesis]. Western University.