Application of neural network to model rainfall pattern of Ethiopia

Abstract

In this paper, we have constructed Artificial Neural Network models which could capture rainfall pattern of Ethiopia. The data was collected from 147 stations across Ethiopia. Seven homogenized rainfall stations have been created based on both local and global patterns of datasets. Back-of-Word algorithm was used for extracting patterns of the datasets. K-means algorithm was used for clustering purpose. Each of the data of homogenized regions was interpolated using a spatial average. Two time series models, ARMA and Facebook's Prophet, have been fitted for each of spatial averages as baseline models. Both have been shown to perform weak for generalization purpose as spatially averaged datasets lose their strong seasonal pattern. On the other hand, the proposed Long Short Term Memory (LSTM) was found to be the best fitted model in comparison to the baseline models. The hyperparameters of the LSTM have been tuned to get optimal parameters. Besides, the RMSE of the baseline model was used as a benchmark for tuning the LSTM used.

Keywords:

• ARIMA
• Back-of-Word algorithm
• clustering
• neural network
• Prophet

Acknowledgements

We thank Dr Yaping Wang for his helpful comments on an earlier version of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by National Natural Science Foundation of China (Grant numbers: 11771146, 11831008, 81530086).