Open Access
E3S Web Conf.
Volume 200, 2020
The 1st Geosciences and Environmental Sciences Symposium (ICST 2020)
Article Number 02016
Number of page(s) 5
Section Environmental Management
Published online 23 October 2020
  1. M. A. Ahmadi, M. Ebadi, A. Shokrollahi, and S. M. Javad Majidi, “Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir,” Appl. Soft Comput. J., vol. 13, no. 2, pp. 1085–1098, 2013, doi: 10.1016/j.asoc.2012.10.009. [CrossRef] [Google Scholar]
  2. L. Sheremetov, A. Cosultchi, J. Martínez-Muñoz, A. Gonzalez-Sánchez, and M. A. Jiménez-Aquino, “Data-driven forecasting of naturally fractured reservoirs based on nonlinear autoregressive neural networks with exogenous input,” J. Pet. Sci. Eng., vol. 123, pp. 106–119, 2014, doi: 10.1016/j.petrol.2014.07.013. [CrossRef] [Google Scholar]
  3. N. Chithra Chakra, K. Y. Song, M. M. Gupta, and D. N. Saraf, “An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs),” J. Pet. Sci. Eng., vol. 106, pp. 18–33, 2013, doi: 10.1016/j.petrol.2013.03.004. [CrossRef] [Google Scholar]
  4. J. Wang and J. Wang, “Forecasting stochastic neural network based on financial empirical mode decomposition,” Neural Networks, vol. 90, pp. 8–20, 2017, doi: 10.1016/j.neunet.2017.03.004. [CrossRef] [Google Scholar]
  5. M. Fayaz, H. Shah, A. Aseere, W. Mashwani, and A. Shah, “A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network,” Technologies, vol. 7, no. 2, p. 30, 2019, doi: 10.3390/technologies7020030. [CrossRef] [Google Scholar]
  6. T. Zhang and X. You, “Improvement of the Training and Normalization Method of Artificial Neural Network in the Prediction of Indoor Environment,” Procedia Eng., vol. 121, pp. 1245–1251, 2015, doi: 10.1016/j.proeng.2015.09.152. [CrossRef] [Google Scholar]
  7. “SILSO | World Data Center for the production, preservation and dissemination of the international sunspot number.” [Online]. Available: [Accessed: 15-Apr2020]. [Google Scholar]
  8. “Compare the effect of different scalers on data with outliers — scikit-learn 0.22.2 documentation.” [Online]. Available: [Accessed: 15-Apr-2020]. [Google Scholar]
  9. S.C. Hicks and R. A. Irizarry, “When to use Quantile Normalization?,” bio Rxiv, p. 012203, 2014, doi: 10.1101/012203. [Google Scholar]
  10. “sklearn.preprocessing.QuantileTransformer — scikit-learn 0.22.2 documentation.” [Online]. Available: [Accessed: 18-Apr2020]. [Google Scholar]
  11. “Basic Feature Engineering With Time Series Data in Python.” [Online]. Available: [Accessed: 18-Apr-2020]. [Google Scholar]
  12. “Forecasting time series: using lag features | Bartosz Mikulski.” [Online]. Available: [Accessed: 18-Apr2020]. [Google Scholar]

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