Open Access
E3S Web Conf.
Volume 353, 2022
8th International Conference on Energy and City of the Future (EVF’2021)
Article Number 01006
Number of page(s) 9
Section City, Environment & Buildings of the Future
Published online 29 June 2022
  1. D.W. Goshime, R. Absi, B. Ledésert, Evaluation and Bias Correction of CHIRP Rainfall Estimate for Rainfall-Runoff Simulation over Lake Ziway Watershed, Ethiopia, Hydrology, 6(3), 68. [Google Scholar]
  2. D.W. Goshime, R. Absi, A.T. Haile; B. Ledésert, T. Rientjes 2020 “Bias-Corrected CHIRP Satellite Rainfall for Water Level Simulation, Lake Ziway, Ethiopia”, Journal of Hydrologic Engineering, Volume 25 Issue 9. [CrossRef] [Google Scholar]
  3. M. Lazri, K. Labadi, J.M. Brucker, S. Ameur, Improving satellite rainfall estimation from MSG data in Northern Algeria by using a multi-classifier model based on machine learning, Journal of Hydrology, Volume 584, 2020, 124705. [CrossRef] [Google Scholar]
  4. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural networks, vol. 2, no. 5, pp. 359–366, 1989. 8. [CrossRef] [Google Scholar]
  5. Guang-Bin H., Qin-Yu Z., Chee-Kheong S. (2006) Extreme learning machine: Theory and applications. Neurocomputing 70(1-3):489–501. [CrossRef] [Google Scholar]
  6. Leung H.C., Leung C.S., Wong E.W.M. (2019) Fault and Noise Tolerance in the Incremental Extreme Learning Machine. IEEE Access 7:155171–155183. [CrossRef] [Google Scholar]
  7. Li H.-T., Chou C.-Y., Chen Y.-T., Wang S.-H., Wu A.-Y. (2019) Robust and Lightweight Ensemble Extreme Learning Machine Engine Based on Eigenspace Domain for Compressed Learning. IEEE Trans Circuits Syst I: Regular Papers 66(12):4699—4712. [CrossRef] [Google Scholar]
  8. EUMETSAT, 2004. Applications of Meteosat Second Generation - Conversion from Counts to Radiances and from Radiances to Brightness Temperatures and Reflectance, [Google Scholar]
  9. Thies, B., Nauss, T., Bendix, J., 2008. Precipitation process and rainfall intensity differentiation using Meteosat second generation spinning enhanced visible and infrared imager data. J. Geophys. Res. 113. [Google Scholar]
  10. Lazri, M., Ouallouche, F., Ameur, S., Brucker, J.M., Mohia, Y., 2012. Identifying convective and stratiform rain by confronting SEVERI sensor multispectral infrared to radar sensor data using neural network. Sens. Transducers J. 145 (10), 19–32. [Google Scholar]
  11. Feidas, H., Giannakos, A., 2011. Identifying precipitating clouds in Greece using multispectral infrared Meteosat Second Generation satellite data. Theor. Appl. Climatol. 104, 25–42. [CrossRef] [Google Scholar]
  12. Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice-Hall (2009) [Google Scholar]
  13. Yu, H., Wilamowski, B.: The Industrial Electronics Handbook, vol. 5. CRC (2011). [Google Scholar]
  14. Zhen Nan, L., Li, Q.F., Nguyen, L.B., Xu, G.H., 2018. Comparing machine-learning models for drought forecasting in Vietnam’s cai river basin. Pol. J. Environ. Stud. 27 (6), 2633–2646. [CrossRef] [Google Scholar]
  15. Deo, R.C., Sahin, M., 2015. Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia. Atmos. Res. 153, 512–525., 2015. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.