Skip to main navigation menu Skip to main content Skip to site footer

Peer Reviewed Article

Vol. 2 (2015)

A Single Long Short-Term Memory Network can Predict Rainfall-Runoff at Multiple Timescales

Published
31-03-2015

Abstract

Long Short-Term Memory Networks, otherwise known as LSTMs, have not been left out when it comes to applying them to daily discharge forecasts rather successfully. A good number of experimental cases, be as it may, need forecasts in a manner with a more granular time frame. Case in point, the correct forecast of brief but intense flooding apexes can bring about a difference with the capacity of saving lives in mass. Still, such climaxes have the capability of escaping the rough non-permanent resolve of daily forecasts. Nevertheless, when an LSTM data is naively learned on an hourly data basis, it entails a time-consuming process with lots of stages, which makes the training complex and computationally-cum financially costly. With this research, we suggest a pair of Multi-Time Scale LSTM or MTS-LSTM frameworks that collaboratively forecast a multiplicity of timescales inside a single model. This is done as they proceed with long-past investments in one non-permanent resolve and diversify into every timescale in order to arrive at more current input stages. For this, we carry out a test on these models on a total of 516 basins through the continental United States and standard in comparison with the United States National Water Model. Juxtaposed with naive forecasts that have distinctive LSTM for each time scale, multi-timescale designs will be computationally the more efficient party, suffering no loss of correctness. Outside the quality of predictions, the multiple-facing timescale has the capacity to process a variety of input variables at various timescales. That, in question, proves quite relevant when it comes to operational applications in which meteorological forcings’ lead time is contingent upon their non-permanent resolutions.

References

  1. Ahmed, A. A. A. (2012). Disclosure of Financial Reporting and Firm Structure as a Determinant: A Study on the Listed Companies of DSE. ASA University Review, 6(1), 43-60. https://doi.org/10.5281/zenodo.4008273
  2. Ahmed, A. A. A., & Dey, M. M. (2009). Corporate Attribute and the Extent of Disclosure: A Study of Banking Companies in Bangladesh. Proceedings of the 5th International Management Accounting Conference (IMAC), OCT 19-21, 2009, UKM, Kuala Lumpur, MALAYSIA, Pages: 531-553. https://publons.com/publon/11427801/
  3. Ahmed, A. A. A., & Dey, M. M. (2010). Accounting Disclosure Scenario: An Empirical Study of the Banking Sector of Bangladesh. Accounting and Management Information Systems, 9(4), 581-602. https://doi.org/10.5281/zenodo.4008276
  4. Azad, M. R., Khan, W., & Ahmed, A. A. A. (2011). HR Practices in Banking Sector on Perceived Employee Performance: A Case of Bangladesh. Eastern University Journal, 3(3), 30–39. https://doi.org/10.5281/zenodo.4043334
  5. Bengio, Y., Simard, P., and Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on NeuralNetworks, 5, 157–166, https://doi.org/10.1109/72.279181
  6. Bynagari, N. B. (2014). Integrated Reasoning Engine for Code Clone Detection. ABC Journal of Advanced Research, 3(2), 143-152. https://doi.org/10.18034/abcjar.v3i2.575
  7. Clausen, B. and Biggs, B. J. F. (2000). Flow variables for ecological studies in temperate streams: groupings based on covariance, Journal of Hydrology, 237, 184–197, https://doi.org/10.1016/S0022-1694(00)00306-1
  8. Court, A. (1962). Measures of streamflow timing, Journal of Geophysical Research (1896-1977), 67, 4335–4339, https://doi.org/10.1029/JZ067i011p04335
  9. Donepudi, P. K. (2014). Voice Search Technology: An Overview. Engineering International, 2(2), 91-102. https://doi.org/10.18034/ei.v2i2.502
  10. Gers, F. A., Schmidhuber, J., and Cummins, F. (1999). Learning to forget: continual prediction with LSTM, IET Conference Proceedings, pp.850–855.
  11. Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: impli-cations for improving hydrological modelling, Journal of Hydrology, 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003
  12. Hochreiter, S. and Schmidhuber, J. (1997). Long Short-Term Memory, Neural Computation, 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735
  13. Maleque, R., Rahman, F., & Ahmed, A. A. A. (2010). Financial Disclosure in Corporate Annual Reports: A Survey of Selected Literature. Journal of the Institute of Bangladesh Studies, Vol. 33, 113-132. https://doi.org/10.5281/zenodo.4008320
  14. Manavalan, M. (2014). Fast Model-based Protein Homology Discovery without Alignment. Asia Pacific Journal of Energy and Environment, 1(2), 169-184. https://doi.org/10.18034/apjee.v1i2.580
  15. Manavalan, M., & Ganapathy, A. (2014). Reinforcement Learning in Robotics. Engineering International, 2(2), 113-124. https://doi.org/10.18034/ei.v2i2.572
  16. Rouf, M. A., Hasan, M. S., & Ahmed, A. A. A. (2014). Financial Reporting Practices in the Textile Manufacturing Sectors of Bangladesh. ABC Journal of Advanced Research, 3(2), 125-136. https://doi.org/10.18034/abcjar.v3i2.38
  17. --0--

Similar Articles

You may also start an advanced similarity search for this article.