Analysis of a gaussian process and feed-forward neural networks based filter for forecasting short rainfall time series
Fil: Rodriguez Rivero, C. Universidad Nacional de Córdoba; Argentina.
| Main Authors: | , , , , , |
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| Format: | conferenceObject |
| Language: | eng |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/11086/29596 |
| _version_ | 1801211443506315264 |
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| author | Rodriguez Rivero, C. Pucheta, J. Patiño, H. Baumgartner, J. Laboret, S. Sauchelli, V. |
| author_facet | Rodriguez Rivero, C. Pucheta, J. Patiño, H. Baumgartner, J. Laboret, S. Sauchelli, V. |
| author_sort | Rodriguez Rivero, C. |
| collection | Repositorio Digital Universitario |
| description | Fil: Rodriguez Rivero, C. Universidad Nacional de Córdoba; Argentina. |
| format | conferenceObject |
| id | rdu-unc.29596 |
| institution | Universidad Nacional de Cordoba |
| language | eng |
| publishDate | 2022 |
| record_format | dspace |
| spelling | rdu-unc.295962022-11-13T09:45:45Z Analysis of a gaussian process and feed-forward neural networks based filter for forecasting short rainfall time series Rodriguez Rivero, C. Pucheta, J. Patiño, H. Baumgartner, J. Laboret, S. Sauchelli, V. Artificial neural networks Rainfall forecast Hursts parameter Analysis of kernel Bayesian adjustment Fil: Rodriguez Rivero, C. Universidad Nacional de Córdoba; Argentina. Fil: Pucheta, J. Universidad Nacional de Córdoba; Argentina. Fil: Patiño, H. Universidad Nacional de Córdoba; Argentina. Fil: Baumgartner, J. Universidad Nacional de Córdoba; Argentina. Fil: Laboret, S. Universidad Nacional de Córdoba; Argentina. Fil: Sauchelli, V. Universidad Nacional de Córdoba; Argentina. In this paper, an analysis of kernel (GP) and feed-forward neural networks (FFNN) based filter to forecast short rainfall time series is presented. For the FFNN, the learning rule used to adjust the filter weights is based on the Levenberg-Marquardt method and Bayesian approach by the assumption of the prior distributions. In addition, a heuristic law is used to relate the time series roughness with the tuning process. The input patterns for both NN-based and kernel models are the values of rainfall time series after applying a time-delay operator. Hence, the NN´s outputs will tend to approximate the current value of the time series. The time lagged inputs of the GP and their covariance functions are both determined via a multicriteria genetic algorithm, called NSGA-II. The optimization criteria are the quantity of inputs and the filter´s performance on the known data which leads to Pareto optimal solutions. Both filters -FFNN and GP Kernel- are tested over a rainfall time series obtained from La Sevillana establishment. This work proposed a comparison of well-known filter referenced in early work where the contribution resides in the analysis of the best horizon of the forecasted rainfall time series proposed by Bayesian adjustment. The performance attained is shown by the forecast of the next 15 months values of rainfall time series from La Sevillana establishment located in (-31° 1´22.46"S, 62°40´9.57"O) Balnearia, Cordoba, Argentina. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6706741&isnumber=6706705 Fil: Rodriguez Rivero, C. Universidad Nacional de Córdoba; Argentina. Fil: Pucheta, J. Universidad Nacional de Córdoba; Argentina. Fil: Patiño, H. Universidad Nacional de Córdoba; Argentina. Fil: Baumgartner, J. Universidad Nacional de Córdoba; Argentina. Fil: Laboret, S. Universidad Nacional de Córdoba; Argentina. Fil: Sauchelli, V. Universidad Nacional de Córdoba; Argentina. Sistemas de Automatización y Control 2022-11-11T13:14:49Z 2022-11-11T13:14:49Z 2013 conferenceObject 978-1-4673-6128-6 http://hdl.handle.net/11086/29596 eng Attribution-NonCommercial-ShareAlike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/ Electrónico y/o Digital |
| spellingShingle | Artificial neural networks Rainfall forecast Hursts parameter Analysis of kernel Bayesian adjustment Rodriguez Rivero, C. Pucheta, J. Patiño, H. Baumgartner, J. Laboret, S. Sauchelli, V. Analysis of a gaussian process and feed-forward neural networks based filter for forecasting short rainfall time series |
| title | Analysis of a gaussian process and feed-forward neural networks based filter for forecasting short rainfall time series |
| title_full | Analysis of a gaussian process and feed-forward neural networks based filter for forecasting short rainfall time series |
| title_fullStr | Analysis of a gaussian process and feed-forward neural networks based filter for forecasting short rainfall time series |
| title_full_unstemmed | Analysis of a gaussian process and feed-forward neural networks based filter for forecasting short rainfall time series |
| title_short | Analysis of a gaussian process and feed-forward neural networks based filter for forecasting short rainfall time series |
| title_sort | analysis of a gaussian process and feed forward neural networks based filter for forecasting short rainfall time series |
| topic | Artificial neural networks Rainfall forecast Hursts parameter Analysis of kernel Bayesian adjustment |
| url | http://hdl.handle.net/11086/29596 |
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