Modeling of the flow time series for a short-term hydrological forecast
Aim of the study: Within this article an example of an effective approach to real-time, short term forecast of flood rates within Vistula river differential catchment was presented. This forecast is based on flow rates time series measured at the water gauge input and output cross sections of the r...
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| Format: | Article |
| Language: | English |
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University of Agriculture in Krakow
2020-11-01
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| Series: | Acta Scientiarum Polonorum. Formatio Circumiectus |
| Subjects: | |
| Online Access: | http://acta.urk.edu.pl/pdf-126852-69581?filename=Modeling%20of%20the%20flow%20time.pdf |
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| _version_ | 1841563871819595776 |
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| author | Tomasz Jarosław Siuta |
| author_facet | Tomasz Jarosław Siuta |
| author_sort | Tomasz Jarosław Siuta |
| collection | DOAJ |
| description | Aim of the study:
Within this article an example of an effective approach to real-time, short term forecast of flood rates within Vistula river differential catchment was presented. This forecast is based on flow rates time series measured at the water gauge input and output cross sections of the river system with a daily delay without taking into account any precipitation data.
Material and methods:
In order to assess the quality of the forecast, four types of time series models were developed for the Smolice outlet gage station. The first type of model is the conventional linear autoregressive relationship (AR), the second one - three layer neural network feedforward (SSN), the third one – two layer recursive neural network and the fourth one- three layer special kind of recurrent neural network (RNN). All models were trained and tested based on historical flood events data.
Results and conclusions:
Among the all tested model types, the most accurate prediction of the instantaneous value of the flow rate in the outlet cross section of the Vistula catchment was obtained using the RNN model. This type of model also had the greatest ability to generalize results confirmed by three independent tests. |
| format | Article |
| id | doaj-art-ba35ee3cebd840ac80d510cb19b3d3b6 |
| institution | Kabale University |
| issn | 1644-0765 |
| language | English |
| publishDate | 2020-11-01 |
| publisher | University of Agriculture in Krakow |
| record_format | Article |
| series | Acta Scientiarum Polonorum. Formatio Circumiectus |
| spelling | doaj-art-ba35ee3cebd840ac80d510cb19b3d3b62025-01-02T23:28:41ZengUniversity of Agriculture in KrakowActa Scientiarum Polonorum. Formatio Circumiectus1644-07652020-11-01193314https://doi.org/10.15576/ASP.FC/2020.19.3.3Modeling of the flow time series for a short-term hydrological forecastTomasz Jarosław Siuta0Politechnika KrakowskaAim of the study: Within this article an example of an effective approach to real-time, short term forecast of flood rates within Vistula river differential catchment was presented. This forecast is based on flow rates time series measured at the water gauge input and output cross sections of the river system with a daily delay without taking into account any precipitation data. Material and methods: In order to assess the quality of the forecast, four types of time series models were developed for the Smolice outlet gage station. The first type of model is the conventional linear autoregressive relationship (AR), the second one - three layer neural network feedforward (SSN), the third one – two layer recursive neural network and the fourth one- three layer special kind of recurrent neural network (RNN). All models were trained and tested based on historical flood events data. Results and conclusions: Among the all tested model types, the most accurate prediction of the instantaneous value of the flow rate in the outlet cross section of the Vistula catchment was obtained using the RNN model. This type of model also had the greatest ability to generalize results confirmed by three independent tests.http://acta.urk.edu.pl/pdf-126852-69581?filename=Modeling%20of%20the%20flow%20time.pdfriver systemrecurrent neural networkflow rate time seriesshort-term forecastpeak flow rate |
| spellingShingle | Tomasz Jarosław Siuta Modeling of the flow time series for a short-term hydrological forecast Acta Scientiarum Polonorum. Formatio Circumiectus river system recurrent neural network flow rate time series short-term forecast peak flow rate |
| title | Modeling of the flow time series for a short-term hydrological forecast |
| title_full | Modeling of the flow time series for a short-term hydrological forecast |
| title_fullStr | Modeling of the flow time series for a short-term hydrological forecast |
| title_full_unstemmed | Modeling of the flow time series for a short-term hydrological forecast |
| title_short | Modeling of the flow time series for a short-term hydrological forecast |
| title_sort | modeling of the flow time series for a short term hydrological forecast |
| topic | river system recurrent neural network flow rate time series short-term forecast peak flow rate |
| url | http://acta.urk.edu.pl/pdf-126852-69581?filename=Modeling%20of%20the%20flow%20time.pdf |
| work_keys_str_mv | AT tomaszjarosławsiuta modelingoftheflowtimeseriesforashorttermhydrologicalforecast |