Abstract
Bu çalışmada hedeflenen Ren
nehrinin düşük debilerini kara kutu modeli yardımıyla iki hafta önceden tahmin
etmektir. Modele eklenecek tanımlayıcı değişkenler korelasyon analizi ile
seçildi. Model girdileri seçildikten sonra model geçmiş gözlemlerle kalibre edildi.
Ardından bir iklim modeli tarafından tahmin edilmiş yağış verisi hidrolojik
modelimize girdi olarak eklendi. Kar yağışının etkin olduğu üst havzalarda düşük
debiler ile havza karakteristik verileri (yağış, buharlaşma ve göl seviyesi
gibi) arasında yüksek korelasyon değerleri bulunurken yağmurun hakim olduğu
aşağı havzalarda korelasyon katsayıları 0.57 ile 0.68 arasında değişmektedir.
Benzetim başarımları Doğu Alp havzası için 0.96 NS (1.0 en yüksek değerdir),
Batı Alp havzası için 0.83 ve Moselle için 0.77 dir. Lobith çıkış noktası için
kalibrasyon ve doğrulama dönemlerindeki tahmin başarımları 0.75 NS civarında
olup sonraki çalısmalar için cesaret vericidir.
References
- Benítez, J.M., Castro, J.L., Requena, I., 1997. Are artificial neural networks black boxes? IEEE Trans. Neural Networks 8, 1156–1164. doi:10.1109/72.623216
- Bouwma, P., 2011. Low flow forecasts for the Rhine at Lobith 14 days ahead A correlation analysis and an artificial neural network study. University of Twente, Enschede (MSc thesis: http://essay.utwente.nl/61075/).
- Danandeh Mehr, A., Demirel, M.C., 2016. On the Calibration of Multigene Genetic Programming to Simulate Low Flows in the Moselle River. Uludag University Journal of The Faculty of Engineering 21, 365–365. doi:10.17482/uumfd.278107
- Danandeh Mehr, A., Kahya, E., 2017. A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction. J. Hydrol. 549, 603–615. doi:10.1016/j.jhydrol.2017.04.045
- Dawson, C.W., Wilby, R.L., 2001. Hydrological modelling using artificial neural networks. Prog. Phys. Geogr. 25, 80–108. doi:10.1177/030913330102500104
- De Bruijn, K.M., Passchier, R., De Bruin, K., Passchier, R., 2006. Predicting low-flows in the Rhine River. WL | Delft Hydraulics, Delft, The Netherlands.
- Demirel, M.C., Booij, M.J., Hoekstra, A.Y., 2013. Identification of appropriate lags and temporal resolutions for low flow indicators in the River Rhine to forecast low flows with different lead times. Hydrol. Process. 27, 2742–2758. doi:10.1002/hyp.9402
- Evans, J., Schreider, S., 2002. Hydrological impacts of climate change on inflows to Perth, Australia. Clim. Change 55, 361–393. doi:10.1023/A:1020588416541
- Grabs, W., Daamen, K., de Montmollin, F., 1997. Impact of Climate Change on Hydrological Regimes and Water Resources Management in the Rhine Basin (CHR-report I-16). CHR/KHR, Lelystad.
- Heezik, A. V., 2008. Strijd om de rivieren: 200 jaar rivierenbeleid in Nederland of de opkomst en ondergang van het streven naar de normale rivier.
- Huisman, P., De Jong, J., Wieriks, K., 2000. Transboundary cooperation in shared river basins: experiences from the Rhine, Meuse and North Sea. Water Policy 2, 83–97. doi:10.1016/S1366-7017(99)00023-9
- Moyle, P.B., Leidy, R.A., 1992. Loss of biodiversity in aquatic ecosystems: evidence from fish faunas, Conservation Biology. doi:10.1007/978-1-4684-6426-9_6
- Rutten, M.M., van de Giesen, N., Baptist, M., Icke, J., Uijttewaal, W., 2008. Seasonal forecast of cooling water problems in the River Rhine. Hydrol. Process. 22, 1037–1045. doi:10.1002/hyp.6988
- Southall, H.L., Simmers, J.A., O’Donnell, T.H., 1995. Direction finding in phased arrays with a neural network beamformer. IEEE Trans. Antennas Propag. 43, 1369–1374. doi:10.1109/8.475924
- Tielrooij, F., 2000. Waterbeleid voor de 21e eeuw: geef water de ruimte en de aandacht die het verdient.
Abstract
The aim of this study
is to predict low flows 14 days in advance using a data-driven model. First, we
apply correlation analysis to select appropriate temporal scales of
pre-selected inputs that are precipitation, potential evapotranspiration,
discharge, groundwater, snow height and lake levels. The forecasted rainfall has
also been used as model input to forecast low flows in the River Rhine at
Lobith. The correlation analysis analysis between low flows and basin
indicators show stronger correlations for the Alpine sub-basins than the
rainfed sub-basins. The Middle and Lower Rhine are downstream channel areas and
they do not contribute to the discharge. Therefore, they are excluded from the
entire analysis. The low flow predictions for the Alpine sub-basins and the
Mosel are reasonable during the validation period, whereas the ANN for Lobith
shows low performance for a different test period. The results for the training
and the validation period are more encouraging than the test period for Lobith,
i.e. Nash-Sutcliffe (NS) efficiency of 0.75 and 0.73 respectively.
Keywords
References
- Benítez, J.M., Castro, J.L., Requena, I., 1997. Are artificial neural networks black boxes? IEEE Trans. Neural Networks 8, 1156–1164. doi:10.1109/72.623216
- Bouwma, P., 2011. Low flow forecasts for the Rhine at Lobith 14 days ahead A correlation analysis and an artificial neural network study. University of Twente, Enschede (MSc thesis: http://essay.utwente.nl/61075/).
- Danandeh Mehr, A., Demirel, M.C., 2016. On the Calibration of Multigene Genetic Programming to Simulate Low Flows in the Moselle River. Uludag University Journal of The Faculty of Engineering 21, 365–365. doi:10.17482/uumfd.278107
- Danandeh Mehr, A., Kahya, E., 2017. A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction. J. Hydrol. 549, 603–615. doi:10.1016/j.jhydrol.2017.04.045
- Dawson, C.W., Wilby, R.L., 2001. Hydrological modelling using artificial neural networks. Prog. Phys. Geogr. 25, 80–108. doi:10.1177/030913330102500104
- De Bruijn, K.M., Passchier, R., De Bruin, K., Passchier, R., 2006. Predicting low-flows in the Rhine River. WL | Delft Hydraulics, Delft, The Netherlands.
- Demirel, M.C., Booij, M.J., Hoekstra, A.Y., 2013. Identification of appropriate lags and temporal resolutions for low flow indicators in the River Rhine to forecast low flows with different lead times. Hydrol. Process. 27, 2742–2758. doi:10.1002/hyp.9402
- Evans, J., Schreider, S., 2002. Hydrological impacts of climate change on inflows to Perth, Australia. Clim. Change 55, 361–393. doi:10.1023/A:1020588416541
- Grabs, W., Daamen, K., de Montmollin, F., 1997. Impact of Climate Change on Hydrological Regimes and Water Resources Management in the Rhine Basin (CHR-report I-16). CHR/KHR, Lelystad.
- Heezik, A. V., 2008. Strijd om de rivieren: 200 jaar rivierenbeleid in Nederland of de opkomst en ondergang van het streven naar de normale rivier.
- Huisman, P., De Jong, J., Wieriks, K., 2000. Transboundary cooperation in shared river basins: experiences from the Rhine, Meuse and North Sea. Water Policy 2, 83–97. doi:10.1016/S1366-7017(99)00023-9
- Moyle, P.B., Leidy, R.A., 1992. Loss of biodiversity in aquatic ecosystems: evidence from fish faunas, Conservation Biology. doi:10.1007/978-1-4684-6426-9_6
- Rutten, M.M., van de Giesen, N., Baptist, M., Icke, J., Uijttewaal, W., 2008. Seasonal forecast of cooling water problems in the River Rhine. Hydrol. Process. 22, 1037–1045. doi:10.1002/hyp.6988
- Southall, H.L., Simmers, J.A., O’Donnell, T.H., 1995. Direction finding in phased arrays with a neural network beamformer. IEEE Trans. Antennas Propag. 43, 1369–1374. doi:10.1109/8.475924
- Tielrooij, F., 2000. Waterbeleid voor de 21e eeuw: geef water de ruimte en de aandacht die het verdient.
Details
| Subjects | Engineering |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | September 19, 2017 |
| Submission Date | September 28, 2016 |
| Acceptance Date | July 30, 2017 |
| Published in Issue | Year 2017 Volume: 22 Issue: 2 |
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