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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-article-BAT9-0032-0025

Czasopismo

Journal of Water and Land Development

Tytuł artykułu

Comparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data

Autorzy Adamowski, J.  Prasher, S. O. 
Treść / Zawartość
Warianty tytułu
PL Porównanie metod uczenia maszynowego do prognozowania spływu w zlewniach górskich na podstawie ograniczonych danych
Języki publikacji EN
Abstrakty
EN Runoff forecasting in mountainous regions with processed based models is often difficult and inaccurate due to the complexity of the rainfall-runoff relationships and difficulties involved in obtaining the required data. Machine learning models offer an alternative for runoff forecasting in these regions. This paper explores and compares two machine learning methods, support vector regression (SVR) and wavelet networks (WN) for daily runoff forecasting in the mountainous Sianji watershed located in the Himalayan region of India. The models were based on runoff, antecedent precipitation index, rainfall, and day of the year data collected over the three year period from July 1, 2001 and June 30, 2004. It was found that both the methods provided accurate results, with the best WN model slightly outperforming the best SVR model in accuracy. Both the WN and SVR methods should be tested in other mountainous watershed with limited data to further assess their suitability in forecasting.
PL Prognozowanie spływu z obszarów górskich z użyciem programowanych modeli jest często trudne i niedokładne z powodu złożonych zależności między opadem a spływem i problemów związanych z pozyskaniem niezbędnych danych. Modele uczenia maszynowego stwarzają alternatywę dla prognozowania spływu z takich regionów. W pracy analizowano i porównano dwie metody uczenia maszynowego - metodę regresji wektorów nośnych (SVR) i sieci falkowych (WN) do dobowego prognozowania spływu w górskiej zlewni Sianji, usytuowanej w indyjskiej części Himalajów. Modele opracowano na podstawie danych o spływie, wskaźniku poprzednich opadów, opadzie i kolejnym dniu roku za trzyletni okres od 1 lipca 2001 r. do 30 czerwca 2004 r. Stwierdzono, że obie metody zapewniają dokładne wyniki, przy czym najlepszy model WN nieco przewyższa najlepszy model SVR pod względem dokładności. Obie metody powinny być testowane w innych zlewniach górskich o ograniczonej liczbie danych, aby lepiej ocenić ich przydatność do prognozowania.
Słowa kluczowe
PL Himalaje   prognozowanie spływu   regresja wektora wsparcia   sieci falkowe   uczenie maszynowe  
EN Himalayas   machine learning   runoff forecasting   support vector regression   wavelet networks  
Wydawca Wydawnictwo ITP
Czasopismo Journal of Water and Land Development
Rocznik 2012
Tom no. 17 [VII-XII]
Strony 89--97
Opis fizyczny Bibliogr. 29 poz., rys., tab.
Twórcy
autor Adamowski, J.
autor Prasher, S. O.
  • Department of Bioresource Engineering, McGill University, 21 111 Lakeshore Road, Ste. Anne de Bellevue, QC, Canada, H9X 3V9, jan.adamowski@mcgill.ca
Bibliografia
ADAMOWSKI J., CHAN H.F. 2011. A wavelet neural Network conjunction model for groundwater level forecasting. Journal of Hydrology. No 407 p. 28–40.
ADAMOWSKI J., CHAN H.F., PRASHER S.O., SHARDA V.N. 2011. Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for rainfall-runoff forecasting in Himalayan micro watersheds with limited data. Journal of Hydroinformatics. No 3 p. 731–744.
ADAMOWSKI J., KARAPATAKI C. 2010. Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms. Journal of Hydrology. No 15 p. 729–743.
ADAMOWSKI J., SUN K. 2010. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology. No 390 p. 85–91.
ASEFA T., KEMBLOWSKI M., MCKEE M., KHALIL A. 2005. Multi-time scale stream flow predictions: The support vector machines approach. Journal of Hydrology. No 318 p. 7–16.
BEHZAD M., ASGHARI K., EAZI M., PALHANG M. 2009. Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with Applications. No 36 p. 7624–7629.
CANNAS B., FANNI A., SIAS G., SEE L. 2006. Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning. Physics and Chemistry of the Earth. No 31 p. 1164–1171.
CASTELLANO-MENDEZ M., GONZALEZ-MANTEIGA W., FEBRERO-BANDE M., PRADA-SANCHEZ J., LOZANO-CALDERON R. 2004. Modelling of the monthly and daily behavior of the runoff of the Xallas River using Box-Jenkins and neural networks methods. Journal of Hydrology. No 296 p. 38–58.
CHANG C., LIN C. 2001. LIBSVM: a Library for Support Vector Machines [online]. [Access 20.10.2012]. Available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
CRISTIANINI N., SHAWE-TAYLOR J. 2000. An introduction to support vector machines and other kernel-based learning methods. New York. Cambridge University Press. ISBN 0-521-78019-5 pp. 204.
GUNN S. 1998. Support vector machines for classification and regression. Technical report. Southampton. University of Southampton. Faculty of Engineering, Science and Mathematics, School of Electronics and Computer Science pp.54.
KANG K.W., KIM J.H., PARK C.Y., HAM K.J. 1993. Evaluation of hydrological forecasting system based on neural network model. In: Proceedings of the 25th Congress of the International Association for Hydraulic Research. Delft, The Netherlands p. 257–264.
KISI O. 2009. Neural networks and wavelet conjunction model for intermittent streamflow forecasting. Journal of Hydrologic Engineering. No 14 p. 773–782.
KRISHNA B., RAO Y., NAYAK P. 2011. Time series modeling of river flow using wavelet neural networks. Journal of Water Resource and Protection. No 1 p. 50–59.
MULLER B., REINHARDT J. 1991. Neural networks: An introduction. Ser. Physics of Neural Networks. Vol. 2. New York. Springer-Verlag. ISBN 0387523804 pp. 266.
NILSSON P., UVO C., BERNTDSSON R. 2005. Monthly runoff simulation: Comparing and combining conceptual and neural and network models. Journal of Hydrology. No 321 p. 344–363.
PANDA R.K., PRAMANIK N., BALA B. 2009. Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Computers and Geosciences. No 36 p. 735–745.
PARTAL T. 2009. River flow forecasting using different artificial neural network algorithms and wavelet transform. Canadian Journal of Civil Engineering. No 36 p. 26–38.
PARTAL T., KISI O. 2008. Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology. No 342 p. 199–212.
PRAMANIK N., PANDA R.K. 2009. Application of neural network and adaptive neuro fuzzy inference systems for stream flow prediction. Hydrological Science Journal. No 54 p. 247–260.
SANG Y., WANG D., WU J. 2009. Comparative study of some improved ANN models for hydrologic time series forecast. Intelligent Systems, GCIS '09, WRI Global Congress. May 19–21, 2009. Xiamen, China. Los Alamitos. IEEE Computer Society. Vol. 4 p. 63–67.
SHAMSELDIN A.Y. 2010. Artificial neural network model for river flow forecasting in a developing country. Journal of Hydroinformatics. No 12 p. 22–35
SHARDA V., PATEL R.M., PRASHER S.O., OJASVI P.R., PRAKASH C. 2006. Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques. Agriculture Water Management. No 83 p. 233–242.
SMOLA A., SCHOLKOPF A. 1998. A tutorial on support vector regression. NeuroCOLT2 Technical Report NC2-TR-1998-030 pp. 71.
TIWARI M.K., CHATTERJEE C. 2010. A new wavelet-bootstrap-ANN hybrid model for daily discharge forecasting. Journal of Hydroinformatics. No 13 p. 500–519.
VAPNIK V. 1995. The nature of statistical learning theory. Ser. Information Science and Statistics. New York. Springer Verl. ISBN 978-0-387-98780-4 pp. 314.
WANG W., CHAU K.C., CHENG L.Q. 2009. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology. No 374 p. 294–306.
WU C., CHAU K., LI Y. 2009. Methods to improve neural network performance in daily flows prediction. Journal of Hydrology. No 372 p. 80–93.
ZEALAND C., BURN D.H., SIMONOVIC S.P. 1998. Short term streamflow forecasting using artificial neural networks, Journal of Hydrology. No 214 p. 32–48.
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