AN INVESTIGATION ON MODELING OF DALAMAN STREAM FLOWS BY USING WAVE-ANFIS

Year 2018, Volume: 6 Issue: 1, 56 - 63, 26.03.2018

Dilek Taylan

https://doi.org/10.21923/jesd.380158

Cited By: 1

Abstract

The object of the study is to investigate a flow estimation
model by using a combination of Wavelet Transform Technique (W) and Adaptive Neural
Based Fuzzy Inference System (ANFIS). Many models has been applied in recent
years for the prediction of Dalaman Stream flow in the south of Turkey. One of
these studies was AR-ANFIS models which developed by Taylan (2008), its
training data set was extended with synthetic series produced by autoregressive
processes. In this study, W-ANFIS models were developed with sub-series
generated by wavelet analysis by using extended training set of ANFIS models.  It is seen that increasing the number of
input data in training increases model performance. Compared with the developed
models, it has been shown that the W-ANFIS hybrid models have a better
predictive power than the AR-ANFIS models. Consequently, the W-ANFIS hybrid
model could be used successfully in predicting of flow.

Keywords

Dalaman Stream, Wavelet, ANFIS

DALGACIK-ADAPTIF AĞ TEMELLI BULANIK ÇIKARIM SISTEMLERI ILE DALAMAN ÇAYI AKIMLARININ MODELLENMESİ ÜZERİNE BİR ÇALIŞMA

Abstract

Çalışmanın amacı Dalgacık analizi ile Adaptif Ağ Temelli
Bulanık Çıkarım Sistemini bir arada kullanarak bir akım tahmin modeli
geliştirmektir. Türkiye’nin güneyinde yer alan Dalaman Çayı akımlarının tahmini
için pek çok model uygulanmıştır. Bu çalışmalardan biri de Taylan (2008)
tarafından geliştirilen eğitim seti otoregresif süreçlerle üretilen sentetik
serilerle genişletilmiş AR-ANFIS modellerdir. Bu çalışmada,  ANFIS modellerinin eğitim seti dalgacık
analizi kullanılarak üretilen alt serilerle genişletilerek W-ANFIS modeller
geliştirilmiştir. Girdi veri setlerinin genişletilmesinin model performansını
artırdığı görülmüştür. Geliştirilen modeller karşılaştırıldığında, W-ANFIS
hibrit modellerinin AR-ANFIS modellerinden daha iyi bir tahmin yeteniğine sahip
oldukları gösterilmiştir. Sonuç olarak W-ANFIS hibrit modeli akım tahmininde
başarı ile kullanılabilir. 

Keywords

Dalaman Çayı, Dalgacık Analizi, Adaptif Ağ Temelli Bulanık Çıkarım Sistemi

References

• Awan, J., Bae, D-H., 2014. Improving ANFIS based model for long-term dam inflow prediction by incorporating monthly rainfall forecasts. Water Resour. Manag., 28 (5), 1185–1199.
• Xu, J., Chen, Y., Li, W., Nie, Q., Song, C., Wei, C., 2014. Integrating wavelet analysis and BPANN to simulate the annual runoff with regional climate change: a case study of Yarkand River, Northwest China. Water Resour. Manag., 28 (9), 2523–2537.
• Valipour, M., Banihabib, M.E., Behbahani, S.M.R., 2013. Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J. Hydrol., 476, 433–441.
• Turan, M., Yurdusev, M., 2014. Predicting monthly river flows by genetic fuzzy systems. Water Resour. Manag., 28 (13), 4685–4697.
• Yarar, A., 2014. A hybrid wavelet and neuro-fuzzy model for forecasting the monthly streamflow data. Water Resour. Manag., 28 (2), 553–565.
• Taylan, E.D., 2008. Application Of Intelligent Systems For Flow Forecasting In Region Of Mediterranean. Ph.D. Thesis. Süleyman Demirel University Graduate School of Applied and Natural Sciences, Turkey.
• Keskin, M.E., Taylan, D., 2009. Artificial models for interbasin flow prediction in southern Turkey. ASCE Journal of Hydrologic Engineering., 14, 752–758.
• Box, G. E. P., Jenkins, G. M., Reinsel, G. C., 1994. Time Series Analysis, Forecasting and Control. Prentice-Hall, Englewood Cliffs, New Jersey, USA.
• Keskin, M.E., Taylan, D., Terzi, Ö., 2006. Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series. Hydrological Sciences Journal, 51, 588–598.
• Pektaş A.O., Cigizoglu, K.H., 2013. ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient. J. Hydrol., 500, 21–36
• Salas, J.D., Delleur, J.W., Yevjevich, V.M., Lane, W.L. 1980. Applied modeling of hydrologic time series. Water Resources Publications, Littleton
• Valipour, M., 2012. Parameters estimate of autoregressive moving average and autoregressive integrated moving average models and compare their ability for inflow forecasting. J. Math. Stat., 8 (3), 330–338.
• Chen, S.H., Lin, Y.H., Chang, L.C., Chang, F. J., 2006. The strategy of building a flood forecast model by neuro-fuzzy network. Hydrol. Processes, 20, 1525–1540.
• Tingsanchali, T., Gautam, M. R,. 2000. Application of tank, NAM, ARMA and neural network models to flood forecasting. Hydrol. Processes. 14, 2473–2487.
• Zadeh, L. A., 1965 Fuzzy sets. Information Control, 8(3), 338–353.
• See, L., Openshaw, S., 2000. Applying soft computing approaches to river level forecasting. Hydrol. Sci. J., 44(5), 763–779.
• Hundecha, Y., Bardossy, A., Theisen, H. W., 2001. Development of a fuzzy logic based rainfall–runoff model. Hydrol. Sci. J. 46(3), 363–377.
• Xiong, L. H., Shamseldin, A. Y., , O’Connor, K. M., 2001. A nonlinear combination of the forecasting of rainfall–runoff models by the first order Takagi-Sugeno fuzzy system. J. Hydrol., 245(1/4), 196–217.
• Jang, J. S. R., 1992. Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans. Neural Networks, 3(5), 714–723.
• Chang, L. C., Chang, F.J., 2001. Intelligent control for modelling of real-time reservoir operation. Hydrol. Processes, 15, 1621–1634.
• Wang W.C., Chau, K.W.,, Xu, D. M.,, Chen, X. Y., 2015. Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resour. Manag., 29, 2655–2675.
• Shafaei, M., Kisi, O., 2015. Lake level forecasting using wavelet-SVR, Wavelet-ANFIS and wavelet-ARMA conjunction models. Water Resour. Manag,, 30(1), 79–97.
• Badrzadeh, H., Sarukkalige, R., Jayawardena, A. W., 2013. Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting, Journal of Hydrology, 507, 75-85.
• Kisi, O., Cimen, M., 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting, J. Hydrol., 399(1–2), 132–140.
• Nourani, V., Alami, M. T., Aminfar, M. H., 2009. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation, Engineering Applications of Artificial Intelligence, 22(3), 466-472. Box, G. E. P., Jenkins, G. M., 1970. Time Series Analysis, Forecasting and Control. Holden-Day, San Francisco, California, USA.
• DeLurgio, S. A., 1998. Forecasting Principles and Applications. McGraw-Hill, New York, USA.
• Tsoukalas, L. H., Uhrig, R. E., 1997. Fuzzy and Neural Approaches in Engineering. Wiley-Interscience, John Wiley & Sons. Inc., New York, USA.
• Lin, C.T., Lee, C.S.G., 1995. Neural fuzzy systems, New Jersey, USA, Prentice Hall PTR 797. Wang, W., Ding, J., 2003. Wavelet network model and its application to the predication of hydrology. Nature and Science, 1(1), 67–71.