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Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection

Year 2020, Volume: 11, 125 - 130, 31.12.2020

Abstract

Water resources are needed to maintain the human life and the management the ecologic system for many areas. The most economical use, protection and development of water resources have a great importance for hydrological studies. Variable such as stream flow data are commonly used in hydrology. Accurate stream flow estimation is very important in terms of planning and management of water resources and minimizing the effects of natural disasters such as drought and flood. Monthly river flow data obtained from the Sakarya basin on Porsuk River between 1970-2000 years were used for the estimation study. For this purpose, forecasting performance has been analyzed using Adaptive Network Based Fuzzy Logic Inference System (ANFIS) and Artificial Neural Networks (ANN) models and performances of these two models were compared. In addition, the average monthly stream flow data, standard deviation values of these data were also used in the forecasting study and applied as an input to ANFIS and ANN models. For a one ahead estimation, models have been developed with different input combinations of 1-3 past value of stream flow data and standard deviation values. In this study, mean square error (mse), mean absolute error (mae) and correlation coefficient parameters were used to evaluate the performance of the models. According to the obtained results, it is seen that the ANN model has better forecasting performance for two inputs according to mse and mae parameters and for three inputs according to R and R2 parameters. Also, it is seen that the ANFIS model has the best performance for two inputs according to mse, mae, R and R2 parameters. There has been some improvement in the forecast performance if the monthly average river stream flow data as well as the standard deviation data has been applied as an input to the model

References

  • 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, 390, 85-91.
  • Bayazıt M., (1998). Hidrolojik Modeller, İTÜ İnşaat Fakültesi Matbaası, İstanbul.
  • Chow V.T., Maidment D.R. & Mays L.W., (1988). Applied Hydrology, McGraw-Hill, NY.
  • Cigizoglu, H. K. (2005). Application of generalized regression neural networks to intermittent flow forecasting and estimation. Journal of Hydrologic Engineering, 10(4), 336-341.
  • Jain, A., & Kumar, A. M. (2007). Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing, 7(2), 585-592.
  • Jang, J.S.R., (1993). ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern, 23, 665-685.
  • Dehghani, M., Seifi A., & Riahi-Madvar H., (2019). Novel forecasting models for immediate-short-term to long-term influent flow prediction by combining ANFIS and grey wolf optimization, Journal of Hydrology, 576, 698-725.
  • Hadi, S.J., & Tombul M., (2018), Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination, Journal of Hydrology, 561, 674-687.
  • Hagan, M.T., Demuth H.B., & Beale M.H., (1996). Neural Network Design. Boston. MA: PWS Publishing.
  • Haykin, S., (1994), Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Company Inc., New.
  • He, Y., Yan Y., Wang X., & Wang C., (2019). Uncertainty Forecasting for Streamflow based on Support Vector Regression Method with Fuzzy Information Granulation. Energy Procedia, 158, 6189-6194.
  • Marquardt, DW., (1963). An algorithm for least-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics, 11, 431-441.
  • Mehr, A. D., Kahya, E., Şahin, A., & Nazemosadat, M. J. (2015). Successive-station monthly streamflow prediction using different artificial neural network algorithms. International Journal of Environmental Science and Technology, 12, 2191-2200.
  • www.dsi.gov.tr/faaliyetler/akim-gozlem-yilliklari , (accession date February, 2020)
Year 2020, Volume: 11, 125 - 130, 31.12.2020

Abstract

References

  • 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, 390, 85-91.
  • Bayazıt M., (1998). Hidrolojik Modeller, İTÜ İnşaat Fakültesi Matbaası, İstanbul.
  • Chow V.T., Maidment D.R. & Mays L.W., (1988). Applied Hydrology, McGraw-Hill, NY.
  • Cigizoglu, H. K. (2005). Application of generalized regression neural networks to intermittent flow forecasting and estimation. Journal of Hydrologic Engineering, 10(4), 336-341.
  • Jain, A., & Kumar, A. M. (2007). Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing, 7(2), 585-592.
  • Jang, J.S.R., (1993). ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern, 23, 665-685.
  • Dehghani, M., Seifi A., & Riahi-Madvar H., (2019). Novel forecasting models for immediate-short-term to long-term influent flow prediction by combining ANFIS and grey wolf optimization, Journal of Hydrology, 576, 698-725.
  • Hadi, S.J., & Tombul M., (2018), Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination, Journal of Hydrology, 561, 674-687.
  • Hagan, M.T., Demuth H.B., & Beale M.H., (1996). Neural Network Design. Boston. MA: PWS Publishing.
  • Haykin, S., (1994), Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Company Inc., New.
  • He, Y., Yan Y., Wang X., & Wang C., (2019). Uncertainty Forecasting for Streamflow based on Support Vector Regression Method with Fuzzy Information Granulation. Energy Procedia, 158, 6189-6194.
  • Marquardt, DW., (1963). An algorithm for least-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics, 11, 431-441.
  • Mehr, A. D., Kahya, E., Şahin, A., & Nazemosadat, M. J. (2015). Successive-station monthly streamflow prediction using different artificial neural network algorithms. International Journal of Environmental Science and Technology, 12, 2191-2200.
  • www.dsi.gov.tr/faaliyetler/akim-gozlem-yilliklari , (accession date February, 2020)
There are 14 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Levent Latıfoglu

Publication Date December 31, 2020
Published in Issue Year 2020Volume: 11

Cite

APA Latıfoglu, L. (2020). Evaluating Stream Flow Forecasting Performance Using Adaptive Network Based Fuzzy Logic Inference System, Artificial Neural Networks with Feature Selection. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 11, 125-130.