Effect of Snow Water Equal Consideration in Runoff Prediction by Using RBF and ANFIS Models

Year 2015, Volume: 36 Issue: 3, 1832 - 1843, 13.05.2015

Mehdi Vafakhah , Fatemeh Sedighi

Abstract

Abstract. Flood is one of devastating natural disasters prediction of which is significantly important. Rainfall-runoff process and flood are physical phenomena with very difficult investigation due to effectiveness of different parameters. Various methods have so far introduced to analyze these phenomena. Current study was aimed to investigate the performance of RBF and ANFIS models in simulation of rainfall-runoff process involved with Snow Water Equivalent (SWE) height in Latian watershed, Tehran province, Iran. Toward this attempt, 92 MODIS images were provided by NASA website during three water years 2003-2005, snow cover surface area in all images was extracted and finally SWE values were calculated for mentioned period. Also, precipitation height, temperature and discharge data of the study period were used for modeling. The results performance comparison of RBF and ANFIS models showed that the latter with rainfall-temperature-SWE inputs, 1-day delay, RMSE of 0.059 and R² of 0.656 and RBF model with rainfall-temperature-SWE inputs, 1-day delay, RMSE of 0.054 and R² of 0.35 had more accurate predictions than other models. It can be concluded from the results that involving SWE in the models improved their performance and increased their accuracy. Also, by comparing the results of ANFIS and RBF models, it can be concluded that ANFIS model with rainfall-temperature-SWE inputs, 1-day delay, RMSE of 0.059 and R² of 0.656 had better and more accurate prediction.

Keywords

Neural-fuzzy model, RBF model, rainfall-runoff model, SWE height, Latian watershed

References

• Alvisi, S., Mascellani, G., Franchini, M., Bardossy, A., 2006. Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrology and Earth System Sciences 10, 1-17.
• Anctil, F., Rat, A., 2005. Evaluation of neural network streamflow forecasting on 47 watersheds. Journal of Hydrologic Engineering 10, 85-88.
• Aqil, M., Kita, I., Yano, A., Nishiyama, S., 2007. A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. Journal of Hydrology 337, 22-34.
• Baareh, A.K., Sheta, A.F., Khnaifes, K.A., 2006. Forecasting river flow in the USA: A Comparison between auto-regression and neural network non-parametric models. Journal of Computer Science 2, 775.
• Banihabib M., Mousavi M., Jamali F., 2010. ANN model for investigation of daily correlation among the stations for prediction of input current into Dez dam. Iranian Water Research Journal 4, 25-32.
• Baratti, R., Cannas, B., Fanni, A., Pintus, M., Sechi, G.M., Toreno, N., 2003. River flow forecast for reservoir management through neural networks. Neurocomputing 55, 421-437.
• Bhattacharya, B., Solomatine, D., 2000. Application of artificial neural network in stage- discharge relationship, Proc. 4th International Conference on Hydroinformatics, Iowa City, USA.
• Christodoulou, C., Georgiopoulos, M., 2000. Applications of neural networks in electromagnetics. Artech House, Inc.
• Dastorani, M.T., Sharifi Darani, H., Talebi, A., Moghadam Nia, A., 2011. Evaluation of the application of artificial neural networks and adaptive neuro-fuzzy inference systems for rainfall-runoff modeling in Zayandeh-rood dam basin. J. of Water and Wastewater 80, 114- 125.
• Dawson, C., Wilby, R., 2001. Hydrological modelling using artificial neural networks. Progress in physical Geography 25, 80-108.
• Deshmukh, R.P.a.G., AA, 2010. Short Term Flood Forecasting Using Recurrent Neural Networks a Comparative Study. IACSIT International Journal of Engineering and Technology 5, 430-434.
• Farahmand, A.S., F., Golkar, M. V. Farahmand, 2011. Modeling of rainfall-runoff in a river basin using artificial neural network, In Proceedings of the First Conference of Applied Research of Water Resources, Kermanshah University of Technology, pp. 141- 147.
• Firat, M., Güngör, M., 2007. River flow estimation using adaptive neuro fuzzy inference system. Mathematics and Computers in Simulation 75, 87-96.
• Hall, D.K., Riggs, G.A., Salomonson, V.V., 2001. Algorithm theoretical basis document (ATBD) for the MODIS snow and sea ice mapping Algorithms. Available at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod10.pdf.
• Hykin, S., 1999. Neural Networks: A Comprehensive Foundation. Printice-Hall. Inc., New Jersey.
• Khan, M.S., Coulibaly, P., 2006. Bayesian neural network for rainfall-runoff modeling. Water Resources Research 42, 1-18.
• Klein, A.G., Hall, D.K., Seidel, K., 1998. Algorithm intercomparison for accuracy assessment of the MODIS snow-mapping algorithm, Proceedings of the 55th Eastern Snow Conference. Citeseer, pp. 3-5.
• Kurtulus, B., M, Razack.,, 2010. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: artificial neural network and neuro-fuzzy. Journal of Hydrology 381, 101–111.
• Mashayekhi, D., 2011. The use of snow hydrology for water resources. Office of Water Resources, Surface Water Section. (In Persian).
• Matreata, M., 2006. Artificial neural networks and fuzzy logic models in operational hydrological forecasting systems, Geophysical Research Abstracts, p. 07889.
• Muharram Pour, M., Kazemi M., Abadi, S., 2011. Daily Runoff Prediction Using Radial Basis Function Neural Networks, First National Conference on Development, Zybaknar Iran.
• Nilsson, P., Uvo, C.B., Berndtsson, R., 2006. Monthly runoff simulation: Comparing and combining conceptual and neural network models. Journal of Hydrology 321, 344-363.
• Pustizadeh, N., Najafi ,N.,, 2011. Discharge prediction by comparing artificial neural network with fuzzy inference system (case study: Zayandehrud River). Iran-Water Resources Research 7, 92-97.
• Raygani, B., Soltani Koupayi, S., Khajeddin, S.J., Barati, S., 2008. Using MODIS Images and NDSI Index for Preparation Snowcover Maps. Journal of the Iranian Natural Research 61, 525-536.
• Server, S., Qadri, S.J., Zare, S., 2011. Rainfall-Runoff Modeling Using ANFIS and Radial Basis Function, Fourth Iranian Water Resources Management Conference. Amirkabir University of Technology, Tehran, Iran.
• Shamseldin, A.Y., 1997. Application of a neural network technique to rainfall-runoff modelling. Journal of Hydrology 199, 272-294.
• Suhaimi, S., Bustami, R.A., 2009. Rainfall Runoff Modeling using Radial Basis Function Neural Network for Sungai Tinjar Catchment, Miri, Sarawak. UNIMAS E-Journal of Civil Engineering 1.
• Tabari, H., Maroufi, S., Zare, H., Amiri, R., and Sharifi M, 2008. Comparison of hybrid and ANN methods for estimation of SWE in Samsami sub-watershed, Third Water Resources Management Conference pp. 1-6.
• Tokar, A.S., Johnson, P.A., 1999. Rainfall-runoff modeling using artificial neural networks. Journal of Hydrologic Engineering 4, 232-239.
• Tokar, A.S., Markus, M., 2000. Precipitation-runoff modeling using artificial neural networks and conceptual models. Journal of Hydrologic Engineering 5, 156-161.
• Vafakhah, M., M., Mohseni Saravi, M., Mahdavi and S. K. Alavipanah, 2011. Snowmelt runoff prediction by using artificial neural network and adaptive neuro–fuzzy inference system in Taleghan Watershed. Iran-Watershed Management Science & Engineering 5, 23- 35.
• Wang, W., Gelder, P.H.V., Vrijling, J., Ma, J., 2006. Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology 324, 383-399.
• Zare Abyaneh, H., Bayat Varkeshi, M., 2011. Evaluation of artificial intelligent and empirical models in estimation of annual runoff. Journal of Water and Soil 25, 365-379.