Comparison of Neuro-fuzzy and SCS methods in sub-watersheds prioritization for watershed measures (Case study: Taleghan watershed)

Tali-Khoshk, Sadegh; Mohseni Saravi, Mohsen; Vatakhah, Mahadi; Khalighi-Sigarodi, Shahram

doi:10.22059/jrwm.2015.54922

Document Type : Research Paper

Authors

¹ Ph. D. student in Watershed Management, Sari Agricultural and Natural Resources University, I.R. Iran

² Professors, Faculty of Natural Resources, University of Tehran, I.R. Iran

³ Assistant professor, Faculty of Natural Resources, Tarbiat Modares University, I.R. Iran

⁴ Associate professor, Faculty of Natural Resources, University of Tehran, I.R. Iran

https://doi.org/10.22059/jrwm.2015.54922

Abstract

Because of insufficient factors including facilities, budget, human resources as well as time watershed operation is not applicable throughout the basin. As a result, watershed operation should be performed in the sub-basins in which is more affectionate and the risk frequency of some events such as destruction, degradation; physical and financial damage and also flooding are considerably high. In addition, due to hydrometric stations, defects or the lack of stations in some areas, some efforts have been made experts recently to assess and consequently introduce some novel and reliable methods for prioritizing on the basis of current data obtained from sub-basins features of different geographical regions. In current study, the utilization possibilities of neuro-fuzzy technique and SCS in HEC-HMS model that have different potential to examine a wide range of advantageous and disadvantageous in making various decisions were studied. To determine the prediction accuracy of these methods, the rate of flow and sediment output of Taleghan sub-basins were taken over one year. The results of these methods were then compared with the maximum two-year return period flow observations. Our results revealed that in making prioritization, neuro-fuzzy as compared with the SCS method can produce the best prediction as long as the coefficients of errors, efficiency compared to the observational data and predictions are taken into account.

Keywords

References

[1] Aqil, M., Kita, I., Yano, A. and Nishiyama, S. (2007a). Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool, Journal of Environmental Management, 85, 215-223.

[2] Aqil, M., Kita, I., Yano, A. and Nishiyama, S. (2007b). 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.

[3] Arbind, K., Verma, M., Rajesh, K. and Mahana, K. (2010). Evaluation of HEC-HMS and WEPP for simulating watershed runoff using remote sensing and geographical information system, Paddy Water Environ, 8, 131-144.

[4] Bhola, K., Punit and Singh, A. (2010). Rainfall-runoff modeling of river Kosi using SCS-CN method and ANN, Bachelor thesis, Rourkela.

[5] Chen, S., Lin, Y., Chang, L. and Chang, F. (2006). The strategy of building a flood forecast model by neuro fuzzy network, Hydrological processes, 20, 1525-1540.

[6] Fathabadi, A. (2007). River flow prediction by Neurofuzzy and time series analysis, M.Sc. thesis, Tehran University.

[7] Firat, M. and Güngör, M. (2007). River flow estimation using adaptive neuro fuzzy inference system, Mathematics and Computers in Simulation, 75, 87-96.

[8] Foody, G., Ghoneim, E. and Arnell, W. (2004). Predicting Location Sensitive to Flash Flooding in Arid Environment, Journal of Hydrology, 292, 48-58.

[9] Han, J. (2002). Application of artificial neural networks for flood warning systems, Ph.D. thesis, North Carolina University.

[10] Jahangir, A. (2004). Rainfall-runoff simulation with artificial neural network (ANN) and HEC-HMS model in Kardeh watershed, M.Sc. thesis, Sari University.

[11] Jahangir, A., Raeini, M. and Ahmadi, M.Z. (2008). Rainfall-runoff simulation with artificial neural network (ANN) and HEC-HMS model in Kardeh watershed, Water and Soil Journal, 22, 72-84.

[12] Jang, J.S. (1993). ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Management and Cybernetics, 23, 665-685.

[13] Khedri-Tajan, B. (2003). Application of fuzzy logic in prioritizing watershed management operations in the Shahrestanak watershed, M.Sc. thesis, Tarbiat Modares, 110pp.

[14] Khosravi, M. (2008). Flood forecasting by artificial neural networks and emprical equations (Case study: Taleghan watershed), M.Sc. thesis, Tehran University.

[15] Khosroshahi, M. and Saghafian, B. (2003). Determination of sub-basins Participation in flood density, Pajouhesh va Sazandegi, 16, 67-75.

[16] Kisi, O., Haktanir, T., Ardiclioglu, M., Ozturk, O., Yalcin, E. and Uludag, S. (2009). Adaptive neuro-fuzzy computing technique for suspended sediment estimation, Advances in Engineering Software, 40, 438-444.

[17] Klausmeyer, K. (2005). Effects of climate change on the hydrology of upper Alameda Creek, M.Sc. thesis, University of California.

[18] Kurtulus, B. and Razack, M. (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.

[19] Mahdavi, M. (2002). Applied Hydrology, 2^nd Volume, University of Tehran Press.

[20] Nayak, P.C., Sudheer, K.P., Rangan, D.M. and Ramasastri, K.S. (2004). A neuro-fuzzy computing technique for modeling hydrological time series, Journal of Hydrology, 291, 52-66.

[21] Roughani, M., Ghafouri, M. and Tabatabaei, M. (2007). An innovative methodology for the prioritization of sub-catchments for flood control, International Journal of Applied Earth Observation and Geoinformation, 9, 79-87.

[22] Salajeghe, A. and Fathabadi, A. (2009). Suspended sediment evaluation by fuzzy logic and artifial network, Iranian Journal of Natural Resources (Range and Watershed Management), 62, 271-282.

[23] Talei, A., Chua, L.H. and Quek, C. (2010). A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling, Expert Systems with Applications, 37, 7456-7468.

[24] Trahan, M. (2005). Hydrology model of the silver river watershed Baraga country, M.Sc. thesis, Michigan Technological University.

[25] USACE (2000). HEC-HMS Technical Manual, Hydrologic Engineering Center, Davis.

[26] USDI (1975). Water measurement manual, United States government printing office, bureau of reclamation, Washington.

[27] Vafakhah, M. (2008). Simulating snow discharge by artifical neural network, fuzzy logic and measurment data of snow in Taleghan watershed, Ph.D. thesis, Tehran University.

[28] Vafakhah, M. (2012). Application of artificial neural networks and adaptive neuro-fuzzy inference system models to short-term streamflow forecasting, Canadian Journal of Civil Engineering, 39, 402-414.

Journal of Range and Watershed Managment

Comparison of Neuro-fuzzy and SCS methods in sub-watersheds prioritization for watershed measures (Case study: Taleghan watershed)

References

References

Volume 68, Issue 2 - Serial Number 2
September 2015
Pages 213-225

Comparison of Neuro-fuzzy and SCS methods in sub-watersheds prioritization for watershed measures (Case study: Taleghan watershed)

References

References

Volume 68, Issue 2 - Serial Number 2September 2015Pages 213-225

Volume 68, Issue 2 - Serial Number 2
September 2015
Pages 213-225