Evaluation of Hydrological and Data Mining Models in Monthly River Discharge Simulation and Prediction (Case Study: Araz-Kouseh Watershed)

Document Type : Complete scientific research article

Author

Abstract

Abstract:

Background and objectives: Quantitative prediction of river discharge one of the most important elements in the management of surface water resources, especially take suitable decisions in occurrence of floods and drought events. Various approaches introduced in hydrology to predict river discharge which conceptual models as well as data-driven models are the most important ones.In this study, long term recorded data sets in Araz-Kouseh watershed with 1678 km2 area located in northern Iran (Golestan province) were used to investigate the precision of different river discharge prediction models. The IHACRES model as a conceptual hydrological model and KNN and M5 as data mining models selected for modeling of monthly river discharge and the results were compared to examine the accuracy of studied models. In some studies, the expressed models used for daily river discharge prediction but the main objectives of this study are application of these models to predict monthly discharge for a watershed.

Material and Methods: The 29 years (1985-2013) daily rainfall and discharge data belonging to Araz-Kouseh hydrometry and meteorological stations used to extract monthly time series for modeling. The required quantity and quality conditions of datasets for modeling confirmed using different statistical tests. Recorded datasets divided to two subseries, first one used for calibration period and second one used for validation of investigated models. The results of models in calibration and validation period analyzed considering model efficiency goodness of fit criteria.

Results:The results of IHACRES conceptual hydrological model for both calibration and validation periods (correlation coefficients equal to 0.81 and 0.79 for calibration and validation periods respectively) show suitable ability of this model to predict monthly river discharge. Moreover investigation of results of both M5 and KNN data mining models (correlation coefficient equal to 0.94 and 0.89 for calibration and validation periods respectively for KNN model and equal to 0.92 and 0.88 for calibration and validation periods respectively for M5 model) reveals that application of these models led to significant increase in prediction precision in comparison with IHACRES model.

Conclusions:The results of this study indicate the data mining models, i.e. M5 and KNN, outperform conceptual hydrological model, i.e. IHACRES, for prediction of monthly river discharge considering different goodness of fit criteria. It is clear that the accuracy of the prediction of data mining models are very close to each other but the M5 model is selected as best model in this study because of its explicit equations for prediction. Furthermore, investigation of time series of predicted river discharge show data maining models had better prediction for low discharges in comparison high discharges.

Keywords

References

1.Adib, A., Salarijazi, M., and Najafpour, K. 2010a. Evaluation of Synthetic Outlet Runoff Assessment Models. J. Appl. Sci. Environ. Manage. 14: 3. 13-18.
2.Adib, A., Salarijazi, M., Shooshtari, M.M., and Akhonodali, A.M. 2011. Comparison between characteristics of geomorphoclimatic instantaneous unit hydrograph be produced by GcIUH based Clark Model and Clark IUH model. J. Mar. Sci. Technol. 19: 2. 201-209.
3.Adib, A., Salarijazi, M., Vaghefi, M., Mahmoodian-shooshtari, M., and Akhonali, A.M. 2010b. Comparison between GcIUH-Clark, GIUH-Nash, Clark-IUH, and Nash-IUH models. Turk. J. Engin. Environ. Sci. 34: 91-103.
4.Alikhanzadeh, A. 2007. Data mining, Edition 1, publishing of computer science, Babol. 344p. (In Persian)
5.Besaw, L.E., Rizzo, D.M., Bierman, P.R., and Hackett, W.R. 2010. Advances in ungauged stream flow prediction using artificial neural networks. Hydrology. 386: 27-37.
6.Crooke, B.F.W., Andrews, F., Spate, J., and Cuddy, S.M. 2005. IHACRES user guide. Technical Report 2005/19. Second Edition. iCAM, School of Resources, Environment and Society, The Australian National University, Canberra. http://www.toolkit.net.au/ihacres.
7.Croke, B.F.W., and Jakeman, A.J. 2008. Use of the IHACRES rainfall-runoff model in arid and semi-arid regions, P 41-48. In: H.S. Wheater, S. Sorooshian and K.D. Sharma (Eds.), Hydrological Modelling in Arid and Semi-arid Areas. Cambridge University Press, Cambridge.
8.Croke, B.F.W., and Jakeman, A.J. 2004. A catchment moisture deficit module for the IHACRES rainfall-runoff model. Environmental Modelling and Software. 19: 1-5.
9.El-Shafie, A., RedaTaha, M., and Noureldin, A. 2007. A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan high dam. Water Resource Manage. 21: 533-556.
10.Fallahi, M., Varvani, H., and Golian, S. 2012. Forecast precipitation using regression tree for flood control. 5th conference of watershed and water resource management and land, Kerman. (In Persian)
11.Fathi, R. 2016. Spatial analysis of the hydrological drought. M.Sc. thesis, Gorgan University of Agricultural Sciences and Natural Resources, 99p. (In Persian)
12.Govindaraju, R.S. 2000. Artificial neural network in hydrology. I: Preliminary Concepts. J. Hydrol. Engin. 5: 2. 115-123.
13.Jain, A., and Kumar, A.M. 2007. Hybrid neural network models for hydrologic time series forecasting. Appl. Soft Comp. J. 7: 2. 585-592.
14.Jakeman, A.J., and Hornberger, G.M. 1993. How Much Complexity Is Warranted in a Rainfall-Runoff Model?. Water Resources Research. 29: 2637-2649.
15.Karamooz, M., and Araghinejad, Sh. 2005. Advanced Hydrology. Amirkabir University Press, 464p. (In Persian)
16.Kisi, O. 2005. Daily river flow forecasting using artificial neural networks and auto regressive models. Turk. J. Engin. Environ. Sci. 29: 9-20.
17.Littlewood, I.G., and Jakeman, A.J. 1994. A New Method of Rainfall-Runoff Modelling and its Applications in Catchment Hydrology. Environmental Modelling. 2: 142-171.
18.Lohani, A.K., Kumar, R., and Singh, R.D. 2012. Hydrological time series modeling: A comparison between adaptive neurofuzzy, neural network and autoregressive techniques. J. Hydrol. 442-443: 23-35.
19.McIntyre, N., and Al-Qurashi, A. 2009. Performance of ten rainfall-runoff models applied to an arid catchment in Oman. Environmental Modelling and Software. 24: 726-738.
20.Nabizadeh, M., Mosaedi, A., Hesam, M., and Dehghani, A.A. 2012. Comparing the performance of Fuzzy based models in stream flow on Lighvan River. J. Water Soil Cons. 19: 1. 117-134. (In Persian)
21.Quinlan, J.R. 1992. Learning with continuous classes. In: proceedings AI, 92 (Adams & Sterling, Eds), P 343-348. Singapore: World Scientific.
22.Salajegheh, A., Fathabadi, A., and Gholami, H. 2010. Predict river discharge using the nearest neighbor. 5th national conference on science and management engineering Iran. Gorgan University of Agricultural Sciences and Natural Resources. (In Persian)
23.Sorjamaa, A., Reyhani, N., and Lendasse, A. 2005. Input and structure selection for K-NN approximator. 8th International Conference on Artificial Neural Networks, Lecture Notes in Computer Science Springer, IWANN, Berlin, Pp: 958-992.
24.Taesombat, W., and Sriwongsitanon, N. 2010. Flood Investigation in the Upper Ping river basin using mathematical models. Kasetsart Natural Science. 44: 152-166.
25.Teimoori, F. 2014. Comparative study if meteorological indices with hydrological indices for drought monitoring using data mining method. M.Sc. thesis, Gorgan University of Agricultural Sciences and Natural Resources, 95p. (In Persian)
26.Two Crows Corporation. 1999. Introduction to data mining and knowledge discovery, third ed., Postmac, MD. Available at: www.twocrows.com, (April 29, 2000).
27.Yates, D., Gangopadhyay, S., Rajagopalan, B., and Strzepek, K. 2003. A technique for generating regional climate scenarios using a nearest-neighbor algorithm. Water Resources Research. 39: 7. 1114-1121.
28.Ye, W., Bates, B.C., Viney, N.R., Sivapalan, M., and Jakeman, A.J. 1997. Performance of Conceptual Rainfall-Runoff Models in Low-Yielding Ephemeral Catchments. Water Resources Research. 33: 1. 153-166.
Journal of Water and Soil Conservation
Volume 23, Issue 1
May 2016
Pages 203-217

Files

Share

How to cite

Statistics