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Impacts of combining meteorological and hydrometric data on the accuracy of streamflow modeling | ||
| Environmental Resources Research | ||
| مقاله 7، دوره 7، شماره 2، فروردین 2019، صفحه 147-164 اصل مقاله (874.64 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22069/ijerr.2019.4822 | ||
| نویسندگان | ||
| M. Motamednia* 1؛ A. Nohegar2؛ A. Malekian3؛ M Saberi Anari4؛ K. Karimi Zarchi5 | ||
| 1Ph.D. of Watershed Management Science and Engineering, | ||
| 2Professor of Learning, faculty of environment, University of Tehran, Karaj, Iran | ||
| 3Associate Professor, Department of Rehabilitation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran | ||
| 4Instructor of Technical and Vocational University, Yazd, Iran | ||
| 5The head of Natural Resources, Bafgh District, Yazd, Iran | ||
| چکیده | ||
| Proper modeling of rainfall-runoff is essential for water quantity and quality management. However, comprehensive evaluation of soft computing techniques for rainfall-runoff modeling in developing countries is still lacking. Towards this end, in the present study two new soft computing techniques of genetic programming (GP) and M5 model tree were formulated for daily streamflow prediction. Firstly, the daily meteorological and hygrometric data including rainfall, temperature, evapotranspiration, relative humidity and discharge were collected for the years 1970 - 2012 throughout Amameh Watershed in Tehran, Iran. Secondly, the input variables were determined using cross-correlation and then 62 different scenarios were developed. Thirdly, the data were standardized in the range of zero to one. Finally, performance of the scenarios was assessed using the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). Totally, 80 and 20 percent of instances were used for training and testing, respectively. The results showed that the scenario number 54 was the best using both GP and M5 model tree techniques. However, GP showed much better performance than M5 model tree with MSE, RMSE, and MAE values of 0.001, 0.031 and 0.009 for training and 0.001, 0.032 and 0.009 for testing, respectively. The scenario 54 had eight inputs including rainfall, discharge, and delay for two days, temperature, evapotranspiration and relative humidity. | ||
| کلیدواژهها | ||
| Genetic programming؛ Model development؛ M5 model tree؛ Scenario analysis؛ Streamflow prediction | ||
| مراجع | ||
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Abrahart, R.J., Anctil, F., Coulibaly, P., Dawson, C.W., Mount, N.J., See, L.M., Shamseldin, A.Y., Solomatine, D.P., Toth, E., and Wilby, R.L. 2012. Two decades of anarchy? Emerging themes and outstanding challenges for neural network modelling of surface hydrology. Progress in Physial Geography, 36 (4), 480–513.
Ajmera, T.K., Goyal, M.K. 2012. Development of stage–discharge rating curve using model tree and neural networks: An application to Peachtree Creek in Atlanta, Expert Systems with Applications, 39, 5702–5710.
Al-Juboori A.M., and Guven A. 2016. A stepwise model to predict monthly streamflow. Journal of Hydrology, 543, 283-292.
Babovic, V., and Keijzer, M. 2000.Geneticprogrammingasamodelinductionengine. Journal of Hydroinform, 2, 35–60.
Behzad, M., Asghari, K., Eazi, M., and Palhang, M. 2009. Generalization performance of support vector machines and neural networks runoff modelling. Expert System with Applications, 36, 7624-7629.
Besaw, L.E., Rizzo, D.M., Bierman, P.R., and Hackett, W.R. 2010. Advances in ungauged streamflow prediction using artificial neural networks. Journal Hydrology, 386, 27–37.
Brameier, M., and Banzhaf, W. 2001. A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans. Evolutionary Computation, 5, 17–26. Bhattacharya, B., and Solomatine, D.P. 2005. Neural networks and M5 model trees in modelling water level–discharge relationship. Neurocomputing, 63, 381-396.
Chandwani, V., Vyas, S.K., Agrawal, V., and Sharma, G. 2015. Soft computing approach for rainfall-runoff modelling: A review, International Conference onWater Resources, Costal and Ocean Engineering (ICWRCOE 2015), Aquatic Procedia, 4, 1054-1061.
Danandeh Mehr, A., Kahya, E., and Olyaie, E. 2013. Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique, Journal of Hydrology, 505, 240–249.
Danandeh Mehr, A., Kahya, E., and Yerdelen, C. 2014. Linear genetic programming application for successive-station monthly streamflow prediction, Journal of Computers and Geosciences, 70, 63–72.
Dawson, C.W., and Wilby, R.L. 2001. Hydrological modelling using artificial neural network, Progress in Physical Geography, 25(1), 80–108.
Dooge, J.C.I. 1977. Problems and methods of rainfall-runoff modelling. In: Ciriani, T.A., U. Maione and J.R. Wallis. 2008. Mathematical Models for Surface Water Hydrology: The Workshop Held at the IBM Scientific Center, Pisa. Wiley, London, 71-108.
Etemad-Shahidi, A., and Mahjoobi, J. 2009. Comparison between M5 model tree and neural networks for prediction of significant wave height in Lake Superior, Journal of Ocean Engineering, 1175-1181.
Fernandoa, A.K., Shamseldinb, A.Y., and Abrahart, R.J. 2011. Comparison of two data-driven approaches for daily river flow forecasting, Proceedings of 19th International Congress on Modelling and Simulation, Perth, Australia, pp.1077-1083.
Ghorbani, M.A., Khatibi, R., Mehr, A.D., and Asadi, H. 2018. Chaos-based multigene genetic programming: A new hybrid strategy for river flow forecasting. Journal of Hydrology, 562, 455-467.
Ghorbani, M.A.,Khatibi,R., Aytek, A., Makarynskyy, O., Shiri, J.,2010.Seawaterlevel forecasting using genetic programming and artificial neural networks. Computers and Geoscience. 36(5), 620–627.
Guven A. 2009. Linear genetic programming for time-series modelling of daily flow rate. Journal of earth system science, 118(2), 137-146.
Hao, Y.H., Yeh, T.C.J., Gao, Z.Q.,Wang, Y.R., and Zhao, Y. 2006. A gray system model for studying the response to climate change: the Liulin Karst springs, China. J. Hydrol. 328, 668.
Harun, S., Ahmat Nor, N.I., and Kassim, M.A.H. 2002. Artificial neural network model for rainfall-Runoff Relationship. Journal of technology, 37, 1-12.
Huo, Z., Feng, S., Kang, S., Huang, G., Wang, F., and Guo, P. 2012. Integrated neural networks for monthly river flow estimation in arid inland basin of Northwest China, Journal of Hydrology, 420–421, 159–170.
Jayawardena AW, Muttil N Fernando TMKG, 2005. Rainfall-Runoff Modelling using Genetic Programming.Pp.1841-1847. International Congress on Modelling and Simulation Society of Australia and New Zealand December 2005, New Zealand.
Keshtegar, B., Mert, C., and Kisi, O. 2018. Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree, Renewable and Sustainable Energy Reviews, 81(1), 330-341.
Khu ST, Liong SY, Babovic V, Madsen H and Muttil N, 2001. Genetic programming and its application in real- time runoff forming. Journal of American Water Resource Associate, 37(2), 439-451.
Koza, J.R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA.
Legates, D.R., and McCabe, G.J. 1999. Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resources Research 35, 233–41
Liong, S.Y., Gautam, T.R., Khu, S.T., Babovic, V., Keijzer, M., and Muttil N. 2002. Genetic Programming: A new paradigm in rainfall runoff modelling. Search Results Journal of the American Water Resources Association, 38(3), 705-718.
Liu, K., Yao, C., Chen, J., Li, Z., Li, Q., and Sun, L. 2017. Comparison of three updating models for real time forecasting: a case study of flood forecasting at the middle reaches of the Huai River in East China. Stochastic Environmental Research and Risk Assessment, 31(6), 1471-1484.
Lu, X., Wang, X., Zhang, L., Zhang, T., Yang, C., Song, X., and Yang, Q. 2018. Improving forecasting accuracy of river flow using gene expression programming based on wavelet decomposition and de-noising. Hydrology Research, 49(3), 711-723
Meshgi, A., Schmitter, P., Chui, T.F.M., and Babovic, V. 2015. Development of a modular streamflow model to quantify runoff contributions from different land uses in tropical urban environments using Genetic Programming, Journal of Hydrology, 525,711–723.
Motamednia, M., Nohegar, A., Malekian, A., Asadi, H., Tavasoli, A., Safari, M., Karimi and Zarchi, K. 2015. Daily river flow forecasting in a semi-arid region using two data- driven, Desert, 20, 11-21.
Najafzadeh, M., Rezaie-Balf, M., Rashedi, E. (2016). Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models. Journal of Hydroinformatics, 18(5), 867-884
Najafzadeh, M., Rezaie-Balf, M., and Tafarojnoruz, A. 2018. Prediction of riprap stone size under overtopping flow using data-driven models. International Journal of River Basin Management, 1-8.
Ni, Q., Wang, L., Ye, R., Yang, F., and Sivakumar, M. 2010. Evolutionary modelling for streamflow forecasting with minimal datasets: a case study in the West Malian River, China. Environmental Engineering Science, 27(5), 377-385.
Nourani, V., Singh, V.P., and Delafrouz, H. 2009. Three geomorphological rainfall–runoff models based on the linear reservoir concept, Catena, 76, 206–214.
Quinlan, J.R. 1992. Learning with continuous classes. In: Adams, Sterling, editors. Proceedings of AI'92. World Scientific. p. 343-348.
Rezaie-Balf, M., and Kisi, O. 2017. New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine. Hydrology Research, 49(3), 939-953.
Rezaie-Balf, M., Naganna, S.R., Ghaemi, A., and Deka, P.C. 2017. Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting. Journal of Hydrology, 553, 356-373.
Rezaie-Balf, M., Kim, S., Fallah, H., and Alaghmand, S. 2019. Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea, Journal of Hydrology, 572, 470-485.
Sattari, M.T., Pal, M., Apaydin, H., and Ozturk, F. 2013. M5 Model Tree Application in Daily River Flow Forecasting in Sohu Stream, Turkey, Water Resources, 40 (3), 233–242.
Selle, S., and Muttil, N. 2011. Testing the structure of a hydrological model using Genetic Programming, Journal of Hydrology, 397, 1–9.
Sinivasulu, S., and Jain, A. 2006. A comparative analysis of training methods for artificial neural network rainfall-runoff models. Applied Soft Computing, 6, 295-306.
Solaimani, K. 2009. Rainfall-Runoff prediction based on artificial neural network (A case study: Jarahi Watershed). American-Eurasian Journal of Agriculture and Environment Science, 5(6), 856-865.
Solomatine, D.P., and Dulal, K.N. 2003. Model trees as an alternative to neural networks in rainfall–runoff modelling. Hydrological Sciences Journal, 48(3), 399-411.
Solomatine, D.P., and Xue, Y. 2004. M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China. ASCE Journal of Hydrologic Engineering, 9(6), 491-501.
Sreekanth, J., and Datta, B. 2011. Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization. Water Resources Research, 47(4), W04516.
Talebi, A., Mahjoobi, J., Dastorani, M.T., and Moosavi, V. 2017. Estimation of suspended sediment load using regression trees and model trees approaches (Case study: Hyderabad drainage basin in Iran). ISH Journal of Hydraulic Engineering, 23(2), 212-219.
Ustoorikar, K., and Deo, M.C. 2008. Filling up gaps in wave data with genetic programming. Marine Structures, 21, 177-195.
Wang, W.C., Chau, K.W., Cheng, Ch.T., and Qiu, L. 2009. A Comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series, Journal of Hydrology, 374 (3-4), 294-306.
Wang, Y., Guo, S., Chen, H., and Zhou, Y. 2014. Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir. Stoch. Environ. Res. Risk Assess. 28, 555–570.
Whigham, P.A., and Crapper, P.F. 2001. Modelling Rainfall-Runoff Relationships using Genetic Programming, Mathematic land Computer Modelling, 33, 707-721.
Witten, I.H., and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann: San Francisco. 2005.
Wu, C.L., Chau, K.W., and Li, Y.S. 2009. Methods to improve neural network performance in daily flows prediction, Journal of Hydrology, 372 (1-4) 80-93.
Wu, W.Y., and Chen, S.P. 2005. A prediction method using the grey model GMC (1, n) combined with the grey relational analysis: a case study on Internet access population forecast. Applied Mathematics and Computation, 1, 1-10.
Zhang D., and Tsai J.J.P. 2007. Advances in Machine Learning Applications in Software Engineering, Idea Group Inc. | ||
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