|تعداد مشاهده مقاله||103,617,678|
|تعداد دریافت فایل اصل مقاله||81,462,964|
Application of Machine Learning Approaches in Rainfall-Runoff Modeling (Case Study: Zayandeh_Rood Basin in Iran)
|Civil Engineering Infrastructures Journal|
|مقاله 4، دوره 51، شماره 2، اسفند 2018، صفحه 293-310 اصل مقاله (1.2 M)|
|نوع مقاله: Research Papers|
|شناسه دیجیتال (DOI): 10.7508/ceij.2018.02.004|
|Mohammad Taghi Dastorani 1؛ Javad Mahjoobi2؛ Ali Talebi3؛ Farzane Fakhar4|
|1Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad|
|22Water Recourse Management Company, Yazd Regional Water Authority, Iran|
|3Associate Professor, Faculty of Natural Resources, Yazd University, Iran|
|4Faculty of Natural Resources , Yazd University, Yazd, Iran|
|Run off resulted from rainfall is the main way of receiving water in most parts of the World. Therefore, prediction of runoff volume resulted from rainfall is getting more and more important in control, harvesting and management of surface water. In this research a number of machine learning and data mining methods including support vector machines, regression trees (CART algorithm), model trees (M5 algorithm) and artificial neural networks have been used to simulate rainfall- runoff process in Zayandeh_rood dam basin in Iran. Data used in this research included 9 years of daily precipitation, minimum temperature, maximum temperature, mean temperature, mean relative humidity of daily times 6:30, 12:30 and 18:30 and run off. A number of 3294 lines of data were totally used, and simulations were carried out in two different conditions: without previous run off data as input vectors (M1 condition), and with previous runoff data as input vectors of the models (M2 condition). Results show that machine learning techniques used in this research are not able to present acceptable predictions of runoff in M1 condition (without previous runoff data). However, predictions are considerably improved when previous runoff data are used as input beside other inputs (M2 condition). Between the models used in this research support vector machines (SVM) presented the most accurate results, as the values of RMSE for results presented by SVM, regression tree, model tree and artificial neural network are 2.4, 6.71, 3.2 and 3.04, respectively.|
|ANN؛ Cart؛ Decision Tree؛ Machine learning؛ Rainfall-runoff؛ SVM|
Adamowski, J. and Prasher, S.O. (2012). “Comparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data”, Journal of Water and Land Development, 17(1), 89-97.
Aqil, M., Kita, I., Yano, A. and 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(1-2), 22-34.
Barzegari, F., Yosefi, M. and Talebi, A. (2015). “Estimating suspended sediment by Artificial Neural Network (ANN), Decision Trees (DT) and Sediment Rating Curve (SRC) models (Case study: Lorestan Province, Iran)”, Civil Engineering Infrastructures Journal, 48(2), 373-380.
Bhadra, A., Bandyopadhyay, A., Singh, R. and Raghuwanshi, N.S. (2010). “Rainfall-runoff modeling: Comparison of two approaches with different data requirements, water resources management”, Water Resources Management, 24(1), 37-62.
Breiman, L., Friedman, J.H., Olshen, R.A. and Stone C.J. (1984). Classification and regression trees, Belmont, Wadsworth Statistical Press.
Dastorani, M.T., Moghadamnia, A.R., Piri, J. and Rico-Ramirez, M. (2010). “Application of ANN and ANFIS models for reconstructing missing flow data”, Environmental Monitoring and Assessment, 166(1-4), 421-434.
Etemad-Shahidi, A. and Mahjoobi, J. (2009). “Comparison between M5' model tree and neural networks for prediction of significant wave height in Lake Superior”, Ocean Engineering, 36(15-16), 1175-1181.
El-shafie, A., Mukhlisin, M., Najah, A.A. and Taha, M.R. (2011). “Performance of artificial neural network and regression techniques for rainfall-runoff prediction”, International Journal of the Physical Sciences, 6(8), 1997-2003.
Granata, F., Gargano, R. and Marinis, G. (2016). “Support vector regression for Rainfall-Runoff modeling in urban drainage: A comparison with the EPA’s storm water management model”, Water, 8(3), 1-13.
Huang, W. and Foo, S. (2002). “Neural network modelling of salinity variation in Apalachicola river”, Water Research, 36(1), 356-362.
Kamali, B. and Mousavi, S.J. (2014). “Automatic calibration of HEC-HMS model using Multi-Objective fuzzy optimal models”, Civil Engineering Infrastructures Journal, 47(1), 1-12.
Karimaee Tabarestani, M. and Zarrati, A.R. (2015). “Design of riprap stone around bridge piers using empirical and neural network method”, Civil Engineering Infrastructures Journal, 48(1), 175-188.
Machado, F., Mine, M., Kaviski, E. and Fill, H. (2011). “Monthly rainfall-runoff modelling using artificial neural networks”, Hydrological Sciences Journal, 56(3), 349-361.
Mahjoobi, J. and Adeli Mosabbeb, E. (2009). “Prediction of significant wave height using regressive support vector machines”, Ocean Engineering, 36(5), 339-347.
Platt, J. (1999). “Fast training of support vector machines using sequential minimal optimization”, Advances in Kernel Methods, Support Vector Learning, Schölkopf_Burges, C.J.C. and Smola, A.J., (eds.), Cambridge, MA, MIT Press, 185-208.
Quinlan, J.R. (1992). “Learning with continuous classes”, Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence, World Scientific, Singapore, 343-348.
Shortridge, J.E., Guikema, S.D. and Zaitchik, B.F. (2016). “Machine learning methods for empirical streamflow simulation: A comparison of model accuracy, interpretability and uncertainty in seasonal watersheds”, Hydrological Earth System Sciences, 20, 2611-2628.
Smola, A.J. and Schölkopf, B. (1988). “A tutorial on support vector regression”, Royal Holloway College, London, U.K., NeuroCOLT Technology Report, TR 1998-030.
Vapnik, V. (1995). The nature of statistical learning tTheory, Springer, N.Y.
Wang, Y. and Witten, I.H. (1997). “Induction of model trees for predicting continuous lasses”, Proceedings of the Poster Papers of the European Conference on Machine Learning, University of Economics, Faculty of Informatics and Statistics, Prague.
Wu, C.L. and Chau, K.W. (2011). “Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis”, Journal of Hydrology, 399(3-4), 394-409.
Yilmaz, A. and Muttil, N. (2014). “Runoff estimation by machine learning methods and application to the Euphrates Basin in Turkey”, Journal of Hydrologic Engineering, 19(5), 1015-1025.
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