Volume 9, Issue 18 (1-2019)                   jwmr 2019, 9(18): 157-167 | Back to browse issues page

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Samadianfard S, Panahi S. Estimating Daily Reference Evapotranspiration using Data Mining Methods of Support Vector Regression and M5 Model Tree. jwmr. 2019; 9 (18) :157-167
URL: http://jwmr.sanru.ac.ir/article-1-872-en.html
University of Tabriz
Abstract:   (765 Views)
Evapotranspiration is one of the most important components of the hydrological circle and its proper determination is highly important in most researches such as water hydrological balance, design and management of irrigation systems, simulation of crop production and design and management of water resources. Nonlinear characteristic, uncertainty and needing for different climatological data in simulating evapotranspiration are the reasons that motivate researchers to investigate data mining methods such as M5 model trees and support vector regression. In the present study, the precision of mentioned methods in estimation of reference crop evapotranspiration in comparison with empirical methods such as Hargrivs and Torrent white equations was studied. For that purpose, using meteorological dataset of 1371-1394 years of Tabriz synoptic station, the daily values of reference crop evapotranspiration were computed by FAO-Penman-Monteith method. Then, using these computed values as target outputs, 17 various scenarios combining at last one to up to six meteorological parameters have been considered using mentioned methods. Finally, the capability of support vector regression and M5 model trees for estimation of evapotranspiration was analyzed using test data set. Results of statistical analysis and Taylor diagram showed that support vector regression and M5 model trees in a case of considering all meteorological parameters with root mean square of 0.398 and 0.44, respectively, provided precise results comparing with empirical methods such as Hargrivs and Torrent white.
Full-Text [PDF 1490 kb]   (434 Downloads)    
Type of Study: Research | Subject: هيدرولوژی
Received: 2017/11/7 | Revised: 2019/01/21 | Accepted: 2018/07/2 | Published: 2019/01/21

1. Alberg, D., M. Last and A. Kandel. 2012. Knowledge discovery in data streams with regression tree methods. Wiley Interdisciplinary Reviews Data Mining Knowledge Discovery, 2: 69-78. [DOI:10.1002/widm.51]
2. Alikhanzadeh, A. 2006. Data minig. Olome Rayaneh, 340 pp (In Persian).
3. Alizadeh, A. 2002. Irrigation System Design. Ferdowsi University of Mashhad, 450 pp (In Persian).
4. Alizadeh, A. and Gh. Kamali 2008. Crops Water Requirements. Emam reza University of Mashhad, Iran, 227 pp (In Persian).
5. Allen, R.G., L.S. Pereira, D. Raes and M. Smith. 1998. Crop Evapotranspiration. Guidelines for Computing Crop Water Requirements. Irrigation and Drainage Paper No. 56, FAO, Rome, Italy, 300 pp.
6. Arun Raj, V.E. and P.G. Jairaj. 2014. Reference evapotranspiration modelliing using support vector regression. International Journal of Scientific & Engineering Research, 5(7): 2229-5518.
7. Babamiri, O., H. Nowzari and S. Maroofi. 2017. Potential Evapotranspiration Estimation using Stochastic Time Series Models (Case Study: Tabriz), Journal of Watershed Management Research, 8(15): 137-146.
8. Bhattacharya, B. and D.P. Solomatine. 2004. Neural networks and M5 model trees in modeling water level-discharge relationship. Department of Hydroinformatics and Knowledge Management, NESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands. [DOI:10.1016/j.neucom.2004.04.016]
9. Boser, B.E., Guyon, I.M. and V.N. Vapnik. 1992. A training algorithm for optimal margin classifiers. In D.Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152, Pittsburgh, PA. [DOI:10.1145/130385.130401]
10. Chiew, F.H.S., N.N. Kamaladassa, H.M. Malano and T.A. MacMahon. 1995. Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia. Agricultural Water management, 28: 9-21. [DOI:10.1016/0378-3774(95)01172-F]
11. Dastorani, M.T., S. Poormohammadi, A.R. Massah Bavani and M.H. Rahimian. Evapotranspiration Condition in Yazd Station under Uncertainties of Different GHG Emission Scenarios and ET Estimation Models, Journal of Watershed Management Research, 1(2): 1-20
12. Fallahi, M.R., H. Varvani and S. Goliyan. 2012. Precipitation forecasting using regression tree model to flood control. 5th International conference on watershed & soil and water management, Kerman, Iran (In Persian).
13. Gleckler, P.J., K.E. Taylor and C. Doutriaux. 2008. Performance metrics for climate models. Journal of Geophysical Research: Atmospheres, 113(D6): 1-20. [DOI:10.1029/2007JD008972]
14. Jensen, M.E., R.D. Burman and R.G. Allen. 1990. Evapotranspiration and irrigation water requirements. ASCE Manuals and Report on Engineering Practices No. 70. American Society of Civil Engineers, New York, 360 pp.
15. Kisi, O. and M. Cimen. 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399: 132-140. [DOI:10.1016/j.jhydrol.2010.12.041]
16. Kumar, M., N.S. Raghuwanshi, R. Singh, W.W. Wallender and W.O. Pruitt. 2002. Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering-ASCE, 128(4): 224-233. [DOI:10.1061/(ASCE)0733-9437(2002)128:4(224)]
17. Manikumari, N. and G. Vinodhini. 2016. Regression models for predicting reference evapotranspiration. International Journal of Engineering Trends and Technology (IJETT), 38(3): 134-139. [DOI:10.14445/22315381/IJETT-V38P224]
18. Mirhashemi, H. and M. Panahi. 2015. Evaluation of a data mining model in predicting of average temperature and potential evapotranspiration month for the next month in the synoptic weather station Yazd. Biological Forum- An International Journal, 7(1): 1469-1473
19. Naderi, N. and A. Alizadeh. 1998. Determining reference crop evapotranspiration in Mashad and comparing with empirical methods, MSc Thesis, Ferdowsi University of Mashhad. 110 pp (In Persian).
20. Pal, M. 2006. M5 model tree for land cover classification. International Journal of Remote Sensing, 27(4): 825-831. [DOI:10.1080/01431160500256531]
21. Pal, M. and S. Deswal. 2009. M5 model tree based modeling of reference evapotranspiration. Hydrological Processes. 23:1437-1443. [DOI:10.1002/hyp.7266]
22. Panahi S., M. Karbasi and J. Nikbakht. 2016. Forecasting of reference evapotranspiration using MLP, RBF and SVM neural networks. Journal of Environment and Water Engineering, 2(1): 51-63
23. Quinlan, J.R. 1992. Learning with continuous classes. In: Proceedings of Australian Joint Conference on Artificial Intelligence (Singapore: World Scientific Press), pp: 343-348.
24. Samadianfard, S. and E. Asadi. 2017. Prediction of SPI drought index using support vector and multiple linear regressions. Journal of Water and Soil Resources Conservation, 6(4): 1-16 (In Persian).
25. Sattari, M.T., V. Ahmadifar and R. Pashapourar. 2014. M5 tree model based modeling of evaporation losses in Eleviyan reservoir and comparison with empirical equations. Irrigation & Water Engineering. 5(17): 110-122 (In Persian).
26. Sattari, M.T., F. Nahrein and V. Azimi. 2014. M5 model trees and neural networks based prediction of daily ET0 (Case Study: Bonab Station). Irrigation and drainage, 1: 104-113 (In Persian).
27. Sayyadi, H., A. Oladghaffari, A. Faalian and A.A. Sadraddini. 2009. Comparison of RBF and MLP neural networks performance of reference crop evapotranspiration, Water and Soil Science, 19(1): 1-12 (In Persian).
28. Soltani, A., S.M. Mirlatifi and H. Dehghanisanij. 2012. Estimating Reference Evapotranspiration Using Limited Weather Data under Different Climatic Conditions. Journal of Water and Soil, 26(1): 139-149 (In Persian).
29. Taylor, K.E. 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106: 7183-7192. [DOI:10.1029/2000JD900719]
30. Trajkovic, S. 2007. Hargreaves versus Penman-Monteith under humid conditions. Journal of Irrigation and Drainage Engineering-ASCE, 133(1): 38-42. [DOI:10.1061/(ASCE)0733-9437(2007)133:1(38)]
31. Trajkovic, S. and S. Kolakovic. 2009. Estimating reference evapotranspiration using limited weather data. Journal of Irrigation and Drainage Engineering-ASCE, 135(4): 443-449. [DOI:10.1061/(ASCE)IR.1943-4774.0000094]
32. Vapnik, V.N. 1995. The Nature of Statistical Learning Theory. Springer, New York. 314 pp. [DOI:10.1007/978-1-4757-2440-0]
33. Vapnik, V.N. 1998. Statistical Learning Theory. Wiley, New York. 736 pp.

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