Application of Support vector machine, CHAID and Random forest models, in estimated daily Reference evapotranspiration in northern Sistan and Baluchestan province

Document Type : Original Article

Authors

1 faculty

2 Faculty of Shiraz University

Abstract

Evapotranspiration as an important component of the hydrological cycle plays a very important role in the planning and management of water resources in dry and hyper dry areas.
Accurate estimation of evapotranspiration requires a costly tool which can not be used anywhere. Hence, researchers are always looking for applied relationships and practices that are low-cost and accurate for the correct estimation of the value of this parameter. Several methods have been developed for the accurate estimation of evapotranspiration throughout the world. Among of these methods can be pointed out empirical equations, Artificial Neural Network, support vector machin, and tree models. Therefore, the purpose of this study was to investigate the accuracy and the capability of linear supporting vector machine models, the decision tree of the type of chiad and the random forest model in the estimation of reference evapotranspiration.The data used in this study include maximum temperature, minimum temperature, average temperature, maximum moisture content, minimum humidity, average humidity, precipitation, sunny hours, wind speed, and a shift from the meteorological station of Sistan Plain between2009-2018. In this study, using meteorological data and the FAO Penman-Monteith model, the values of evapotranspiration were calculated and then by providing different combination scenarios of the meteorological parameters as inputs of the studied models on a daily basis, an attempt was made to find a more accurate estimate of the refrence evapotranspiration as the output of the models. In this research, correlation coefficient (R) and Mean Absolute amount of Error (MAE) were used to compare different model. The results showed among the support model carriers, the random knife, the random forest model with M7 patterns has the highest accuracy with the correlation coefficient (R = 0.983) and the lowest mean error magnitude (MAE = 0.798). Therefore, this research recommends a random forest model for estimating evapotranspiration in the area of Sistan plain.

Keywords

References

Allen, R.G., L.S. Pereira, D. Raes and M. Smith. 1998. Crop evapotranspiration (Guidelines for computing crop water requirements). FAO irrigation and drainage Paper No. 56. Food and Agricultural Organization of the United Nations, Rome, 300p.
Booker, D.J. and Snelder, T. H. 2012. Comparing methods for estimating flow duration curves at ungauged sites. Journal of Hydrology 434–435, 78–94.
Boulesteix, A.L, Janitza, S. Kruppa, J, and König IR. 2012. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol.24 (2), pp: 493-507.
Breiman, L., 2001. Application and analysis of random forests and machine learning. Journal of Water Management, 15(1): 5-32.
Chen S.T., Yu P.Sh., and Tang H.Y. 2010. Statistical downscaling of daily Precipition using support vector machines and multivariate analysis. Journal of Hydrology, 385:13-23.
Dehghanisanij, H., T. Yamamoto and V. Rasiah. 2004. Assessment of evapotranspiration estimation models for use in semiarid environments, Agricultural Water Management, 64: 91-106.
Gislason, PO. Benediktsson, JA. and Sveinsson, JR. 2004. Random forest classification of multisource remote sensing andgeographic data. Journal of Geoscience and Remote Sensing Symposium, Vol. 2, pp: 1049-52.
Guo J., Zhou J., Qin H., Zou Q. and Li Q. (2011). Monthly stream flow forecasting based on improved support vector machine model, Expert Sys. Appl., 38 (10), 13073-13081.
Irmak, S., Haman, D., Jones, J.W., 2002. Evaluation of class ‘A’ pan coefficients for estimating reference evapotranspiration in a humid location. J. Irrig. Drain. Eng. ASCE 128 (3), 153–159.
Kisi O. (2008). The potential of different ANN techniques in evapotranspiration modeling. Hydrol.Proc., 22(14), 2449-2460.
Kisi O. 2009. Modeling monthly evaporation using two different neural computing, techniques Irrigation Science, 27(5): 417-430
Kisi O. Kilic Y. 2015. An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration. TheorAppl Climatol. 1-13p
Kisi, Ozgur, Onur Genc, Semih Dinc, and Mohammad Zounemat-Kermani. "Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree." Computers and Electronics in Agriculture 122 (2016): 112-117.
Moghaddamnia A., Ghafari Gousheh M., Piri J., Amin S., and Han D. 2009. Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources. 32 : 88–97.
Pahlavan Rad, M.R., Toomanian, N., Khormali, F., Brungard, C., Komaki, C.B, and Bogaert, P. 2014. Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran. Journal of Geoderma, Vol. 232, pp: 97–106
Piri, J., S. Amin, A. Moghaddamnia, A. Keshavarz, D. Han and R. Remesan. (2009). ”Daily Pan Evaporation Modeling in a Hot and Dry Climate.” J. Hydrol. Eng., 14(8): 803–811.
Ramaswami, M., & Bhaskaran, R. (2010). A CHAID based performance prediction model in educational data mining. arXiv preprint arXiv:1002.1144.
Shrestha, N. K., & Shukla, S. (2015). Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment. Agricultural and forest meteorology, 200, 172-184.
Tripathi Sh., Srinivas V.V., and Nanjundiah R.S. 2006. Downscaling of precipitation for climate change scenarios: A support vector machine approach.Journal of Hydrology, 330:62- 640.
Wang, K., & Liang, S. (2008). An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature, and soil moisture. Journal of Hydrometeorology, 9(4), 712-727.
Iranian Journal of Irrigation & Drainage
Volume 13, Issue 2 - Serial Number 74
July and August 2019
Pages 378-388

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