Abstract
In the present study, a hybrid intelligent model called SVR_RSM, which was extracted using response surface method (RSM) combined by the support vector regression (SVR) approaches was applied for predicting monthly pan evaporation (Epan). This method is established based on two basic calibrating process using RSM and SVR. In the first process, an input data group with two different input variables are used to calibrate the RSM; hence, the calibrating data by RSM in the first process are applied as input database for calibrating the SVR in the second process. Results obtained using the proposed SVR_RSM was compared with those obtained using the RSM, SVR, and the well-known multilayer perceptron neural network (MLPNN) models. Climatic variables including maximum and minimum temperatures (Tmax, Tmin), wind speed (U2), and relative humidity (H%), and the periodicity represented by the month number (α) were selected for predicting the monthly Epan measured with the standard class A evaporation pan. Data was collected at six climatic stations located at the northern East of Algeria. The performances of the proposed models were compared using the RMSE, MAE, modified index of agreement (d), coefficient of correlation (R), and modified Nash and Sutcliffe efficiency (NSE). Using various input combination, the results show that the hybrid SVR_RSM model performed better than all the proposed models. Overall, better accuracy was observed when the model contained the periodicity (α), and it was demonstrated that the best accuracy was obtained using only Tmax and Tmin, coupled with the periodicity.
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References
Allawi MF, El-Shafie A (2016) Utilizing RBF-NN and ANFIS methods for multi-lead ahead prediction model of evaporation from reservoir. Water Resour Manag 30(13):4773–4788. https://doi.org/10.1007/s11269-016-1452-1
Brereton RG, Lloyd GR (2010) Support vector machines for classification and regression. Analyst 135:230–267. https://doi.org/10.1039/B918972F
Cahoon JE, Costello TA, Ferguson JA (1991) Estimating pan evaporation using limited meteorological observations. Agric For Meteorol 55(3-4):181–190. https://doi.org/10.1016/0168-1923(91)90061-T
Dao VN, Vemuri VR (2002) A performance comparison of different back propagation neural networks methods in computer network intrusion detection. Diff Equ Dyn Sys 10(1&2):201–214
Deo RC, Samui P, Kim D (2016) Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models. Stoch Env Res Risk A 30(6):1769–1784. https://doi.org/10.1007/s00477-015-1153-y
Eray O, Mert C, Kisi O (2018) Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation. Hydrol Res. https://doi.org/10.2166/nh.2017.076
Feng Y, Jia Y, Zhang Q, Gong D, Cui N (2018) National-scale assessment of pan evaporation models across different climatic zones of China. J Hydrol 564:314–328. https://doi.org/10.1016/j.jhydrol.2018.07.013
Fun MH, Hagan MT (1996) Levenberg-Marquardt training for modular networks, Neural Networks. IEEE International Conference on. IEEE, pp. 468-473. https://doi.org/10.1109/ICNN.1996.548938.
Ghorbani MA, Deo RC, Yaseen ZM, Kashani MH, Mohammadi B (2018) Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theor Appl Climatol 133(3-4):1119–1131. https://doi.org/10.1007/s00704-017-2244-0
Guven A, Kisi O (2011) Daily pan evaporation modeling using linear genetic programming technique. Irrig Sci 29:135–145. https://doi.org/10.1007/s00271-010-0225-5
Heddam S, Keshtegar B, Kisi O (2019) Predicting total dissolved gas concentration on a daily scale using kriging interpolation method (KIM), response surface method (RSM) and artificial neural network (ANN): case study of Columbia River Basin Dams, USA. Nat Resour Res. https://doi.org/10.1007/s11053-019-09524-2
Keshtegar B, Heddam S (2018) Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study. Neural Comput & Applic 30(10):2995–3006. https://doi.org/10.1007/s00521-017-2917-8
Keshtegar B, Kisi O (2016) A nonlinear modelling-based high-order response surface method for predicting monthly pan evaporations. Hydrol Earth Syst Sci Discuss. https://doi.org/10.5194/hess-2016-191
Keshtegar B, Kisi O (2017) Modified response-surface method: new approach for modeling pan evaporation. ASCE J Hydro Engin 22(10):04017045. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001541
Keshtegar B, Mert C, 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 81:330–341
Keshtegar B, Seghier MEAB (2018) Modified response surface method basis harmony search to predict the burst pressure of corroded pipelines. Eng Fail Anal 89:177–199. https://doi.org/10.1016/j.engfailanal.2018.02.016
Keshtegar B, Bagheri M, Yaseen ZM (2019a) Shear strength of steel fiber-unconfined reinforced concrete beam simulation: Application of novel intelligent model. Compos Struct 212:230–242. https://doi.org/10.1016/j.compstruct.2019.01.004
Keshtegar B, Heddam S, Kisi O, Zhu SP (2019b) Modelling total dissolved gas (TDG) concentration at Columbia River Basin dams: high-order response surface method (H-RSM) vs. M5Tree, LSSVM and MARS. Arab J Geosci 12:544. https://doi.org/10.1007/s12517-019-4687-3
Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J Hydrol 351(3-4):299–317. https://doi.org/10.1016/j.jhydrol.2007.12.014
Kisi O (2013) Evolutionary neural networks for monthly pan evaporation modeling. J Hydrol 498:36–45. https://doi.org/10.1016/j.jhydrol.2013.06.011
Kisi O (2015) Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J Hydrol 528:312–320. https://doi.org/10.1016/j.jhydrol.2015.06.052
Kisi O, Tombul M (2013) Modeling monthly pan evaporations using fuzzy genetic approach. J Hydrol 477:203–212. https://doi.org/10.1016/j.jhydrol.2012.11.030
Kowsar R, Keshtegar B, Miyamoto A (2019) Understanding the hidden relations between pro-and anti-inflammatory cytokine genes in bovine oviduct epithelium using a multilayer response surface method. Sci Rep 9(1):3189. https://doi.org/10.1038/s41598-019-39081-w
Kurt H, Kayfeci M (2009) Prediction of thermal conductivity of ethylene glycol-water solutions by using artificial neural networks. Appl Energy 86:2244–2248. https://doi.org/10.1016/j.apenergy.2008.12.020
Lu CJ (2014) Sales forecasting of computer products based on variable selection scheme and support vector regression. Neurocomputing 128:491–499. https://doi.org/10.1016/j.neucom.2013.08.012
Lu X, Ju Y, Wu L, Fan J, Zhang F, Li Z (2018) Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models. J Hydrol 566:668–684. https://doi.org/10.1016/j.jhydrol.2018.09.055
Malik A, Kumar A, Kisi O (2017) Monthly pan-evaporation estimation in Indian central Himalayas using different heuristic approaches and climate based models. Comput Electron Agric 143:302–313. https://doi.org/10.1016/j.compag.2017.11.008
McMahon TA, Peel MC, Lowe L, Srikanthan R, McVicar TR (2013) Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrol Earth Syst Sci 17(4):1331–1363. https://doi.org/10.5194/hess-17-1331-2013
Pathirage CSN, Li J, Li L, Hao H, Liu W, Ni P (2018) Structural damage identification based on autoencoder neural networks and deep learning. Eng Struct 172:13–28. https://doi.org/10.1016/j.engstruct.2018.05.109
Qasem SN, Samadianfard S, Kheshtgar S, Jarhan S, Kisi O, Shamshirband S, Chau KW (2019) Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engin Applic Comput Fluid Mech 13(1):177–187. https://doi.org/10.1080/19942060.2018.1564702
Rezaie-Balf M, Kisi O, Chua LH (2018) Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation. Hydrol Res. https://doi.org/10.2166/nh.2018.050
Sebbar A., Heddam S., Djemili L. (2019). Predicting daily pan evaporation (Epan) from dams reservoirs in the Mediterranean regions of Algeria: OPELM vs OSELM. Environmental Process. https://doi.org/10.1007/s40710-019-00353-2.
Shiri J (2019) Evaluation of a neuro-fuzzy technique in estimating pan evaporation values in low-altitude locations. Meteorol Appl. https://doi.org/10.1002/met.1753
Shiri J, Kisi O (2011) Application of artificial intelligence to estimate daily pan evaporation using available and estimated climatic data in the Khozestan Province (South Western Iran). ASCE J Irrig Drain Eng 137:412–425. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000315
Shiri J, Dierickx W, Pour-Ali BA, Neamati S, Ghorbani MA (2011) Estimating daily pan evaporation from climatic data of the State of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrol Res 42(6):491–502. https://doi.org/10.2166/nh.2011.020
Thissen U, Pepers M, Üstün B, Melssen WJ, Buydens LMC (2004) Comparing support vector machines to PLS for spectral regression applications. Chemom Intell Lab Syst 73(2):169–179. https://doi.org/10.1016/j.chemolab.2004.01.002
Wang L, Niu Z, Kisi O, Li C, Yu D (2017) Pan evaporation modeling using four different heuristic approaches. Comput Electron Agric 140:203–213. https://doi.org/10.1016/j.compag.2017.05.036
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Keshtegar, B., Heddam, S., Sebbar, A. et al. SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation. Environ Sci Pollut Res 26, 35807–35826 (2019). https://doi.org/10.1007/s11356-019-06596-8
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DOI: https://doi.org/10.1007/s11356-019-06596-8