Sensitivity analysis of meteorological data in estimating reference evapotranspiration with the minimum data using wavelet-neuro-fuzzy, ANN and ANFIS models

Subject Areas : Farm water management with the aim of improving irrigation management indicators

ahmadreza karimipour ¹ , Golnoosh Banitalebi ²

1 - Payam Noor University,
2 - Ph.D. student, Isfahan University of Technology

Received: 2019-07-30 Accepted : 2020-05-11 Published : 2020-05-21

Keywords: ANFIS, Sensitivity analysis, Fuzzy-neuro-wavelet model, FAO-Penman-Monteith,

Abstract :

The aim of this study was to estimate the ET0 in a moderately cold semi-humid climate in a 22-year statistical period by applying a wavelet-neuro-fuzzy model with a minimum number of input parameters.The results were compared with the ANN and ANFIS models to evaluate the performance of the wavelet-neuro-fuzzy model, The sensitivity analysis of the input parameters was done in three ways: Hill method, coefficient of determination, and StatSoft. Sensitivity analysis showed that temperature (T), Rs, Ra, mean daily wind speed at 2 meters (U2) and Rn were an effective parameter.Based on the results of the sensitivity analysis, six combinations with these parameters were selected.The results indicate that the wavelet-neural-fuzzy model has a better performance than the artificial neural network model. The results also showed that the estimated ET0 value with three inputs parameters of maximum and minimum temperature and solar radiation using fuzzy-neural-wavelet model was more accurate than the neural network. Based on the coefficient of determination and the amount of calculated error for the artificial neural network and the Anfis, use of the combination of 7 input parameters (Ra, Rn, Rs, U2, Tmean, Tmin and Tmax) and four meteorological input parameters (Ra, U2, Tmean and Tmax) lead to more accurate estimates of ET0 in comparison to the FAO Penman-Monteith method. The results also showed that the highest amount of explanatory factor and the lowest error value among the different wavelets used in the fuzzy-neuro-wavelet model were for the 7 and three input parameters (Tmax, Tmin, Rs), respectively.

References:

شیرانی، ح. 1396 . شبکه­های عصبی مصنوعی با قابلیت کاربرد در کشاورزی و علوم طبیعی. انتشارات دانشگاه ولیعصر رفسنجان. 320 صفحه.

Adamala, S. 2019. Nonlinear Evapotranspiration Modeling Using Artificial Neural Networks. In Advanced Evapotranspiration Methods and Applications. IntechOpen. pp: 1-24.

Adamala, S. Raghuwanshi, N.S. Mishra, A. and Singh, R. 2019. Generalized wavelet neural networks for evapotranspiration modeling in India. ISH Journal of Hydraulic Engineering. 25(2): 119-131.‏

Allawi, M.F. Binti Othman, F. Afan, H.A. Ahmed, A.N. Hossain, M. Fai, C.M. and El-Shafie, A. 2019. Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods. Water. 11(6). 1226.

Alves, W.B. Rolim, G.D.S. and Aparecido, L.E.D.O. 2017. Reference evapotranspiration forecasting by artificial neural networks. Engenharia Agrícola. 37 (6): 1116-1125.‏

Arca, B. Beniscasa, F. and Vincenzi, M. 2001. Evaluation of neural network techniques for estimating evapotranspiration. Evolving Solution with Neural Networks. 62-97.‏

Benzaghta, M.A. Mohammed, T. A. Ghazali, A.H. and Soom, M.A. M. 2012. Prediction of evaporation in tropical climate using artificial neural network and climate-based models. Scientific Research and Essays. 7(36): 3133-3148.

Ghorbani, A.M. Kazempour, R. Chau, K.W. Shamshirband, S. and Taherei Ghazvinei, P. 2018. Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: A case study in Talesh, Northern Iran. Engineering Applications of Computational Fluid Mechanics. 12 (1): 724-737.

Goyal, M. K., Bharti, B., Quilty, J., Adamowski, J., and Pandey, A. 2014. Modeling of daily pan evaporation in sub-tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert systems with applications, 41(11): 5267-5276.

Hamraz, B.S. Akbarpour, A. and Pour Reza bilandi, M. 2016. Sensitivity analysis in groundwater model. Aquifer and aqueduct Journal. 1(1): 50-60.

Hill, M.C. and Tiedeman, C.R. 2006. Effective groundwater model calibration: with analysis of data, sensitivities, predictions, and uncertainty. John Wiley and Sons.

Hornik, K. Stinchcombe, M. and White, H. 1989. Multilayer feed forward networks are

universal approximations. Neural Network. 2: 359–366.

Jain, S.K. Nayak, P.C. and Sudheer, K. P. 2008. Models for estimating evapotranspiration using artificial neural networks and their physical interpretation. Hydrology Process. 22: 2225-2234.

Kisi, O. 2006. Daily pan evaporation modeling using a neuro-fuzzy computing technique. Journal of Hydrology. 329 (3-4): 636-646.

Kumar, M. Bandyopadhyay, A. Raghuwanshi, N.S. and Singh, R. 2008. Comparative study of conventional and artificial neural network-based ET0 estimation models. Irrigation Science. 26(6): 531.

Laaboudi, A., Mouhouche, B. and Draoui, B. 2012. Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions. International journal of biometeorology, 56(5): 831-841.

Odhiambo, L.O. Yoder, R.E. and Yoder, D.C. 2001. Estimation of reference crop evapotranspiration using fuzzy state models. Transactions of the ASAE. 44(3): 543-550.

Pianosi, F. Beven, K. Freer, J. Hall, J.W. Rougier, J. Stephenson, D.B. and Wagener, T. 2016. Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modeling and Software. 79: 214-232.

Qasem, S.N. Samadianfard, S. Kheshtgar, S. Jarhan, S. Kisi, O. Shamshirband, S. and Chau, K.W. 2019. Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics. 13(1): 177-187.‏

Razavi, S. and Gupta, H.V. 2015. What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in Earth and Environmental systems models. Water Resources Research. 51(5): 3070-3092.

Riahi-modavar, H. and Ayoubzadeh, S.A. 2008. Estimation of Longitudinal Dispersion Coefficient Using Comparative Neuro-Fuzzy Inference System. Water and Wastewater Journal. 67: 34-46.

SAMMEN, S.S. 2013. Forecasting of evaporation from Hemren reservoir by using artificial neural networks. Diyala Journal of Engineering Sciences. 6(4): 38-53.

Terzi, O. and Keskin, M.E. 2010. Comparison of artificial neural networks and empirical equations to estimate daily pan evaporation. Irrigation and drainage. 59(2): 215-225.

Trajkovic, S. and Kolakovic, S. 2009. Estimating reference evapotranspiration using limited weather data. Journal of irrigation and drainage engineering. 135(4): 443-449.

Valipour, M. 2017. Analysis of potential evapotranspiration using limited weather data. Applied Water Science. 7(1): 187-197

Zanetti, S.S. Sousa, E.F. Oliveira, V.P. Almeida, F. T. and Bernardo, S. 2007. Estimating evapotranspiration using artificial neural network and minimum climatological data. Journal of Irrigation and Drainage Engineering. 133(2): 83-89.