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Multi-lead ahead prediction model of reference evapotranspiration utilizing ANN with ensemble procedure

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Abstract

Obtaining an accurate estimate of the reference evapotranspiration (ETo) can be difficult, especially when there is insufficient data to utilize the Penman–Monteith method. Artificial intelligence–based methods may provide reliable prediction models for several applications in engineering. However, time-series prediction based on artificial neural network (ANN) learning algorithms is fundamentally problematic. For example, the ANN model can experience over-fitting during training and, in consequence, lose its generalization. In this research, several over-fitting procedures have been augmented with the classical ANN model, are proposed. This model was applied to the prediction of the daily ETo at Rasht city, located in the north part of Iran, by using the minimum and maximum daily temperature of the region collected from 1975–1988. In addition, three different scenarios have been developed in order to achieve better prediction accuracy. The results showed that the proposed ENN model successfully predicted the daily ETo with a significant level of accuracy using only the maximum and minimum temperatures. The model also outperformed the classical ANN method. In addition, the proposed ENN compared with Hargreaves and Samani (Appl Eng Agric 1:96–99, 1985) (HGS) model and showed the ENN provides more accurate prediction for ETo. Furthermore, the proposed model could provide relatively good level of accuracy when examined for multi-lead predictions, which could not be afford by HGS model.

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Abbreviations

ANN:

Artificial neural networks

R2 :

Correlation coefficient

ENN:

Ensemble neural network

ETo :

Reference evapotranspiration

FFBP:

Feed-forward back-propagation

HGS:

Hargreaves and Samani

IN:

Iteration number

LVC:

Low value criteria

MARE:

Mean absolute relative error

MAE:

Mean absolute error

MSE:

Mean square error

PVC:

Peak value criteria

PM:

Penman–Monteith

Rs :

Solar radiation

Ra :

Extraterrestrial radiation

Tmin :

Minimum temperature

Tmean :

Mean temperature

Tmax :

Maximum temperature

References

  • Adeli H, Hung SL (1995) Machine learning—neural networks, genetic algorithms, and fuzzy systems. Wiley, New York

    Google Scholar 

  • Adeli H, Vishnubhotla PR (1992) Parallel processing and parallel machines. In: Adeli H (ed) Parallel processing in computational mechanics. Marcel Dekker, New York, pp 1–20

    Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. FAO irrigation and drainage paper 56, Food and Agricultural Organization of the United Nations (FAO), Rome. Artificial Neural Network. J Irrig Drain Eng 129(6):454–457

  • Awchi TA (2008) Application of radial basis function neural networks for reference evapotranspiration prediction. Al-Rafi-dain Eng 16(1):117–129

  • Birkinshaw SJ, Parkin G, Rao Z (2008) A hybrid neural networks and numerical models approach for predicting groundwater abstraction impacts. J Hydroinformatics 10(2):127–137

    Google Scholar 

  • Bishop CM (1996) Neural networks for pattern recognition, 1st edn. Oxford University Press, Oxford

  • Boto-Giralda D, Díaz-Pernas FJ, González-Ortega D, Díez-Higuera JF, Antón-Rodríguez M, Martínez-Zarzuela M, Torre-Díez I (2010) Wavelet-based denoising for traffic volume time series forecasting with self-organizing neural networks. Comput Aided Civil Infrastruct Eng 25(7):530–545

    Article  Google Scholar 

  • Box GH, Jenkins G (1970) Time series analysis: forecasting and control. San Francisco, Holden-Day

  • Bulygina N, Gupta H (2010) How Bayesian data assimilation can be used to estimate the mathematical structure of a model. Stoch Environ Res Risk Assess 24:925–937

    Article  Google Scholar 

  • Chattopadhyay S, Chattopadhyay G (2008) Identification of the best hidden layer size for three layered neural net in predicting monsoon rainfall in India. J Hydroinformatics 10(2):181–188

    Google Scholar 

  • Chauhan S, Shrivastava RK (2008) Performance evaluation of reference evapotranspiration estimation using climate based methods and artificial neural networks. Water Resour Manag 23:825–837

    Article  Google Scholar 

  • Chowdhury S, Sharma A (2009) Multisite seasonal forecast of arid river flows using a dynamic model combination approach. Water Resour Res 45(10): art. no. W10428

  • Clair TA, Ehrman JM (1998) Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers. Water Resour Res 34(3):447–455

    Article  CAS  Google Scholar 

  • Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3–4):244–257

    Google Scholar 

  • Dibike BY, Coulibaly P (2008) TDNN with logical values for hydrologic modeling in a cold and snowy climate. J Hydroinformatics 10(4):289–300

    Google Scholar 

  • Drucker H, Cortes C, Jackel D (1994) Boosting and other ensemble methods. Neural Comput 6:1289–1301

    Google Scholar 

  • El-Shafie A, Abdin AE, Noureldin A, Taha MR (2009) Enhancing inflow forecasting model at Aswan high dam utilizing radial basis neural network and upstream monitoring stations measurements. Water Resour Manag 23(11):2289–2315

    Google Scholar 

  • El-Shafie AH, El-Shafie A, El Mazoghi HG, Shehata A, Taha MR (2011) Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt. Int J Phys Sci 6(6):1306–1316

    Google Scholar 

  • El-Shafie A, Jaafer O, Seyed A (2011) Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia. Int J Phys Sci 6(12):2875-2888

    Google Scholar 

  • Elshorbagy A, Simonovic SP, Panu US (2000) Performance evaluation of artificial neural networks for runoff prediction. J Hydrol Eng 5(4):424–427

    Article  Google Scholar 

  • Fernando DA, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng 3(3):203–209

    Google Scholar 

  • Fooladmand HR (2011) Evaluation of some equations for estimating evapotranspiration in the south of Iran. Arch Agron Soil Sci 57(7):741–752

    Article  Google Scholar 

  • Gao G, Chong-Yu X, Chen D, Singh VP (2012) Spatial and temporal characteristics of actual evapotranspiration over Haihe River basin in China. Stoch Environ Res Risk Assess 26:655–669

    Article  Google Scholar 

  • Gibson GJ, Cowan CFN (1990) On the decision regions of multilayer perceptrons. Proc IEEE 78(10):1590–1594

    Google Scholar 

  • Giustolisi O, Laucelli D (2005) Improving generalization of artificial neural networks in rainfall–runoff modelling / Amélioration de la généralisation de réseaux de neurones artificiels pour la modélisation pluiedébit. Hydrol Sci J 50(3):457

    Google Scholar 

  • Giustolisi O, Simeone V (2006) Optimal design of artificial neural networks by a multi-objective strategy: groundwater level predictions. Hydrol Sci J 51(3):502–523

    Google Scholar 

  • Graf W, Freitag S, Kaliske M, Sickert J-U (2010) Recurrent neural networks for uncertain time-dependent structural behavior. Comput Aided Civil Infrastruct Eng 25(5):322–323

    Article  Google Scholar 

  • Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1(2):96–99

    Google Scholar 

  • Haykin S (1994) Neural networks: A comprehensive foundation. Macmillan College Publishing Company, New York

  • Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517–2530

    Google Scholar 

  • IPCC 2007 (2009) Summary for policymakers. In: Solomon S, Qin D, Jain M, Das A, Srivastava DK (eds) Climate change 2007. Application of ANN for reservoir inflow prediction and operation. J Water Resour Plan Manag 125(5):263–271

    Google Scholar 

  • Jacquin AP, Shamseldin AY (2009) Review of the application of fuzzy inference systems in river flow forecasting. J Hydroinformatics 11(3–4):202–210

    Google Scholar 

  • Jain SK, Nayak PC, Sudheer KP (2008) Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrol Process 22(13):2225–2234

    Google Scholar 

  • Johnson F, Sharma A (2010) A comparison of australian open water body evaporation trends for current and future climates estimated from class a evaporation pans and general circulation models. J Hydrometeorol 11(1):105–121

    Article  Google Scholar 

  • Khajehzadeh M, Taha MR, El-Shafie A, Eslami M (2011) Modified particle swarm optimization for optimum design of spread footing and retaining wall. J Zhejiang U:Science A 12(6):415-427

    Google Scholar 

  • Khoob AR (2008) Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment. Irrig Sci 26(3):253–259

    Article  Google Scholar 

  • Kisi O (2009) Modeling monthly evaporation using two different neural computing techniques. Irrig Sci 27:417–430

    Article  Google Scholar 

  • Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128(4):224–233

    Article  Google Scholar 

  • Liong SY, Khu ST, Chan WT (2001) Derivation of Pareto front with genetic algorithm and neural network. J Hydrol Eng 6(1):52–61

    Google Scholar 

  • Maier HR, Dandy GC (1999) Empirical comparison of various methods for training feed-forward neural networks for salinity forecasting. Water Resour Res 35(8):2591–2596

    Article  Google Scholar 

  • Noureldin A, El-Shafie A, Bayoumi M (2011) GPS/INS integration utilizing dynamic neural networks for vehicular navigation. Inf Fusion 12(1):48–57

    Article  Google Scholar 

  • Odhiambo LO, Yoder RE, Hines JW (2001) Optimization of fuzzy evapotranspiration model through neural training with input-output examples. Trans ASAE 44(6):1625–1633

    Google Scholar 

  • Rahimi Khoob A, Behbahani MB, Fakheri J (2012) An evaluation of four reference evapotranspiration models in a subtropical climate. Water Resour Manag 26(10):2867–2881

    Article  Google Scholar 

  • Reuter U, Möller B (2010) Artificial neural networks for forecasting of fuzzy time series. Comput Aided Civil Infrastruct Eng 25(5):363–374

    Article  Google Scholar 

  • Ripley BD (1996) Pattern recognition and neural networks, Cambridge University Press, New York

  • Royuela MP, Manzano J, Palau G (2008) Improvement of temperature based ANN models for ETo prediction in coastal locations by means of preliminary models and exogenous data. In: 2008 eighth international conference on hybrid intelligent systems, pp 344-349

  • Sabziparvar AA, Tabari H (2010) Regional estimation of reference evapotranspiration in arid and semi-arid regions. J Irrig Drain Eng ASCE 136(10):724–731

    Article  Google Scholar 

  • Samant A, Adeli H (2000) Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis. Comput Aided Civil Infrastruct Eng 15(4):241–250

    Article  Google Scholar 

  • Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng 129(3):214–218

    Article  Google Scholar 

  • Tabari H (2010) Evaluation of reference crop evapotranspiration equations in various climates. Water Resour Manag 24:2311–2337

    Article  Google Scholar 

  • Tabari H, Grismer ME, Trajkovic S (2011) Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrig Sci. doi:10.1007/s00271-011-0295

    Google Scholar 

  • Tabari H, Martinez C, Ezani A, Hosseinzadeh Talaee P (2012) Applicability of support vector machines and adaptive neurofuzzy inference system for potato crop evapotranspiration forecasting. Irrig Sci. doi:10.1007/s00271-012-0332-6

    Google Scholar 

  • Telesca L, Vicente-Serrano SM, Lo′pez-Moreno JI (2012) Power spectral characteristics of drought indices in the Ebro river basin at different temporal scales. Stoch Environ Res Risk Assess. doi:10.1007/s00477-012-0651-4

  • Todorovic B, Stankovic M, Todorovic-Zakula S (2000) Structurally adaptive RBF network in nonstationary time series prediction. In: Proceedings of adaptive systems for signal processing, communication and control symposium, IEEE, New York, pp 224–229

  • Tokar AS, Johnson PA (1999) Rainfall-runoff modeling using artificial neural networks. J Hydrol Eng 4(3):232–239

    Google Scholar 

  • Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting of reference evapotranspiration by artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone. Agric Water Manag 97(5):707–714

    Google Scholar 

  • Tsanis IK, Coulibaly P, Daliakopoulos IN (2008) Improving groundwater level forecasting with a feed forward neural network and linearly regressed projected precipitation. J Hydroinformatics 10(4):317–330

    Google Scholar 

  • Wang YM, Kerh T (2008) Neural network approach for estimating reference evapotranspiration from limited climatic data in Burkina Faso. WSEAS Trans Comput 7(6):704–713

    Google Scholar 

  • Yang T, Chen X, Xu Chong-Yu, Zhang Z-C (2009) Spatio-temporal changes of hydrological processes and underlying driving forces in Guizhou region, Southwest China. Stoch Environ Res Risk Assess 23:1071–1087

    Article  Google Scholar 

  • Zanetti SS, Sousa EF, Oliveira VPS, Almeida FT, Bernardo S (2007) Estimating evapotranspiration using artificial neural network and minimum climatological data. J Irrig Drain Eng 133(2):83–89

    Google Scholar 

  • Zamani A, Heemink A, Solomatine D (2009) Wave height prediction at the Caspian Sea using a data driven model and ensemble-based data assimilation methods. J Hydroinformatics 11(2):154–164

    Google Scholar 

  • Zhang Q, Chong-Yu X, Chen YD, Ren L (2011) Comparison of evapotranspiration variations between the Yellow River and Pearl River basin, China. Stoch Environ Res Risk Assess 25:139–150

    Article  Google Scholar 

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Acknowledgments

This research is supported by a research grants to the first author by Smart Engineering System, and Water and Environmental Research Group, University Kebangsaan Malaysia; UKM-GUP-2011-034, UKM-DLP-2011-002 and FRGS/1/2012/TK03/UKM/02/4.

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Authors and Affiliations

  1. Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor Darul Ehsan, Malaysia

    Ahmed El-Shafie, Humod Mosad Alsulami & Ali Najah

  2. Infrastructure Department, Faculty of Engineering, University of Melbourne, Parkville, VIC, Australia

    Heerbod Jahanbani

  3. Ministry of Higher Education, Kingdom of Saudi Arabia, Riyadh, 11153, Saudi Arabia

    Humod Mosad Alsulami

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  1. Ahmed El-Shafie
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  2. Humod Mosad Alsulami
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  3. Heerbod Jahanbani
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  4. Ali Najah
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Correspondence to Ahmed El-Shafie.

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El-Shafie, A., Alsulami, H.M., Jahanbani, H. et al. Multi-lead ahead prediction model of reference evapotranspiration utilizing ANN with ensemble procedure. Stoch Environ Res Risk Assess 27, 1423–1440 (2013). https://doi.org/10.1007/s00477-012-0678-6

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