Skip to main content

Advertisement

SpringerLink
Log in

Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

In recent years, a significant evolution of forecasting methods has been possible due to advances in artificial computational intelligence. The achievement of the optimal architecture of an ANN is a complex process. Thus, in this work, an Evolutionary Robotic (study of the evolution of an ANN using Genetic Algorithm) approach has been used to obtain an Artificial Neuro-Genetic Networks (ANGN) to the short-term forecasting of daily irrigation water demand that maximizes the accuracy of the predictions. The methodology is applied in the Bembézar Irrigation District (Southern Spain). An optimal ANGN architecture (ANGN (7, 29, 16, 1)) has achieved obtaining a Standard Error Prediction (SEP) value of the daily water demand of 12.63 % and explaining 93 % of the total variance observed during validation process. The developed model proved to be a powerful tool that, without long dataset and time requirements, can be very useful for the development of management strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Abrahart RJ, See L (2000) Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrol Process 14:2157–2172

    Article  Google Scholar 

  • Abrahart RJ, See L (2002) Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments. Hydrol Earth Syst Sci 6(4):655–670

    Article  Google Scholar 

  • Abrahart RJ, See L, Kneale PE (1999) Using pruning algorithms and genetic algorithms to optimize network architectures and forecasting inputs in a neural network rainfall–runoff model. J Hydroinf 1(2):103–114

    Google Scholar 

  • Agarwal A, Mishra SK, Ram S, Singh JK (2006) Simulation of runoff and sediment yield using artificial neural networks. Biosyst Eng 94(4):597–613

    Article  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, FAO, Rome

  • Anctil F, Rat A (2005) Evaluation of neural network stream flow forecasting on 47 watersheds. J Hydrol Eng 10(1):85–88

    Article  Google Scholar 

  • Battiti R (1992) First and second order methods for learning: between steepest descent and Newton’s method. Neural Comput 4(2):141–166

    Article  Google Scholar 

  • Brent RP (1973) Algorithms for minimization without derivatives. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Cameron D, Kneale P, See L (2002) An evaluation of a traditional and a neural net modelling approach to flood forecasting for an upland catchment. Hydrol Process 16:1033–1046

    Article  Google Scholar 

  • Charalambous C (1992) Conjugate gradient algorithm for efficient training of artificial neural networks. IEEE Proc 139(3):301–310

    Article  Google Scholar 

  • Chiang YM, Chang LC, Chang FJ (2004) Comparison of static feed forward and dynamic-feedback ward neural networks for rainfall–runoff modeling. J Hydrol 290:297–311

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2)

  • Demuth HB, Beale MH, Hagan MT (2009) Neural network toolbox TM 6. User’s guide. The MathWorks Inc., Natick

    Google Scholar 

  • Dennis JE, Schnabel RB (1983) Numerical methods for unconstrained optimization and nonlinear equations. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Doorenbos J, Kassam AH (1979) Yield response to water. FAO Irrigation and Drainage Paper 33, FAO, Rome

  • Doorenbos J, Pruitt WO (1977) Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper 24, FAO, Rome

  • Fletcher R, Reeves CM (1964) Function minimization by conjugate gradients. Comput J 7:149–154

    Article  Google Scholar 

  • French MN, Krajewski WF, Cuykendall RR (1992) Rainfall forecasting in space and time using a neural network. J Hydrol 137:1–31

    Article  Google Scholar 

  • Hagan MT, Menhaj M (1994) Training feed-forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993

    Article  Google Scholar 

  • Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing, Boston

    Google Scholar 

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

    Article  Google Scholar 

  • Instituto Nacional de Estadística (INE) (2014) Encuesta sobre el uso del agua en el sector agrario (año 2012). Nota de prensa. Madrid. Spain. Available via http://www.ine.es/jaxi/menu.do?type=pcaxis&path=%2Ft26%2Fp067%2Fp01&file=inebase&L=0. Accessed 2015

  • Kuligowski RJ, Barros AP (1998) Experiments in short-term precipitation forecasting using artificial neural networks. Mon Weather Rev 126(2):470–482

    Article  Google Scholar 

  • Lorrai M, Sechi GM (1995) Neural nets for modelling rainfall– runoff transformations. Water Resour Manag 9:299–313

    Article  Google Scholar 

  • Mason JC, Temme A, Price RK (1996) A neural network model of rainfall–runoff using radial basis functions. J Hydraul Res 34(4):537–548

    Article  Google Scholar 

  • Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533

    Article  Google Scholar 

  • Moradkhani H, Hsu K, Gupta HV, Sorooshian S (2004) Improved stream flow forecasting using self-organizing radial basis function artificial neural networks. J Hydrol 295:246–262

    Article  Google Scholar 

  • Nayebi M, Khalili D, Amin S, Zand-Parsa S (2006) Daily stream flow prediction capability of artificial neural networks as influenced by minimum air temperature data. Biosyst Eng 95(4):557–567

    Article  Google Scholar 

  • Ondimu S, Murase H (2007) Reservoir level forecasting using neural networks: lake Naivasha. Biosyst Eng 96(1):135–138

    Article  Google Scholar 

  • Playan E, Mateos L (2006) Modernization and optimization of irrigation systems to increase water productivity. Agric Water Manag 80:100–116

    Article  Google Scholar 

  • Poff LN, Tokar AS, Johnson PA (1996) Stream hydrological and ecological responses to climate change assessed with an artificial neural network. Limnol Oceanogr 41(5):857–863

    Article  Google Scholar 

  • Powell MJD (1977) Restart procedures for the conjugate gradient method. Math Program 12:241–254

    Article  Google Scholar 

  • Pratap R (2010) Getting started with Matlab. A quick introduction for scientist and engineers. Oxford University Press, USA

    Google Scholar 

  • Pulido-Calvo I, Gutiérrez-Estrada JC (2009) Improved irrigation water demand forecasting using a soft-computing hybrid model. Biosyst Eng 102(1):202–218

    Article  Google Scholar 

  • Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: The RPROP algorithm. Proc IEEE Int Conf Neural Netw

  • Roger LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour Res 30(2):457–481

    Article  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back propagation errors. Nature 323(9):533–536

    Article  Google Scholar 

  • Scales LE (1985) Introduction to non-linear optimization. Springer, New York

    Google Scholar 

  • See L, Openshaw S (2000) A hybrid multi-model approach to river level forecasting. Hydrol Sci J 45(4):523–536

    Article  Google Scholar 

  • Shin HS, Salas JD (2000) Regional drought analysis based on neural networks. J Hydrol Eng 5(2):145–155

    Article  Google Scholar 

  • Spanish Ministry of Agriculture, Food and Environment (MAGRAMA) (2013) Boletín Hidrológico Semanal 1. Madrid. Spain (in Spanish). Available via:http://www.magrama.gob.es/es/agua/temas/evaluacion-de-los-recursos-hidricos/boletin-hidrologico/

  • Thirumalaiah K, Deo MC (1998) River stage forecasting using artificial neural networks. J Hydrol Eng 3(1):26–32

    Article  Google Scholar 

  • Thirumalaiah K, Deo MC (2000) Hydrological forecasting using neural networks. J Hydrol Eng 5(2):180–189

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Tokar AS, Markus M (2000) Precipitation–runoff modeling using artificial neural networks and conceptual models. J Hydrol Eng 5(2):156–161

    Article  Google Scholar 

  • Van Aelst PV, Ragab RA, Feyen J, Raes D (1988) Improving irrigation management by modelling the irrigation schedule. Agric Water Manag 13:113–125

    Article  Google Scholar 

  • Ventura S, Silva M, Pérez-Bendito D, Hervás C (1995) Artificial neural networks for estimation of kinetic analytical parameters. Anal Chem 67(9):1521–1525

    Article  Google Scholar 

  • Yang CC, Prasher SO, Lacroix R, Sreekanth S, Patni NK, Masse L (1997) Artificial neural network model for subsurface-drained farmland. J Irrig Drain Eng 123(4):285–292

    Article  Google Scholar 

  • Zhang M, Fulcher J, Scofield RA (1997) Rainfall estimation using artificial neural network group. Neurocomputing 16:97–115

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by an FPU grant (Formación de Profesorado Universitario) from the Spanish Ministry of Education, Culture and Sports to Rafael González Perea. This work is part of the TEMAER project (AGL2014-59747-C2-2-R), funded by the Spanish Ministry of Economy and Competitiveness.

Author information

Authors and Affiliations

  1. Department of Agronomy, University of Córdoba, Campus Rabanales Edif.da Vinci, 14071, Córdoba, Spain

    R. González Perea, P. Montesinos & J. A. Rodríguez Díaz

  2. Department of Rural Engineering, University of Córdoba, Campus Rabanales, Edif. da Vinci, 14071, Córdoba, Spain

    E. Camacho Poyato

Authors
  1. R. González Perea
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Camacho Poyato
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Montesinos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. A. Rodríguez Díaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. González Perea.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Perea, R.G., Poyato, E.C., Montesinos, P. et al. Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks. Water Resour Manage 29, 5551–5567 (2015). https://doi.org/10.1007/s11269-015-1134-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-015-1134-4

Keywords

Search

Navigation