Skip to main content
SpringerLink
Log in

Comparison of M5 Model Tree and Artificial Neural Network’s Methodologies in Modelling Daily Reference Evapotranspiration from NOAA Satellite Images

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

The objective of this study was to compare feed-forward artificial neural network (ANN) and M5 model tree for estimating reference evapotranspiration (ET0) only on the basis of the remote sensing based surface temperature (Ts) data. The input variables for these models were the daytime surface temperature at the cold pixel obtained from the AVHRR/NOAA sensor and extraterrestrial radiation (Ra). The study has been carried out in five irrigated units that cultivate sugar cane, which located in the Khuzestan plain in the southwest of Iran. A total of 663 images of NOAA–AVHRR level 1b during the period 1999–2009, covering the area of this study were collected from the Satellite Active Archive of NOAA. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two above approaches. The study demonstrated that modelling of ET0 through the use of M5 model tree gave better estimates than the ANN technique. However, differences with the ANN model are small. Root mean square error and R2 for the comparison between reference and estimated ET0 for the tested data set using the proposed M5 model are 13.7 % and 0.96, respectively. For the ANN model these values are 14.3 % and 0.95, respectively.

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

Similar content being viewed by others

References

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration. Guidelines for computing crop water requirements. In: FAO irrigation and drainage paper, no 56. FAO, Roma, Italy

  • Allen RG, Tasumi M, Trezza R (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) – model. J Irrig Drain Eng 133(4):380–394

    Article  Google Scholar 

  • Bastiaanssen WGM, Menenti M, Feddes RA, Holtslang AA (1998) A remote sensing surface energy balance algorithm for land (SEBAL): 1. Formulation. J Hydrol 212–213:198–212

    Article  Google Scholar 

  • Bhartiya KM, Ghare AD (2014) Relative humidity based model for estimation of reference evapotranspiration for western plateau and hills region of India. Water Resour Manag 28(10):3355–3364

    Article  Google Scholar 

  • Bhattacharya B, Solomatine DP (2005) Neural networks and M5 model trees in modeling water level–discharge relationship. Neurocomputing 63:381–396

    Article  Google Scholar 

  • Caparrini F, Castelli F, Entekhabi D (2004) Estimation of surface turbulent fluxes through assimilation of radiometric surface temperature sequences. J Hydrometeorol 5(1):145–159

    Article  Google Scholar 

  • Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62(3):241–252

    Article  Google Scholar 

  • Cleugh HA, Leuning R, Qiaozhen M, Running SW (2007) Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sens Environ 106:285–304

    Article  Google Scholar 

  • Crow WT, Kustas WP (2005) Utility of assimilating surface radiometric temperature observations for evaporative fraction and heat transfer coefficient retrieval. Bound-Layer Meteorol 115:105–130

    Article  Google Scholar 

  • De Vries DA, Birch JW (1961) The modification of climate near the ground by irrigation for pastures on the riverine plain. Aust J Agr Res 12(2):260–272

    Article  Google Scholar 

  • Debnath S, Adamala S, Raghuwanshi NS (2015) Sensitivity analysis of FAO-56 Penman-Monteith method for different agro-ecological regions of India. Environ Process 2:689–704

    Article  Google Scholar 

  • French AN, Jacob F, Anderson MC, Kustas WP, Timmermans W, Gieske A, Su Z, Su H, McCabe MF, Li F, Prueger J, Brunsell N (2005) Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote Sens Environ 99:55–65

    Article  Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  • Kisi O, Cengiz TM (2013) Fuzzy genetic approach for estimating reference evapotranspiration of Turkey: Mediterranean region. Water Resour Manag 27(10):3541–3553

    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 

  • Maeda EE, Wiberg DA, Pellikka PK (2011) Estimating reference evapotranspiration using remote sensing and empirical models in a region with limited ground data availability in Kenya. Appl Geogr 31:251–258

    Article  Google Scholar 

  • Norman JM, Anderson MC, Kustas WP, French AN, Mecikalski J, Torn R, Diak GR, Schmugge TJ, Tanner BCW (2003) Remote sensing of surface energy fluxes at 101-m pixel resolutions. Water Resour Res 39(8):1221. doi:10.1029/2002WR001775

    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

    Article  Google Scholar 

  • Pal M, Deswal S (2009) M5 model tree based modelling of reference evapotranspiration. Hydrol Process 23:1437–1443

    Article  Google Scholar 

  • Papadavid G, Hadjimitsis DG, Toulios L, Michaelides S (2013) A modified SEBAL modeling approach for estimating crop evapotranspiration in semi-arid conditions. Water Resour Manag 27(9):3493–3506

    Article  Google Scholar 

  • Quinlan JR (1992) Learning with continuous classes. In Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence, Hobart, Australia, 16–18 November, World Scientific, Singapore: 343–348

  • Sobrino JA, Raissouni N (2000) Toward remote sensing methods for land cover dynamic monitoring, application to Morocco. Int J Remote Sens 21(2):353–366

    Article  Google Scholar 

  • Solomatine DP, Xue Y (2004) M5 model trees compared to neural networks: application to flood forecasting in the upper reach of the Huai River in China. J Hydrol Eng 9(6):491–501

    Article  Google Scholar 

  • Su Z (2002) The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol Earth Syst Sci 6:85–100

    Article  Google Scholar 

  • Trajkovic S, Kolakovic S (2009) Evaluation of reference evapotranspiration equations under humid conditions. Water Resour Manag 23:3057–3067

    Article  Google Scholar 

  • Ulivieri C, Castronouvo MM, Francioni R, Cardillo A (1994) A split-window algorithm for estimating land surface temperature from satellites. Adv Space Res 14(3):59–65

    Article  Google Scholar 

  • Valor E, Caselles V (1996) Mapping land surface emissivity from NDVI: application to European, African and South American areas. Remote Sens Environ 57(3):167–184

    Article  Google Scholar 

  • Wang Y, Witten IH (1997) Induction of model trees for predicting continuous lasses. In: Proceedings of the Poster Papers of the European Conference on Machine Learning. University of Economics, Faculty of Informatics and Statistics, Prague

Download references

Acknowledgments

This study is the partial work under Project No. 88-01-02-054 supported by Khuzestan Water and Power Authority and was done in the Department of Irrigation and Drainage Engineering, Abouraihan Campus, University of Tehran.

Author information

Authors and Affiliations

  1. Department of Irrigation and Drainage Engineering College of Aburaihan, University of Tehran, Pakdasht, Iran

    Ali Rahimikhoob

Authors
  1. Ali Rahimikhoob
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Rahimikhoob.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahimikhoob, A. Comparison of M5 Model Tree and Artificial Neural Network’s Methodologies in Modelling Daily Reference Evapotranspiration from NOAA Satellite Images. Water Resour Manage 30, 3063–3075 (2016). https://doi.org/10.1007/s11269-016-1331-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-016-1331-9

Keywords

Search

Navigation