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

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  • Ali Rahimikhoob

    (University of Tehran)

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.

Suggested Citation

  • Ali Rahimikhoob, 2016. "Comparison of M5 Model Tree and Artificial Neural Network’s Methodologies in Modelling Daily Reference Evapotranspiration from NOAA Satellite Images," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3063-3075, July.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:9:d:10.1007_s11269-016-1331-9
    DOI: 10.1007/s11269-016-1331-9
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    References listed on IDEAS

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    1. Slavisa Trajkovic & Srdjan Kolakovic, 2009. "Evaluation of Reference Evapotranspiration Equations Under Humid Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(14), pages 3057-3067, November.
    2. Krishna Bhartiya & Aniruddha Ghare, 2014. "Relative humidity based model for estimation of reference evapotranspiration for western plateau and hills region of India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 3355-3364, August.
    3. Giorgos Papadavid & Diofantos Hadjimitsis & Leonidas Toulios & Silas Michaelides, 2013. "A Modified SEBAL Modeling Approach for Estimating Crop Evapotranspiration in Semi-arid Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3493-3506, July.
    4. Ozgur Kisi & Taner Cengiz, 2013. "Fuzzy Genetic Approach for Estimating Reference Evapotranspiration of Turkey: Mediterranean Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3541-3553, August.
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    Cited by:

    1. Jian Yin & Chesheng Zhan & Wen Ye, 2016. "An Experimental Study on Evapotranspiration Data Assimilation Based on the Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5263-5279, November.
    2. Dilip Kumar Roy & Tapash Kumar Sarkar & Sujit Kumar Biswas & Bithin Datta, 2023. "Generalized Daily Reference Evapotranspiration Models Based on a Hybrid Optimization Algorithm Tuned Fuzzy Tree Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 193-218, January.
    3. Mohammad Taghi Sattari & Halit Apaydin & Shahaboddin Shamshirband, 2020. "Performance Evaluation of Deep Learning-Based Gated Recurrent Units (GRUs) and Tree-Based Models for Estimating ETo by Using Limited Meteorological Variables," Mathematics, MDPI, vol. 8(6), pages 1-18, June.
    4. Khalil Ghorbani & Meysam Salarijazi & Nozar Ghahreman, 2022. "Developing Stepwise m5 Tree Model to Determine the Influential Factors on Rainfall Prediction and to Overcome the Greedy Problem of its Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3327-3348, July.
    5. Roohollah Noori & Hossien Sheikhian & Farhad Hooshyaripor & Ali Naghikhani & Jan Franklin Adamowski & Behzad Ghiasi, 2017. "Granular Computing for Prediction of Scour Below Spillways," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 313-326, January.
    6. Wang, Sheng & Lian, Jinjiao & Peng, Yuzhong & Hu, Baoqing & Chen, Hongsong, 2019. "Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China," Agricultural Water Management, Elsevier, vol. 221(C), pages 220-230.
    7. Behrooz Keshtegar & Ozgur Kisi & Hamed Ghohani Arab & Mohammad Zounemat-Kermani, 2018. "Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1101-1116, February.
    8. Mohammad Rezaie-Balf & Zahra Zahmatkesh & Sungwon Kim, 2017. "Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3843-3865, September.

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    More about this item

    Keywords

    Reference evapotranspiration; AVHRR data; Cold pixel; M5 model tree; ANN model;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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