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Modeling reference evapotranspiration using three different heuristic regression approaches

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  • Kisi, Ozgur

Abstract

Modeling reference evapotranspiration (ET0) is important in reservoir management, planning regional water resources and evaluation of drinking-water supplies. The study investigates the ability of three different heuristic regression approaches, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5Tree) in modeling ET0. The first part of the study focused on testing the accuracy of the LSSVR, MARS and M5Tree models in estimating the ET0 data of Antalya and Isparta stations located in Mediterranean Region of Turkey. Cross-validation method was utilized in the applications. The LSSVR models were observed to be better than the MARS and M5Tree models in estimating ET0 of Antalya and Isparta stations with local input and output data. The accuracy of the applied methods was investigated in estimation of ET0 using air temperature, solar radiation, relative humidity and wind speed inputs from nearby station in the second part of the study (cross-station application without local input data). The results showed that the MARS models provided better accuracy than the LSSVR and M5Tree models with respect to SI, mean absolute error (MAE) and determination coefficient (R2). In the third part of the study, the accuracy of the applied models was investigated in ET0 estimation using input and output data from nearby station. The results showed that the M5Tree models outperformed the other models with respect to SI, MAE and R2. The overall results showed that the LSSVR could be successfully used in estimating ET0 by using local input and output data. In case of without local inputs, however, the MARS model performed better than the LSSVR and M5Tree models while the M5Tree was observed to be the best alternative for estimating ET0 in the absence of local input and output data.

Suggested Citation

  • Kisi, Ozgur, 2016. "Modeling reference evapotranspiration using three different heuristic regression approaches," Agricultural Water Management, Elsevier, vol. 169(C), pages 162-172.
    • Handle: RePEc:eee:agiwat:v:169:y:2016:i:c:p:162-172
    DOI: 10.1016/j.agwat.2016.02.026
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    7. Malik, Anurag & Jamei, Mehdi & Ali, Mumtaz & Prasad, Ramendra & Karbasi, Masoud & Yaseen, Zaher Mundher, 2022. "Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection," Agricultural Water Management, Elsevier, vol. 272(C).
    8. 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.
    9. Mahmoudi, Neda & Majidi, Arash & Jamei, Mehdi & Jalali, Mohammadnabi & Maroufpoor, Saman & Shiri, Jalal & Yaseen, Zaher Mundher, 2022. "Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation," Agricultural Water Management, Elsevier, vol. 261(C).
    10. Feng, Yu & Cui, Ningbo & Gong, Daozhi & Zhang, Qingwen & Zhao, Lu, 2017. "Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling," Agricultural Water Management, Elsevier, vol. 193(C), pages 163-173.
    11. Wu, Lifeng & Peng, Youwen & Fan, Junliang & Wang, Yicheng & Huang, Guomin, 2021. "A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation," Agricultural Water Management, Elsevier, vol. 245(C).
    12. Feng, Yu & Jia, Yue & Cui, Ningbo & Zhao, Lu & Li, Chen & Gong, Daozhi, 2017. "Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China," Agricultural Water Management, Elsevier, vol. 181(C), pages 1-9.
    13. Tianao Wu & Wei Zhang & Xiyun Jiao & Weihua Guo & Yousef Alhaj Hamoud, 2020. "Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    14. Valiantzas, John D., 2018. "Temperature-and humidity-based simplified Penman’s ET0 formulae. Comparisons with temperature-based Hargreaves-Samani and other methodologies," Agricultural Water Management, Elsevier, vol. 208(C), pages 326-334.
    15. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.
    16. Yamaç, Sevim Seda & Todorovic, Mladen, 2020. "Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data," Agricultural Water Management, Elsevier, vol. 228(C).
    17. Rana Muhammad Adnan & Salim Heddam & Zaher Mundher Yaseen & Shamsuddin Shahid & Ozgur Kisi & Binquan Li, 2020. "Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches," Sustainability, MDPI, vol. 13(1), pages 1-21, December.
    18. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).

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    More about this item

    Keywords

    Reference evapotranspiration; Heuristic regression approaches; Least square support vector regression; Multivariate adaptive regression splines; M5 model tree; Modeling;
    All these keywords.

    JEL classification:

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

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