IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v274y2022ics0378377422005029.html
   My bibliography  Save this article

A regional machine learning method to outperform temperature-based reference evapotranspiration estimations in Southern Spain

Author

Listed:
  • Bellido-Jiménez, Juan A.
  • Estévez, Javier
  • García-Marín, Amanda P.

Abstract

Accurate estimations of reference evapotranspiration (ET0) are crucial for determining crop water requirements and designing an adequate irrigation scheduling to optimize the use of water. In this work, a new clustering method to outperform the accuracy of ET0 estimations only using temperature variables has been developed and assessed, based on the multifractal properties of the Diurnal Temperature Range (DTR). Thus, a more accurate weather stations’ grouping method has been evaluated, regardless of their geographic location. All the datasets were collected from 89 automated weather stations in the period 2000–2018 and pooled into two main regions (1 and 2). In each region, an iterative procedure has been carried out: 1) selection of all the stations except the candidate one for the training procedure and 2) test procedure using the candidate station. The results showed that Machine Learning models (ML) highly outperformed Hargreaves-Samani (HS) in most of the cases, being Multilayer Perceptron (MLP) the most accurate over Extreme Learning Machine models (ELM). On average, the results obtained by MLP using the best configuration in the first region were better than those obtained in the second region. Specifically, the first region got an Root Mean Square Error (RMSE) = 0.657 mm/d, Nash–Sutcliffe Efficiency (NSE) = 0.897, Coefficient of Determination (R2) = 0.931 and Mean Bias Error (MBE) = |0.04|mm/d while the second region obtained an RMSE = 0.703 mm/d, NSE = 0.867, R2 = 0.897 and MBE = |0.045|mm/d. Regarding the seasonal performance, spring and autumn obtained the best NSE and R2 results, whereas winter carried out the lowest RMSE values. This study provides a new and more accurate methodology to improve ET0 estimations on a regional basis and only using temperature data in the whole process.

Suggested Citation

  • Bellido-Jiménez, Juan A. & Estévez, Javier & García-Marín, Amanda P., 2022. "A regional machine learning method to outperform temperature-based reference evapotranspiration estimations in Southern Spain," Agricultural Water Management, Elsevier, vol. 274(C).
    • Handle: RePEc:eee:agiwat:v:274:y:2022:i:c:s0378377422005029
    DOI: 10.1016/j.agwat.2022.107955
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422005029
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2022.107955?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yassin, Mohamed A. & Alazba, A.A. & Mattar, Mohamed A., 2016. "Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate," Agricultural Water Management, Elsevier, vol. 163(C), pages 110-124.
    2. 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).
    3. Yin, Juan & Deng, Zhen & Ines, Amor V.M. & Wu, Junbin & Rasu, Eeswaran, 2020. "Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)," Agricultural Water Management, Elsevier, vol. 242(C).
    4. Yan, Shicheng & Wu, Lifeng & Fan, Junliang & Zhang, Fucang & Zou, Yufeng & Wu, You, 2021. "A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: Applications in arid and humid regions of China," Agricultural Water Management, Elsevier, vol. 244(C).
    5. Ferreira, Lucas Borges & da Cunha, Fernando França, 2020. "New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning," Agricultural Water Management, Elsevier, vol. 234(C).
    6. Luo, Yufeng & Chang, Xiaomin & Peng, Shizhang & Khan, Shahbaz & Wang, Weiguang & Zheng, Qiang & Cai, Xueliang, 2014. "Short-term forecasting of daily reference evapotranspiration using the Hargreaves–Samani model and temperature forecasts," Agricultural Water Management, Elsevier, vol. 136(C), pages 42-51.
    7. 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.
    8. Bellido-Jiménez, Juan Antonio & Estévez Gualda, Javier & García-Marín, Amanda Penélope, 2021. "Assessing new intra-daily temperature-based machine learning models to outperform solar radiation predictions in different conditions," Applied Energy, Elsevier, vol. 298(C).
    9. Gavilan, P. & Lorite, I.J. & Tornero, S. & Berengena, J., 2006. "Regional calibration of Hargreaves equation for estimating reference ET in a semiarid environment," Agricultural Water Management, Elsevier, vol. 81(3), pages 257-281, March.
    10. Estévez, J. & García-Marín, A.P & Morábito, J.A & Cavagnaro, M., 2016. "Quality assurance procedures for validating meteorological input variables of reference evapotranspiration in mendoza province (Argentina)," Agricultural Water Management, Elsevier, vol. 172(C), pages 96-109.
    11. Paredes, P. & Pereira, L.S., 2019. "Computing FAO56 reference grass evapotranspiration PM-ETo from temperature with focus on solar radiation," Agricultural Water Management, Elsevier, vol. 215(C), pages 86-102.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shih-Lun Fang & Yi-Shan Lin & Sheng-Chih Chang & Yi-Lung Chang & Bing-Yun Tsai & Bo-Jein Kuo, 2024. "Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables," Agriculture, MDPI, vol. 14(4), pages 1-20, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Lei & Zhao, Xin & Zhu, Ge & He, Jun & Chen, Jian & Chen, Zhicheng & Traore, Seydou & Liu, Junguo & Singh, Vijay P., 2023. "Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China," Agricultural Water Management, Elsevier, vol. 289(C).
    2. 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).
    3. Bellido-Jiménez, Juan Antonio & Estévez, Javier & García-Marín, Amanda Penélope, 2021. "New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain," Agricultural Water Management, Elsevier, vol. 245(C).
    4. Dong, Juan & Xing, Liwen & Cui, Ningbo & Zhao, Lu & Guo, Li & Wang, Zhihui & Du, Taisheng & Tan, Mingdong & Gong, Daozhi, 2024. "Estimating reference crop evapotranspiration using improved convolutional bidirectional long short-term memory network by multi-head attention mechanism in the four climatic zones of China," Agricultural Water Management, Elsevier, vol. 292(C).
    5. Mohammadi, Babak & Mehdizadeh, Saeid, 2020. "Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 237(C).
    6. 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).
    7. Hadeel E. Khairan & Salah L. Zubaidi & Mustafa Al-Mukhtar & Anmar Dulaimi & Hussein Al-Bugharbee & Furat A. Al-Faraj & Hussein Mohammed Ridha, 2023. "Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting," Sustainability, MDPI, vol. 15(19), pages 1-19, September.
    8. Roy, Dilip Kumar & Lal, Alvin & Sarker, Khokan Kumer & Saha, Kowshik Kumar & Datta, Bithin, 2021. "Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system," Agricultural Water Management, Elsevier, vol. 255(C).
    9. Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).
    10. 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.
    11. Zhou, Hanmi & Ma, Linshuang & Niu, Xiaoli & Xiang, Youzhen & Chen, Jiageng & Su, Yumin & Li, Jichen & Lu, Sibo & Chen, Cheng & Wu, Qi, 2024. "A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain," Agricultural Water Management, Elsevier, vol. 296(C).
    12. Qiu, Rangjian & Luo, Yufeng & Wu, Jingwei & Zhang, Baozhong & Liu, Zhihe & Agathokleous, Evgenios & Yang, Xiumei & Hu, Wei & Clothier, Brent, 2023. "Short–term forecasting of daily evapotranspiration from rice using a modified Priestley–Taylor model and public weather forecasts," Agricultural Water Management, Elsevier, vol. 277(C).
    13. Paredes, Paula & Trigo, Isabel & de Bruin, Henk & Simões, Nuno & Pereira, Luis S., 2021. "Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products," Agricultural Water Management, Elsevier, vol. 248(C).
    14. Yang, Yang & Luo, Yufeng & Wu, Conglin & Zheng, Hezhen & Zhang, Lei & Cui, Yuanlai & Sun, Ningning & Wang, Li, 2019. "Evaluation of six equations for daily reference evapotranspiration estimating using public weather forecast message for different climate regions across China," Agricultural Water Management, Elsevier, vol. 222(C), pages 386-399.
    15. Paredes, P. & Pereira, L.S. & Almorox, J. & Darouich, H., 2020. "Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables," Agricultural Water Management, Elsevier, vol. 240(C).
    16. Nouri, Milad & Homaee, Mehdi, 2022. "Reference crop evapotranspiration for data-sparse regions using reanalysis products," Agricultural Water Management, Elsevier, vol. 262(C).
    17. Fan, Junliang & Ma, Xin & Wu, Lifeng & Zhang, Fucang & Yu, Xiang & Zeng, Wenzhi, 2019. "Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data," Agricultural Water Management, Elsevier, vol. 225(C).
    18. Valipour, Mohammad & Khoshkam, Helaleh & Bateni, Sayed M. & Jun, Changhyun & Band, Shahab S., 2023. "Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States," Agricultural Water Management, Elsevier, vol. 283(C).
    19. Zhang, Q. & Li, Y.P. & Huang, G.H. & Wang, H. & Li, Y.F. & Shen, Z.Y., 2024. "Multivariate time series convolutional neural networks for long-term agricultural drought prediction under global warming," Agricultural Water Management, Elsevier, vol. 292(C).
    20. Chia, Min Yan & Huang, Yuk Feng & Koo, Chai Hoon, 2021. "Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine," Agricultural Water Management, Elsevier, vol. 243(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:274:y:2022:i:c:s0378377422005029. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.