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A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation

Author

Listed:
  • Wu, Lifeng
  • Peng, Youwen
  • Fan, Junliang
  • Wang, Yicheng
  • Huang, Guomin

Abstract

Accurate and fast estimation of reference evapotranspiration (ET0) is important in determining crop water requirements, designing irrigation schedule, planning and managing agricultural water resources, especially when limited meteorological data are available. This study proposed a novel kernel extreme learning machine model coupled with the K-means clustering and firefly algorithms (Kmeans-FFA-KELM) with 5, 10, 15, 20, 25, 30 and 40 data subsets for estimating monthly mean daily ET0 in parallel computation in the Poyang Lake basin of South China with pooled temperature data from 26 weather stations. Two input combinations, i.e. (1) mean temperature (Tavg) and extraterrestrial radiation (Ra), (2) maximum and minimum temperatures (Tmax and Tmin) and Ra, were considered. Meteorological data during 1966–2000 were used to train the models, while those for the period 2001–2015 were used for model testing. The results showed that the prediction accuracy of selected machine learning models with Tmax, Tmin and Ra was improved by 7.0–15.5% in terms of RMSE compared to that with Tavg and Ra during testing. The FFA-KELM model slightly outperformed the adaptive network based fuzzy inference system (ANFIS) model, both of which were superior to the random forest (RF) and M5 prime model tree (M5P) models, followed by the Hargreaves and Thornthwaite models. The RMSE values of Kmeans-FFA-KELM models with more than 20 subsets were decreased by 0.7–3.5% compared with those of the FFA-KELM models. The Kmeans-FFA-KELM model with 25 subsets (FFA-KELM-25) outperformed the FFA-KELM model in summer and in the count of absolute errors greater than 0.9 mm d−1. The computational time of Kmeans-FFA-KELM models first decreased and then increased with the increase of the subset number. The parallel FFA-KELM-25 model (0.5–0.7 s) significantly reduced the computational time, which was 10–13 times faster than the sequential Kmeans-FFA-KELM model (7.0–7.4 s), and 1185–1603 times faster than the FFA-KELM model (802.2–830.0 s). This study provides a new and fast modeling method for processing large datasets in agricultural and water resources studies on a regional scale.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:245:y:2021:i:c:s0378377420321715
    DOI: 10.1016/j.agwat.2020.106624
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    References listed on IDEAS

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    3. 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).
    4. 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).
    5. 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).
    6. 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).
    7. Ying Wang & Jianzhou Wang & Hongmin Li & Hufang Yang & Zhiwu Li, 2022. "Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1483-1511, November.
    8. 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).

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