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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

Author

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  • Yan, Shicheng
  • Wu, Lifeng
  • Fan, Junliang
  • Zhang, Fucang
  • Zou, Yufeng
  • Wu, You

Abstract

The information of reference evapotranspiration (ET0) is vital for optimizing irrigation scheduling, planning water resources and assessing hydrological drought. However, accurate estimation of ET0 is difficult if long-term or complete climatic variables are unavailable, especially in developing countries like China. This study proposed a novel hybrid extreme gradient boosting (XGB) model with the whale optimization algorithm (WOA) to estimate daily ET0 at four stations in the arid region and four stations in the humid region of China. Particularly, its performances were evaluated under the local and three external scenarios with seven incomplete combinations of maximum and minimum temperatures (Tmax and Tmin), relative humidity (RH), wind speed (U2), relative sunshine duration (n/N) and extra-terrestrial radiation (Ra) for the period 1966–2015. The results showed that U2 was the most influencing variable for daily ET0 estimation in the arid region, followed by n/N and RH, while n/N was more important than RH and U2 in the humid region. Locally trained and tested WOA-XGB models greatly outperformed their corresponding simplified FAO-56 PM models, with the average decrease in root mean square error (RMSE) by 40.1% and 38.9% in the arid and humid regions, respectively. Compared with local WOA-XGB models, the prediction accuracy of externally trained WOA-XGB models with local or external testing data decreased by 18.1% or 69.9% in the arid region, and 16.8% or 67.9% in the humid region, respectively. However, external WOA-XGB models with synthetic testing data from the target and adjacent stations overall improved the prediction accuracy of local WOA-XGB models by 5.7% and 9.6% in the arid and humid regions, respectively. The results indicated that external WOA-XGB models with local testing data produced acceptable daily ET0 estimates. However, when synthetic data were employed during testing, external WOA-XGB models gave excellent daily ET0 estimates, which were comparable to or even better than local WOA-XGB models. This is a promising strategy that allows more accurate estimation of daily ET0 when lack of long-term historical or complete recent data.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:244:y:2021:i:c:s0378377420321417
    DOI: 10.1016/j.agwat.2020.106594
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    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. He, Bohao & Jia, Biying & Zhao, Yanghe & Wang, Xu & Wei, Mao & Dietzel, Ranae, 2022. "Estimate soil moisture of maize by combining support vector machine and chaotic whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 267(C).
    7. 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).
    8. 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).
    9. 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.
    10. Xiwen Cui & Shaojun E & Dongxiao Niu & Bosong Chen & Jiaqi Feng, 2021. "Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm," Sustainability, MDPI, vol. 13(21), pages 1-18, November.

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