IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v244y2021ics0378377420321417.html
   My bibliography  Save this article

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

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377420321417
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2020.106594?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. 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.
    2. Torres, Alfonso F. & Walker, Wynn R. & McKee, Mac, 2011. "Forecasting daily potential evapotranspiration using machine learning and limited climatic data," Agricultural Water Management, Elsevier, vol. 98(4), pages 553-562, February.
    3. Matin Ahooghalandari & Mehdi Khiadani & Mina Esmi Jahromi, 2016. "Developing Equations for Estimating Reference Evapotranspiration in Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3815-3828, September.
    4. Sentelhas, Paulo C. & Gillespie, Terry J. & Santos, Eduardo A., 2010. "Evaluation of FAO Penman-Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada," Agricultural Water Management, Elsevier, vol. 97(5), pages 635-644, May.
    5. Jamshidi, Sajad & Zand-Parsa, Shahrokh & Kamgar-Haghighi, Ali Akbar & Shahsavar, Ali Reza & Niyogi, Dev, 2020. "Evapotranspiration, crop coefficients, and physiological responses of citrus trees in semi-arid climatic conditions," Agricultural Water Management, Elsevier, vol. 227(C).
    6. Althoff, Daniel & Filgueiras, Roberto & Dias, Santos Henrique Brant & Rodrigues, Lineu Neiva, 2019. "Impact of sum-of-hourly and daily timesteps in the computations of reference evapotranspiration across the Brazilian territory," Agricultural Water Management, Elsevier, vol. 226(C).
    7. 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).
    8. 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.
    9. Zhang, Lei & Traore, Seydou & Cui, Yuanlai & Luo, Yufeng & Zhu, Ge & Liu, Bo & Fipps, Guy & Karthikeyan, R. & Singh, Vijay, 2019. "Assessment of spatiotemporal variability of reference evapotranspiration and controlling climate factors over decades in China using geospatial techniques," Agricultural Water Management, Elsevier, vol. 213(C), pages 499-511.
    10. Shiri, Jalal, 2017. "Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran," Agricultural Water Management, Elsevier, vol. 188(C), pages 101-114.
    11. 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.
    12. Negm, Amro & Minacapilli, Mario & Provenzano, Giuseppe, 2018. "Downscaling of American National Aeronautics and Space Administration (NASA) daily air temperature in Sicily, Italy, and effects on crop reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 209(C), pages 151-162.
    13. Fan, Junliang & Wu, Lifeng & Ma, Xin & Zhou, Hanmi & Zhang, Fucang, 2020. "Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions," Renewable Energy, Elsevier, vol. 145(C), pages 2034-2045.
    14. Liu, Xiaoying & Xu, Chunying & Zhong, Xiuli & Li, Yuzhong & Yuan, Xiaohuan & Cao, Jingfeng, 2017. "Comparison of 16 models for reference crop evapotranspiration against weighing lysimeter measurement," Agricultural Water Management, Elsevier, vol. 184(C), pages 145-155.
    15. Fan, Junliang & Zheng, Jing & Wu, Lifeng & Zhang, Fucang, 2021. "Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models," Agricultural Water Management, Elsevier, vol. 245(C).
    16. 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).
    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. 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).
    2. 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).
    3. 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.
    4. 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).
    5. 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).
    6. Zheng, Jing & Fan, Junliang & Zhang, Fucang & Wu, Lifeng & Zou, Yufeng & Zhuang, Qianlai, 2021. "Estimation of rainfed maize transpiration under various mulching methods using modified Jarvis-Stewart model and hybrid support vector machine model with whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 249(C).
    7. 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).
    8. 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).
    9. 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).
    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.
    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. 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).
    13. Chia, Min Yan & Huang, Yuk Feng & Koo, Chai Hoon, 2022. "Resolving data-hungry nature of machine learning reference evapotranspiration estimating models using inter-model ensembles with various data management schemes," Agricultural Water Management, Elsevier, vol. 261(C).

    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. 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).
    2. 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).
    3. Xiang, Keyu & Li, Yi & Horton, Robert & Feng, Hao, 2020. "Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review," Agricultural Water Management, Elsevier, vol. 232(C).
    4. 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).
    5. 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).
    6. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    7. 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).
    8. Xing, Liwen & Cui, Ningbo & Liu, Chunwei & Zhao, Lu & Guo, Li & Du, Taisheng & Zhan, Cun & Wu, Zongjun & Wen, Shenglin & Jiang, Shouzheng, 2022. "Estimation of daily apple tree transpiration in the Loess Plateau region of China using deep learning models," Agricultural Water Management, Elsevier, vol. 273(C).
    9. 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).
    10. Mattar, Mohamed A., 2018. "Using gene expression programming in monthly reference evapotranspiration modeling: A case study in Egypt," Agricultural Water Management, Elsevier, vol. 198(C), pages 28-38.
    11. Ali Mokhtar & Nadhir Al-Ansari & Wessam El-Ssawy & Renata Graf & Pouya Aghelpour & Hongming He & Salma M. Hafez & Mohamed Abuarab, 2023. "Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1557-1580, March.
    12. Zheng, Jing & Fan, Junliang & Zhang, Fucang & Wu, Lifeng & Zou, Yufeng & Zhuang, Qianlai, 2021. "Estimation of rainfed maize transpiration under various mulching methods using modified Jarvis-Stewart model and hybrid support vector machine model with whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 249(C).
    13. 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).
    14. 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).
    15. 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).
    16. Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
    17. 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).
    18. 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).
    19. Granata, Francesco & Di Nunno, Fabio, 2021. "Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks," Agricultural Water Management, Elsevier, vol. 255(C).
    20. Phon Sheng Hou & Lokman Mohd Fadzil & Selvakumar Manickam & Mahmood A. Al-Shareeda, 2023. "Vector Autoregression Model-Based Forecasting of Reference Evapotranspiration in Malaysia," Sustainability, MDPI, vol. 15(4), pages 1-18, February.

    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:244:y:2021:i:c:s0378377420321417. 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.