IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v236y2020ics0378377420304029.html
   My bibliography  Save this article

Daily reference evapotranspiration prediction of Tieguanyin tea plants based on mathematical morphology clustering and improved generalized regression neural network

Author

Listed:
  • Ruiming, Fang
  • Shijie, Song

Abstract

Tieguanyin tea plant is the most important tea cultivar in Fujian Province, China. It has suffered great economic losses due to high temperature and dry weather in recently years. This study proposed a prediction model for daily reference evapotranspiration (ET0) of Tieguanyin based on the combination of mathematical morphology clustering (MMC) and generalized regression neural networks (GRNN). Average air temperature, sunshine hours and relative humidity were chosen as the input factors of GRNN after a correlation analysis of the microclimate factors of the tea garden. The MMC was adopted to cluster the historical meteorological data to find the similar class as the training dataset of GRNN. The fruit fly optimization algorithm (FOA) was used to optimize the smoothing factor of GRNN. The meteorological data from JAN. 2018 to OCT. 2019 measured in Dabaofeng tea garden, Anxi County, Fujian Province, China were used to train and test the proposed model, and the performances was assessed with RMSE, MAE and model validity coefficient. The prediction results of different seasons show that the proposed model is efficient with high accuracy and has good adaptability under complex meteorological conditions.

Suggested Citation

  • Ruiming, Fang & Shijie, Song, 2020. "Daily reference evapotranspiration prediction of Tieguanyin tea plants based on mathematical morphology clustering and improved generalized regression neural network," Agricultural Water Management, Elsevier, vol. 236(C).
  • Handle: RePEc:eee:agiwat:v:236:y:2020:i:c:s0378377420304029
    DOI: 10.1016/j.agwat.2020.106177
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377420304029
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2020.106177?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. Ahmed El-Shafie & Ali Najah & Humod Alsulami & Heerbod Jahanbani, 2014. "Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 947-967, March.
    2. Paul D. McNicholas, 2016. "Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 331-373, October.
    3. Tao, Hai & Diop, Lamine & Bodian, Ansoumana & Djaman, Koffi & Ndiaye, Papa Malick & Yaseen, Zaher Mundher, 2018. "Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso," Agricultural Water Management, Elsevier, vol. 208(C), pages 140-151.
    4. Deng, Xi-Ping & Shan, Lun & Zhang, Heping & Turner, Neil C., 2006. "Improving agricultural water use efficiency in arid and semiarid areas of China," Agricultural Water Management, Elsevier, vol. 80(1-3), pages 23-40, February.
    5. Chen, X.H. & Zhuang, C.G. & He, Y.F. & Wang, L. & Han, G.Q. & Chen, C. & He, H.Q., 2010. "Photosynthesis, yield, and chemical composition of Tieguanyin tea plants (Camellia sinensis (L.) O. Kuntze) in response to irrigation treatments," Agricultural Water Management, Elsevier, vol. 97(3), pages 419-425, March.
    6. Ding, Yimin & Wang, Weiguang & Song, Ruiming & Shao, Quanxi & Jiao, Xiyun & Xing, Wanqiu, 2017. "Modeling spatial and temporal variability of the impact of climate change on rice irrigation water requirements in the middle and lower reaches of the Yangtze River, China," Agricultural Water Management, Elsevier, vol. 193(C), pages 89-101.
    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. Bouman, B. A. M. & Tuong, T. P., 2001. "Field water management to save water and increase its productivity in irrigated lowland rice," Agricultural Water Management, Elsevier, vol. 49(1), pages 11-30, July.
    9. Minhas, P.S. & Ramos, Tiago B. & Ben-Gal, Alon & Pereira, Luis S., 2020. "Coping with salinity in irrigated agriculture: Crop evapotranspiration and water management issues," Agricultural Water Management, Elsevier, vol. 227(C).
    10. Bendu, Harisankar & Deepak, B.B.V.L. & Murugan, S., 2017. "Multi-objective optimization of ethanol fuelled HCCI engine performance using hybrid GRNN–PSO," Applied Energy, Elsevier, vol. 187(C), pages 601-611.
    11. Pelosi, A. & Medina, H. & Villani, P. & D’Urso, G. & Chirico, G.B., 2016. "Probabilistic forecasting of reference evapotranspiration with a limited area ensemble prediction system," Agricultural Water Management, Elsevier, vol. 178(C), pages 106-118.
    12. Yang, Yang & Cui, Yuanlai & Luo, Yufeng & Lyu, Xinwei & Traore, Seydou & Khan, Shahbaz & Wang, Weiguang, 2016. "Short-term forecasting of daily reference evapotranspiration using the Penman-Monteith model and public weather forecasts," Agricultural Water Management, Elsevier, vol. 177(C), pages 329-339.
    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. 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).
    2. 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.

    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. Cao, Jingjing & Tan, Junwei & Cui, Yuanlai & Luo, Yufeng, 2019. "Irrigation scheduling of paddy rice using short-term weather forecast data," Agricultural Water Management, Elsevier, vol. 213(C), pages 714-723.
    2. 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).
    3. 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).
    4. Longo-Minnolo, G. & Vanella, D. & Consoli, S. & Intrigliolo, D.S. & Ramírez-Cuesta, J.M., 2020. "Integrating forecast meteorological data into the ArcDualKc model for estimating spatially distributed evapotranspiration rates of a citrus orchard," Agricultural Water Management, Elsevier, vol. 231(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. Luo, Wanqi & Chen, Mengting & Kang, Yinhong & Li, Wenping & Li, Dan & Cui, Yuanlai & Khan, Shahbaz & Luo, Yufeng, 2022. "Analysis of crop water requirements and irrigation demands for rice: Implications for increasing effective rainfall," Agricultural Water Management, Elsevier, vol. 260(C).
    7. 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).
    8. Liu, Yi & Hu, Yue & Wei, Chenchen & Zeng, Wenzhi & Huang, Jiesheng & Ao, Chang, 2024. "Synergistic regulation of irrigation and drainage based on crop salt tolerance and leaching threshold," Agricultural Water Management, Elsevier, vol. 292(C).
    9. Chen, Baoqing & Liu, Enke & Mei, Xurong & Yan, Changrong & Garré, Sarah, 2018. "Modelling soil water dynamic in rain-fed spring maize field with plastic mulching," Agricultural Water Management, Elsevier, vol. 198(C), pages 19-27.
    10. Zhang, Kang & Xie, Xianhong & Zhu, Bowen & Meng, Shanshan & Yao, Yi, 2019. "Unexpected groundwater recovery with decreasing agricultural irrigation in the Yellow River Basin," Agricultural Water Management, Elsevier, vol. 213(C), pages 858-867.
    11. Yang, Yang & Cui, Yuanlai & Bai, Kaihua & Luo, Tongyuan & Dai, Junfeng & Wang, Weiguang & Luo, Yufeng, 2019. "Short-term forecasting of daily reference evapotranspiration using the reduced-set Penman-Monteith model and public weather forecasts," Agricultural Water Management, Elsevier, vol. 211(C), pages 70-80.
    12. Wu, Zhangsheng & Li, Yue & Wang, Rong & Xu, Xu & Ren, Dongyang & Huang, Quanzhong & Xiong, Yunwu & Huang, Guanhua, 2023. "Evaluation of irrigation water saving and salinity control practices of maize and sunflower in the upper Yellow River basin with an agro-hydrological model based method," Agricultural Water Management, Elsevier, vol. 278(C).
    13. Brinkhoff, James & Houborg, Rasmus & Dunn, Brian W., 2022. "Rice ponding date detection in Australia using Sentinel-2 and Planet Fusion imagery," Agricultural Water Management, Elsevier, vol. 273(C).
    14. Mohammed Seyam & Faridah Othman & Ahmed El-Shafie, 2017. "RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 187-204, January.
    15. Yang, Danni & Li, Sien & Kang, Shaozhong & Du, Taisheng & Guo, Ping & Mao, Xiaomin & Tong, Ling & Hao, Xinmei & Ding, Risheng & Niu, Jun, 2020. "Effect of drip irrigation on wheat evapotranspiration, soil evaporation and transpiration in Northwest China," Agricultural Water Management, Elsevier, vol. 232(C).
    16. Wang, Linlin & Li, Qiang & Coulter, Jeffrey A. & Xie, Junhong & Luo, Zhuzhu & Zhang, Renzhi & Deng, Xiping & Li, Linglin, 2020. "Winter wheat yield and water use efficiency response to organic fertilization in northern China: A meta-analysis," Agricultural Water Management, Elsevier, vol. 229(C).
    17. Tao, Hai & Diop, Lamine & Bodian, Ansoumana & Djaman, Koffi & Ndiaye, Papa Malick & Yaseen, Zaher Mundher, 2018. "Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso," Agricultural Water Management, Elsevier, vol. 208(C), pages 140-151.
    18. Kriti Poudel & Ram Hari Timilsina & Anish Bhattarai, 2020. "Effect Of Crop Establishment Methods On Yield Of Spring Rice At Khairahani, Chitwan, Nepal," Big Data In Agriculture (BDA), Zibeline International Publishing, vol. 3(1), pages 6-11, November.
    19. Manel Ben Hassen & Federica Monaco & Arianna Facchi & Marco Romani & Giampiero Valè & Guido Sali, 2017. "Economic Performance of Traditional and Modern Rice Varieties under Different Water Management Systems," Sustainability, MDPI, vol. 9(3), pages 1-10, February.
    20. Shrestha, N.K. & Shukla, S., 2014. "Basal crop coefficients for vine and erect crops with plastic mulch in a sub-tropical region," Agricultural Water Management, Elsevier, vol. 143(C), pages 29-37.

    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:236:y:2020:i:c:s0378377420304029. 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.