Abstract
The precise estimation of reference crop evapotranspiration (ETO) is vital for regional and irrigation water resource management. It is also beneficial to the rational allocation of regional water resources and alleviates the disparity between water supply and demand. This study accurately estimates the ETO of 14 meteorological stations in southern China. Five neural network models (extreme learning machine [ELM], back-propagation neural network [BP], ant colony optimization [ACO]-BP, bird swarm algorithm [BSA]-BP, and cat swarm optimization [CSO]-BP) were introduced to predict ETO with limited factors using different methods. The results demonstrated that models involving T (average, maximum, and minimum air temperature), sunshine duration (n), and relative humidity (RH) exhibited the highest accuracy of all studied combinations; the role of T, n, RH, wind speed (U2) and average atmospheric pressure (AP) regarding ETO gradually decreased. These three biological heuristic algorithms (ACO, BSA, and CSO) each significantly enhanced the capability of the BP model. The accuracy and computational cost of the CSO-BP model are better than those built by other algorithms. Therefore, it is strongly recommended to use the CSO-BP model for ETO estimation in southern China. This result serves as a reference for a more accurate estimation of ETO for future irrigation decision-making and water resource management in southern China.
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Acknowledgements
Our research would not have been possible without the climate database provided by the China Meteorological Administration.
Funding
This work was supported by National Natural Science Foundation of China (51922072, 51779161, 51009101), Key R&D and Promotion Projects in Henan Province (Science and Technology Development) (No. 222102110452 and No. 2023–1836), the Fundamental Research Funds for the Central Universities (2019CDPZH-10), PhD research start up the foundation of Henan University of Science and Technology (No. 13480025 and No. 13480033) and Key Scientific Research Projects of Colleges and Universities in Henan Province(22B416002).
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H.Z: Validation, L.X: Methodology and Writing, L.Z: Conceptualization and Writing, N.C: Conceptualization, S.C: Data curation, X.Z: Data curation, Y.S: Methodology, Y.W: Methodology and Writing, Z.L: Data curation.
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Zhao, L., Xing, L., Wang, Y. et al. Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors. Water Resour Manage 37, 1207–1222 (2023). https://doi.org/10.1007/s11269-022-03423-7
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DOI: https://doi.org/10.1007/s11269-022-03423-7