Daily reference evapotranspiration prediction for irrigation scheduling decisions based on the hybrid PSO-LSTM model

PLoS One. 2023 Apr 18;18(4):e0281478. doi: 10.1371/journal.pone.0281478. eCollection 2023.


The shortage of available water resources and climate change are major factors affecting agricultural irrigation. In order to improve the irrigation water use efficiency, it is necessary to predict the water requirements for crops in advance. Reference evapotranspiration (ETo) is a hypothetical standard reference crop evapotranspiration, many types of artificial intelligence models have been applied to predict ETo; However, there are still few in the literature regarding the application of hybrid models for deep learning model parameters optimization. This paper proposes two hybrid models based on particle swarm optimization (PSO) and long-short-term memory (LSTM) neural network, used to predict ETo at the four climate stations, Shaanxi province, China. These two hybrid models were trained using 40 years of historical data, and the PSO was used to optimize the hyperparameters in the LSTM network. We applied the optimized model to predict the daily ETo in 2019 under different datasets, the result showed that the optimized model has good prediction accuracy. The optimized hybrid models can help farmers and irrigation planners to make plan earlier and precisely, and can provide valuable information to improve tasks such as irrigation planning.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agricultural Irrigation
  • Artificial Intelligence*
  • Neural Networks, Computer*
  • Water
  • Water Resources


  • Water

Grants and funding

This work was supported in part by the National Key Research and Development Project of the 13th five-year plan fertilizer-water source-equipment adaptation technology and control equipment (No.2017YFD0201504)(http://www.most.gov.cn/index.html), by the Key R&D Program of Shaanxi Province (2023-YBNY-202), by Ningbo Science and Technology Plan Project (2021S022), by the Zhejiang Province Basic Public welfare Research Program (LGN20F030001), and by the Key Industrial Innovation Chain Projects of Shaaxi Province (2023-ZDLNY-67).