The development of a hybrid model to forecast paddy water temperature as an alert system for high‐temperature damage

Xie, Wenpeng; Kimura, Masaomi; Asada, Yohei; Iida, Toshiaki; Kubo, Naritaka

doi:10.1002/ird.2692

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…The framework comprises two main components, a physical process model and a neural network, as shown in Figure 1. Xie et al (2021Xie et al ( , 2022 developed a two-layer heat balance model for calculating physical processes for paddy field analysis. It simulates the heat transfer in the paddy field and computes the water temperature by calculating the net inflow of heat to the paddy water while accounting for the influence of the vegetation layer on the temperature.…”

Section: Framework Outlinementioning

confidence: 99%

Interpretable Framework of Physics‐Guided Neural Network With Attention Mechanism: Simulating Paddy Field Water Temperature Variations

Xie

Kimura

Takaki

et al. 2022

Water Resources Research

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With the development of large‐scale rice cultivation management initiatives in East Asia, there is concern that a reduction in the number of human cultivators per unit area may lead to poor water management, which could result in decreased land productivity, owing to abnormally high‐ and low‐temperature damage to crops. Accurate simulation of paddy field water temperature is important for studying its impact on crops and providing timely information to aid in decision‐making for more efficient management under limited resources. We propose a neural‐network framework that considers the heat transfer by the vegetation canopy and applies physical‐theory constraints in its training. A novel tuning method is proposed to cope with the trade‐off between water temperature accuracy and physical consistency during training to ensure that the calculated water temperature variations in a paddy field enjoy high accuracy and physical consistency. In the experiments, the proposed framework outperforms physical process models and pure neural network models while maintaining high accuracy in the case of sparse data sets. Furthermore, an attention‐mechanism input layer is integrated into the model to rank feature importance, providing global interpretation to the proposed framework. We also perform sensitivity analysis on the physical process and propose models to compare their different strategies of feature ranking. The results show that the two methods have different sensitivities to different feature patterns, but they complement each other. In summary, the proposed model is credible and stable for practical applications and has the potential to guide more efficient paddy management.

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Section: Framework Outlinementioning

confidence: 99%