Soil moisture forecasting in wireless sensor networks via spatiotemporal graph convolutional networks
Abstract Wireless sensor networks enable long‐term, automated, networked monitoring of soil moisture, an indispensable tool in soil moisture sensing research and application. The growing abundance of soil moisture data has increased interest in using historical data to forecast future soil moisture...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | Vadose Zone Journal |
| Online Access: | https://doi.org/10.1002/vzj2.70000 |
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| Summary: | Abstract Wireless sensor networks enable long‐term, automated, networked monitoring of soil moisture, an indispensable tool in soil moisture sensing research and application. The growing abundance of soil moisture data has increased interest in using historical data to forecast future soil moisture variations effectively. However, due to the combined effects of internal factors like soil types, terrain, and vegetation cover, as well as external factors such as precipitation and temperature, soil moisture data in wireless sensor networks exhibit complex spatial and temporal interdependencies. Consequently, developing predictive models that can accurately capture these dependencies and enable precise forecasts of soil moisture within these networks poses a significant challenge. To address this problem, a graph was used to express the network topology among sensors. Graph neural network is adept at capturing and utilizing the complex spatiotemporal relationships and patterns inherent in graph‐structured data, thereby enhancing prediction accuracy. This study incorporates soil temperature and precipitation as external factors into the model predictions. The performance of the prediction model was evaluated by comparing its results with those of two baseline methods across different time intervals. The results indicated that the model is suitable for long‐term forecasting of soil moisture. The model's accuracy showed improvements of 49.53%, 34.86%, and 29.73% at the 48‐h, 144‐h, and 192‐h time steps, respectively. The correlation coefficient between predicted and actual values was 0.94. The model contributes to the scientific management of water resources, facilitates the rational regulation of irrigation water volume and timing, and enhances agricultural yield. |
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| ISSN: | 1539-1663 |