Simulation of Groundwater Level in Irrigation Area Based on Sky-Earth Cooperation and Deep Learning

CHEN Wen-long; YANG Yun-li; ZHANG Yu; HU Zu-kang

doi:10.11988/ckyyb.20220061

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Journal of Changjiang River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (7) : 88-95. DOI: 10.11988/ckyyb.20220061

Agricultural Water Conservancy

Simulation of Groundwater Level in Irrigation Area Based on Sky-Earth Cooperation and Deep Learning

CHEN Wen-long¹, YANG Yun-li¹, ZHANG Yu², HU Zu-kang³

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Abstract

In this study, we aim to simulate the groundwater level in irrigation area by utilizing sequential observation data from ground-based and remote sensing platforms. A deep learning model based on a multi-layer GRU network was developed for this purpose. Precipitation, soil moisture, historical measurements of groundwater levels, and Sentinel-2 remote sensing observations were used as simulation factors. Furthermore, groundwater level simulation experiments and result analyses are conducted for two irrigation areas located in North and South China. The experimental findings reveal that the groundwater level simulation model, which incorporates the synergy between sky-earth observations and deep learning, effectively establishes the intrinsic relationship between external environmental factors and groundwater levels within the irrigation area. The model is apparently superior to comparative models that merely considers groundwater level as it demonstrates impressive simulation performance, exhibits applicability in diverse geographical environments, and holds promising potential for practical applications. It can provide valuable decision-making support for crop cultivation and water resources management in irrigation area.

Key words

groundwater level simulation / deep learning / sky-earth cooperation / irrigation area / remote sensing

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CHEN Wen-long, YANG Yun-li, ZHANG Yu, HU Zu-kang. Simulation of Groundwater Level in Irrigation Area Based on Sky-Earth Cooperation and Deep Learning[J]. Journal of Changjiang River Scientific Research Institute. 2023, 40(7): 88-95 https://doi.org/10.11988/ckyyb.20220061

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