Prediction of groundwater level using satellite image and multivariate GRU

Park JaeSeong

doi:10.1117/12.3031436

20 November 2024 Prediction of groundwater level using satellite image and multivariate GRU

Park JaeSeong

Proceedings Volume 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI; 131910L (2024) https://doi.org/10.1117/12.3031436
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

Groundwater depletion, driven by excessive extraction and climate change, necessitates accurate prediction models. This study proposes a groundwater level prediction model using GPM Core satellite precipitation data combined with a GRU (Gated Recurrent Unit) time series prediction model. The model's performance will be compared with Korea's LDAPS (Local Data Assimilation and Prediction System) to develop an optimal groundwater level prediction model for Korea. The research focuses on a monitoring station in Shinpung-myeon, Gongju-si, Chungcheongnam-do, covering the period from 2013 to 2021. Input data includes GPM Core precipitation, Day of Year (DOY), and cumulative precipitation data. The 20-day cumulative precipitation showed high importance, improving the model's performance. This model is expected to effectively monitor groundwater level changes and predict decreases due to reduced precipitation, facilitating proactive groundwater management. This work is supported by the Korea Agency for Infrastructure Technology Advancemen(KAIA) grant funded by the Ministry of Land,Infrastructure and Transport (Grant RS-2022-00155763).

Conference Presentation

Citation Download Citation

Park JaeSeong "Prediction of groundwater level using satellite image and multivariate GRU", Proc. SPIE 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI, 131910L (20 November 2024); https://doi.org/10.1117/12.3031436

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KEYWORDS

Data modeling

Earth observing sensors

Satellite imaging

Satellites

Environmental monitoring

Education and training

Performance modeling

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