Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Abstract

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

Large‐Scale Groundwater Monitoring in Brazil Assisted With Satellite‐Based Artificial Intelligence Techniques

“…There is a clear contribution of GRACE data assimilation (DA) into hydrological models in the representation and prediction of hydrological processes (Getirana et al., 2020a, 2020b; Girotto et al., 2017; Jung et al., 2019; Kumar et al., 2016; Zaitchik et al., 2008). Nevertheless, new tools based on the so‐called artificial intelligence (AI) algorithms have also proved to be very efficient for the pattern recognition of groundwater behavior worldwide (Afzaal et al., 2020; Huang et al., 2019; Iqbal et al., 2021; Lähivaara et al., 2019; Ren et al., 2021; Tao et al., 2022; Zhang et al., 2020). AI algorithms, associated with GRACE‐based TWS variations can be of great value in the survey of aquifers.…”

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

“…Deep learning (DL) has become increasingly prevalent for anomaly detection (AD) applications for reliability, safety, and health monitoring in several domains with the proliferation of sensor data in recent years [ 1 , 2 , 3 ]. AD has been applied for a diverse set of tasks, including but not limited to machinery fault diagnosis and prognosis [ 4 , 5 ], electronic device fault diagnosis [ 6 , 7 , 8 , 9 ], medical diagnosis [ 10 , 11 , 12 , 13 ], cybersecurity [ 14 , 15 , 16 ], crowd monitoring [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ], traffic monitoring [ 24 , 25 ], environment monitoring [ 26 ], the Internet of things [ 3 , 27 ], and energy and power management [ 28 , 29 ]. AD aims to determine anomalies depending on the setting and application domain [ 2 ].…”

“…Several DL models have been proposed in the literature for diverse data types, such as structural [ 1 ], time series [ 7 , 8 , 9 , 12 , 13 , 16 , 27 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ], image [ 10 , 26 ], graph network data [ 14 , 15 , 24 , 25 , 39 ], and spatio-temporal [ 10 , 14 , 15 , 17 , 18 , 19 , 20 , 21 , 22 , 24 , 25 , 39 ]. Spatio-temporal (ST) data are commonly collected in diverse domains, such as visual streaming data [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ], transportation traffic flows [ 24 , 25 ], sensor networks [ 14 , 15 , 39 ], geoscience [ 26 ], medical diagnosis [ 10 ], and high-energy physics [ 40 , 41 ]. A uni...…”

Evaluating downscaling methods of GRACE data: a case study over a fractured crystalline aquifer in South India

“…In the literature, data-driven methods have been used to downscale GRACE data at various scales, either at the watershed scale for a thematic approach as in Seyoum and Milewski (2017) (5,000 km 2 to 20,000 km 2 ), or grid-based. The downscaling resolution for data-based techniques is often limited by the coarsest resolution among the predictors : rainfall from the Tropical Rainfall Measuring Mission (TRMM) or model outputs from the NASA's Global Land Data Assimilation System (GLDAS) at 0.25 • (Ali et al, 2021;Jyolsna et al, 2021;Ning et al, 2014;Seyoum et al, 2019;Zhang et al, 2021a), the NASA's North American Land Data Assimilation System (NLDAS) model at 0.125 • (Sahour et al, 2020), the Ecological Assimilation of Land and Climate Observations (EALCO) model at 5 km (Zhong et al, 2021) or evapotranspiration from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 2 km (Yin et al, 2018). Even finer resolution can be targeted when using interpolation based methods, up to the kilometer (Zhang et al, 2021a;Zuo et al, 2021).…”