Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain
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 dev...
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doaj-a0c3a63b0b78440fb3905c284d4e38482020-12-25T00:00:39ZengMDPI AGSensors1424-82202021-12-0121464610.3390/s21010046Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China PlainGangqiang Zhang0Wei Zheng1Wenjie Yin2Weiwei Lei3School of Surveying and Landing Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Surveying and Landing Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaQian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, ChinaSchool of Surveying and Landing Information Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaThe 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.https://www.mdpi.com/1424-8220/21/1/46machine learning-based fusion modelGRACEgradient boosting decision treegroundwater level anomaliesstatistical downscalingNorth China Plain |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gangqiang Zhang Wei Zheng Wenjie Yin Weiwei Lei |
spellingShingle |
Gangqiang Zhang Wei Zheng Wenjie Yin Weiwei Lei Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain Sensors machine learning-based fusion model GRACE gradient boosting decision tree groundwater level anomalies statistical downscaling North China Plain |
author_facet |
Gangqiang Zhang Wei Zheng Wenjie Yin Weiwei Lei |
author_sort |
Gangqiang Zhang |
title |
Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain |
title_short |
Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain |
title_full |
Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain |
title_fullStr |
Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain |
title_full_unstemmed |
Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain |
title_sort |
improving the resolution and accuracy of groundwater level anomalies using the machine learning-based fusion model in the north china plain |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-12-01 |
description |
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. |
topic |
machine learning-based fusion model GRACE gradient boosting decision tree groundwater level anomalies statistical downscaling North China Plain |
url |
https://www.mdpi.com/1424-8220/21/1/46 |
work_keys_str_mv |
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