GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE
Monitoring and managing groundwater resources is critical for sustaining livelihoods and supporting various human activities, including irrigation and drinking water supply. The most common method of monitoring groundwater is well water level measurements. These records can be difficult to collect a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/9/2247 |
| _version_ | 1827742736937123840 |
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| author | Sarva T. Pulla Hakan Yasarer Lance D. Yarbrough |
| author_facet | Sarva T. Pulla Hakan Yasarer Lance D. Yarbrough |
| author_sort | Sarva T. Pulla |
| collection | DOAJ |
| description | Monitoring and managing groundwater resources is critical for sustaining livelihoods and supporting various human activities, including irrigation and drinking water supply. The most common method of monitoring groundwater is well water level measurements. These records can be difficult to collect and maintain, especially in countries with limited infrastructure and resources. However, long-term data collection is required to characterize and evaluate trends. To address these challenges, we propose a framework that uses data from the Gravity Recovery and Climate Experiment (GRACE) mission and downscaling models to generate higher-resolution (1 km) groundwater predictions. The framework is designed to be flexible, allowing users to implement any machine learning model of interest. We selected four models: deep learning model, gradient tree boosting, multi-layer perceptron, and k-nearest neighbors regressor. To evaluate the effectiveness of the framework, we offer a case study of Sunflower County, Mississippi, using well data to validate the predictions. Overall, this paper provides a valuable contribution to the field of groundwater resource management by demonstrating a framework using remote sensing data and machine learning techniques to improve monitoring and management of this critical resource, especially to those who seek a faster way to begin to use these datasets and applications. |
| first_indexed | 2024-03-11T04:08:23Z |
| format | Article |
| id | doaj.art-d8a1bf50e9b044d1a0e087450befe2e1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:08:23Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d8a1bf50e9b044d1a0e087450befe2e12023-11-17T23:37:30ZengMDPI AGRemote Sensing2072-42922023-04-01159224710.3390/rs15092247GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACESarva T. Pulla0Hakan Yasarer1Lance D. Yarbrough2Department of Civil Engineering, The University of Mississippi, University, MS 38677, USADepartment of Civil Engineering, The University of Mississippi, University, MS 38677, USADepartment of Geology and Geological Engineering, The University of Mississippi, University, MS 38677, USAMonitoring and managing groundwater resources is critical for sustaining livelihoods and supporting various human activities, including irrigation and drinking water supply. The most common method of monitoring groundwater is well water level measurements. These records can be difficult to collect and maintain, especially in countries with limited infrastructure and resources. However, long-term data collection is required to characterize and evaluate trends. To address these challenges, we propose a framework that uses data from the Gravity Recovery and Climate Experiment (GRACE) mission and downscaling models to generate higher-resolution (1 km) groundwater predictions. The framework is designed to be flexible, allowing users to implement any machine learning model of interest. We selected four models: deep learning model, gradient tree boosting, multi-layer perceptron, and k-nearest neighbors regressor. To evaluate the effectiveness of the framework, we offer a case study of Sunflower County, Mississippi, using well data to validate the predictions. Overall, this paper provides a valuable contribution to the field of groundwater resource management by demonstrating a framework using remote sensing data and machine learning techniques to improve monitoring and management of this critical resource, especially to those who seek a faster way to begin to use these datasets and applications.https://www.mdpi.com/2072-4292/15/9/2247GRACE satellitegroundwaterdownscale |
| spellingShingle | Sarva T. Pulla Hakan Yasarer Lance D. Yarbrough GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE Remote Sensing GRACE satellite groundwater downscale |
| title | GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE |
| title_full | GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE |
| title_fullStr | GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE |
| title_full_unstemmed | GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE |
| title_short | GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE |
| title_sort | grace downscaler a framework to develop and evaluate downscaling models for grace |
| topic | GRACE satellite groundwater downscale |
| url | https://www.mdpi.com/2072-4292/15/9/2247 |
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