Improving Groundwater Imputation through Iterative Refinement Using Spatial and Temporal Correlations from In Situ Data with Machine Learning
Abstract
:1. Introduction
1.1. Motivation
1.2. Research Overview
2. Methods
2.1. Methods Overview
- Step A: Obtain the raw groundwater data with gaps and preprocess each dataset so that the datasets at each well have the same time steps.
- Step B: Perform an initial imputation to generate a complete time series dataset for each well. This means that every discretized time step will have an associated value. After imputation, replace any imputed value that has an observed measurement with the original data. The imputation step only fills gaps (i.e., imputation).
- Step 1: Select the number of iterations, n, to pass through the entire data set.
- Step 2: Use a Hampel filter, described in Section 2.2, to smooth synthetic data spikes or model predictions that are unrealistic for groundwater data. This process removes outliers from the initially imputed dataset. Before each iteration step, apply this filter to remove outliers. The Hampel filter is used so that any anomalies do not propagate errors.
- Step 3: Iterate through each well, w, in the aquifer. For each well:
- ○
- Step 3a: Select a small set of imputed time series datasets from the wells correlated to the target well. We selected wells based on linear correlation and spatial distance; both ideas are explained in Section 2.3.
- ○
- Step 3b: Develop a model for the target well using the time series data selected in Step 3a.
- ○
- Step 3c: Run the target well model to generate a complete time series. Replace any predictions that have an observed value with the in situ measurement. The results of every model are updated synchronously at the end of the iteration. This means that an updated representation will not be available as a feature until the next iteration; if a particular well is selected multiple times as a feature, each model will see the same version of the data. Once every well has been visited, the model output is used as the input for the next iteration.
- Step 4: Repeat Steps 2 and 3 for n iterations.
- Step 5: Examine the results.
2.2. Hampel Filter
2.3. Well Modeling
2.3.1. Well Feature Selection
2.3.2. Prior Features
2.3.3. Temporal Features
2.4. Iterative Refinement
3. Results
3.1. Case Study: Beryl-Enterprise Utah Aquifer
3.2. Aquifer Results
3.3. Well Details
3.4. Validation through Water Storage Analysis
4. Discussion
4.1. Imputation Case I
4.2. Imputation Case II
4.3. Imputation Case III
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Value | Feature Wells Used | Value | Feature Wells Used |
---|---|---|---|
< 0.1 | 11 | < 0.6 | 6 |
< 0.2 | 10 | < 0.7 | |
< 0.3 | 9 | < 0.8 | 5 |
< 0.4 | 8 | < 0.9 | |
< 0.5 | 7 | < 1.0 |
Date | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Decimal Time |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 January 1948 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0000 |
1 February 1948 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0012 |
1 March 1948 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0023 |
1 April 1948 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0035 |
1 May 1948 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0046 |
1 June 1948 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0058 |
1 July 2020 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0.9942 |
1 August 2020 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0.9954 |
1 September 2020 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0.9965 |
1 October 2020 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0.9977 |
1 November 2020 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0.9988 |
1 December 2020 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1.0000 |
Imputation Case I | Target and feature wells have measured data over the same intervals and the feature well has measured data over the gaps. |
Imputation Case II | Target and feature wells do not necessarily have measured data over the same intervals. Much of the correlation between the two is conducted through previous imputation results. The feature well have measured data within the gaps of the target well. |
Imputation Case III | Target and feature wells have measured data over the same interval, but only imputed values exist over the gap periods. The feature wells do not have any measured data in the gaps. |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Ramirez, S.G.; Williams, G.P.; Jones, N.L.; Ames, D.P.; Radebaugh, J. Improving Groundwater Imputation through Iterative Refinement Using Spatial and Temporal Correlations from In Situ Data with Machine Learning. Water 2023, 15, 1236. https://doi.org/10.3390/w15061236
Ramirez SG, Williams GP, Jones NL, Ames DP, Radebaugh J. Improving Groundwater Imputation through Iterative Refinement Using Spatial and Temporal Correlations from In Situ Data with Machine Learning. Water. 2023; 15(6):1236. https://doi.org/10.3390/w15061236
Chicago/Turabian StyleRamirez, Saul G., Gustavious Paul Williams, Norman L. Jones, Daniel P. Ames, and Jani Radebaugh. 2023. "Improving Groundwater Imputation through Iterative Refinement Using Spatial and Temporal Correlations from In Situ Data with Machine Learning" Water 15, no. 6: 1236. https://doi.org/10.3390/w15061236
APA StyleRamirez, S. G., Williams, G. P., Jones, N. L., Ames, D. P., & Radebaugh, J. (2023). Improving Groundwater Imputation through Iterative Refinement Using Spatial and Temporal Correlations from In Situ Data with Machine Learning. Water, 15(6), 1236. https://doi.org/10.3390/w15061236