Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020WR028059 |
Groundwater Withdrawal Prediction Using Integrated Multi‐Temporal Remote Sensing Datasets and Machine Learning | |
S. Majumdar; R. Smith; J. J. Butler; V. Lakshmi | |
2020-10-22 | |
发表期刊 | Water Resources Research
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出版年 | 2020 |
英文摘要 | Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multi‐temporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests— a state of the art machine learning algorithm— to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the years 2002‐2019. Our modeled withdrawals had high accuracy on both training and testing datasets (R2≈ 0.99 and R2≈ 0.93, respectively) during leave‐one‐out (year) cross‐validation with low Mean Absolute Error (MAE) ≈ 4.31 mm and Root Mean Square Error (RMSE) ≈ 13.50 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R2≈ 0.84) with MAE ≈ 9.72 mm and RMSE ≈ 24.17 mm. Therefore, the proposed machine learning approach should be applicable to similar regions for proactive water management practices. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/300211 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | S. Majumdar,R. Smith,J. J. Butler,等. Groundwater Withdrawal Prediction Using Integrated Multi‐Temporal Remote Sensing Datasets and Machine Learning[J]. Water Resources Research,2020. |
APA | S. Majumdar,R. Smith,J. J. Butler,&V. Lakshmi.(2020).Groundwater Withdrawal Prediction Using Integrated Multi‐Temporal Remote Sensing Datasets and Machine Learning.Water Resources Research. |
MLA | S. Majumdar,et al."Groundwater Withdrawal Prediction Using Integrated Multi‐Temporal Remote Sensing Datasets and Machine Learning".Water Resources Research (2020). |
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