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Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators
The US Drought Monitor (USDM) is a hallmark in real time drought monitoring and assessment as it was developed by multiple agencies to provide an accurate and timely assessment of drought conditions in the US on a weekly basis. The map is built based on multiple physical indicators as well as report...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573101/ https://www.ncbi.nlm.nih.gov/pubmed/34761214 http://dx.doi.org/10.3389/fdata.2021.750536 |
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author | Hatami Bahman Beiglou, Pouyan Luo, Lifeng Tan, Pang-Ning Pei, Lisi |
author_facet | Hatami Bahman Beiglou, Pouyan Luo, Lifeng Tan, Pang-Ning Pei, Lisi |
author_sort | Hatami Bahman Beiglou, Pouyan |
collection | PubMed |
description | The US Drought Monitor (USDM) is a hallmark in real time drought monitoring and assessment as it was developed by multiple agencies to provide an accurate and timely assessment of drought conditions in the US on a weekly basis. The map is built based on multiple physical indicators as well as reported observations from local contributors before human analysts combine the information and produce the drought map using their best judgement. Since human subjectivity is included in the production of the USDM maps, it is not an entirely clear quantitative procedure for other entities to reproduce the maps. In this study, we developed a framework to automatically generate the maps through a machine learning approach by predicting the drought categories across the domain of study. A persistence model served as the baseline model for comparison in the framework. Three machine learning algorithms, logistic regression, random forests, and support vector machines, with four different groups of input data, which formed an overall of 12 different configurations, were used for the prediction of drought categories. Finally, all the configurations were evaluated against the baseline model to select the best performing option. The results showed that our proposed framework could reproduce the drought maps to a near-perfect level with the support vector machines algorithm and the group 4 data. The rest of the findings of this study can be highlighted as: 1) employing the past week drought data as a predictor in the models played an important role in achieving high prediction scores, 2) the nonlinear models, random forest, and support vector machines had a better overall performance compared to the logistic regression models, and 3) with borrowing the neighboring grid cells information, we could compensate the lack of training data in the grid cells with insufficient historical USDM data particularly for extreme and exceptional drought conditions. |
format | Online Article Text |
id | pubmed-8573101 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85731012021-11-09 Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators Hatami Bahman Beiglou, Pouyan Luo, Lifeng Tan, Pang-Ning Pei, Lisi Front Big Data Big Data The US Drought Monitor (USDM) is a hallmark in real time drought monitoring and assessment as it was developed by multiple agencies to provide an accurate and timely assessment of drought conditions in the US on a weekly basis. The map is built based on multiple physical indicators as well as reported observations from local contributors before human analysts combine the information and produce the drought map using their best judgement. Since human subjectivity is included in the production of the USDM maps, it is not an entirely clear quantitative procedure for other entities to reproduce the maps. In this study, we developed a framework to automatically generate the maps through a machine learning approach by predicting the drought categories across the domain of study. A persistence model served as the baseline model for comparison in the framework. Three machine learning algorithms, logistic regression, random forests, and support vector machines, with four different groups of input data, which formed an overall of 12 different configurations, were used for the prediction of drought categories. Finally, all the configurations were evaluated against the baseline model to select the best performing option. The results showed that our proposed framework could reproduce the drought maps to a near-perfect level with the support vector machines algorithm and the group 4 data. The rest of the findings of this study can be highlighted as: 1) employing the past week drought data as a predictor in the models played an important role in achieving high prediction scores, 2) the nonlinear models, random forest, and support vector machines had a better overall performance compared to the logistic regression models, and 3) with borrowing the neighboring grid cells information, we could compensate the lack of training data in the grid cells with insufficient historical USDM data particularly for extreme and exceptional drought conditions. Frontiers Media S.A. 2021-10-25 /pmc/articles/PMC8573101/ /pubmed/34761214 http://dx.doi.org/10.3389/fdata.2021.750536 Text en Copyright © 2021 Hatami Bahman Beiglou, Luo, Tan and Pei. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Hatami Bahman Beiglou, Pouyan Luo, Lifeng Tan, Pang-Ning Pei, Lisi Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators |
title | Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators |
title_full | Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators |
title_fullStr | Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators |
title_full_unstemmed | Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators |
title_short | Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators |
title_sort | automated analysis of the us drought monitor maps with machine learning and multiple drought indicators |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573101/ https://www.ncbi.nlm.nih.gov/pubmed/34761214 http://dx.doi.org/10.3389/fdata.2021.750536 |
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