Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world’s freshwater resources have inadequate monitoring of critical environmental variables needed for management. Yet, the need to have widespread pr...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2025-01-01
|
| Series: | Environmental Data Science |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460224000141/type/journal_article |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832591644368568320 |
|---|---|
| author | Jared D. Willard Charuleka Varadharajan Xiaowei Jia Vipin Kumar |
| author_facet | Jared D. Willard Charuleka Varadharajan Xiaowei Jia Vipin Kumar |
| author_sort | Jared D. Willard |
| collection | DOAJ |
| description | Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world’s freshwater resources have inadequate monitoring of critical environmental variables needed for management. Yet, the need to have widespread predictions of hydrological variables such as river flow and water quality has become increasingly urgent due to climate and land use change over the past decades, and their associated impacts on water resources. Modern machine learning methods increasingly outperform their process-based and empirical model counterparts for hydrologic time series prediction with their ability to extract information from large, diverse data sets. We review relevant state-of-the art applications of machine learning for streamflow, water quality, and other water resources prediction and discuss opportunities to improve the use of machine learning with emerging methods for incorporating watershed characteristics and process knowledge into classical, deep learning, and transfer learning methodologies. The analysis here suggests most prior efforts have been focused on deep learning frameworks built on many sites for predictions at daily time scales in the United States, but that comparisons between different classes of machine learning methods are few and inadequate. We identify several open questions for time series predictions in unmonitored sites that include incorporating dynamic inputs and site characteristics, mechanistic understanding and spatial context, and explainable AI techniques in modern machine learning frameworks. |
| format | Article |
| id | doaj-art-33df83056443443b88f745965d212145 |
| institution | Kabale University |
| issn | 2634-4602 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Environmental Data Science |
| spelling | doaj-art-33df83056443443b88f745965d2121452025-01-22T07:15:27ZengCambridge University PressEnvironmental Data Science2634-46022025-01-01410.1017/eds.2024.14Time series predictions in unmonitored sites: a survey of machine learning techniques in water resourcesJared D. Willard0https://orcid.org/0000-0003-4434-051XCharuleka Varadharajan1https://orcid.org/0000-0002-4142-3224Xiaowei Jia2Vipin Kumar3Computing Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USAEarth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USADepartment of Computer Science, University of Pittsburgh, Pittsburgh, PA, USADepartment of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USAPrediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world’s freshwater resources have inadequate monitoring of critical environmental variables needed for management. Yet, the need to have widespread predictions of hydrological variables such as river flow and water quality has become increasingly urgent due to climate and land use change over the past decades, and their associated impacts on water resources. Modern machine learning methods increasingly outperform their process-based and empirical model counterparts for hydrologic time series prediction with their ability to extract information from large, diverse data sets. We review relevant state-of-the art applications of machine learning for streamflow, water quality, and other water resources prediction and discuss opportunities to improve the use of machine learning with emerging methods for incorporating watershed characteristics and process knowledge into classical, deep learning, and transfer learning methodologies. The analysis here suggests most prior efforts have been focused on deep learning frameworks built on many sites for predictions at daily time scales in the United States, but that comparisons between different classes of machine learning methods are few and inadequate. We identify several open questions for time series predictions in unmonitored sites that include incorporating dynamic inputs and site characteristics, mechanistic understanding and spatial context, and explainable AI techniques in modern machine learning frameworks.https://www.cambridge.org/core/product/identifier/S2634460224000141/type/journal_articledeep learningmachine learningprediction in unmonitored basinstransfer learning |
| spellingShingle | Jared D. Willard Charuleka Varadharajan Xiaowei Jia Vipin Kumar Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources Environmental Data Science deep learning machine learning prediction in unmonitored basins transfer learning |
| title | Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources |
| title_full | Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources |
| title_fullStr | Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources |
| title_full_unstemmed | Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources |
| title_short | Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources |
| title_sort | time series predictions in unmonitored sites a survey of machine learning techniques in water resources |
| topic | deep learning machine learning prediction in unmonitored basins transfer learning |
| url | https://www.cambridge.org/core/product/identifier/S2634460224000141/type/journal_article |
| work_keys_str_mv | AT jareddwillard timeseriespredictionsinunmonitoredsitesasurveyofmachinelearningtechniquesinwaterresources AT charulekavaradharajan timeseriespredictionsinunmonitoredsitesasurveyofmachinelearningtechniquesinwaterresources AT xiaoweijia timeseriespredictionsinunmonitoredsitesasurveyofmachinelearningtechniquesinwaterresources AT vipinkumar timeseriespredictionsinunmonitoredsitesasurveyofmachinelearningtechniquesinwaterresources |