Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models
The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. T...
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| Veröffentlicht in: | Journal of environmental management Jg. 351; S. 119789 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
England
Elsevier Ltd
01.02.2024
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| ISSN: | 0301-4797, 1095-8630, 1095-8630 |
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| Abstract | The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.
[Display omitted]
•Build a novel ConvAE-LSTM groundwater forecast model by fusing ConvAE and LSTM.•ConvAE-LSTM deeply extracts features from observed (past) and simulated (future) data.•ConvAE-LSTM effectively extracts spatial information from high dimensional images.•ConvAE-LSTM outperforms HBV-light due to state transition overcoming data uncertainty.•ConvAE-LSTM makes accurate regional multi-horizon groundwater level forecasts. |
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| AbstractList | The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.
[Display omitted]
•Build a novel ConvAE-LSTM groundwater forecast model by fusing ConvAE and LSTM.•ConvAE-LSTM deeply extracts features from observed (past) and simulated (future) data.•ConvAE-LSTM effectively extracts spatial information from high dimensional images.•ConvAE-LSTM outperforms HBV-light due to state transition overcoming data uncertainty.•ConvAE-LSTM makes accurate regional multi-horizon groundwater level forecasts. The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource. The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource. |
| ArticleNumber | 119789 |
| Author | Sun, Wei Chang, Li-Chiu Chang, Fi-John Kow, Pu-Yun Liou, Jia-Yi |
| Author_xml | – sequence: 1 givenname: Pu-Yun orcidid: 0000-0001-5718-9316 surname: Kow fullname: Kow, Pu-Yun organization: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan – sequence: 2 givenname: Jia-Yi surname: Liou fullname: Liou, Jia-Yi organization: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan – sequence: 3 givenname: Wei surname: Sun fullname: Sun, Wei organization: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan – sequence: 4 givenname: Li-Chiu surname: Chang fullname: Chang, Li-Chiu organization: Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City, 25137, Taiwan – sequence: 5 givenname: Fi-John orcidid: 0000-0002-1655-8573 surname: Chang fullname: Chang, Fi-John email: changfj@ntu.edu.tw organization: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, 10617, Taiwan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38100860$$D View this record in MEDLINE/PubMed |
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| Keywords | Deep learning Long short-term memory neural network (LSTM) Convolutional neural network (CNN) Groundwater level forecast HBV-Light model |
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| SubjectTerms | case studies Convolutional neural network (CNN) data collection Deep learning environmental management groundwater Groundwater level forecast HBV-Light model Long short-term memory neural network (LSTM) neural networks Taiwan water management water table watersheds |
| Title | Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models |
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