Machine Learning-Based Water Management Strategies for Sustainable Groundwater Resources
Abstract Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to en...
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| Autor*in: |
Sanu, Shubha G. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: SN Computer Science - Springer Nature Singapore, 2020, 5(2024), 4 vom: 27. März |
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| Übergeordnetes Werk: |
volume:5 ; year:2024 ; number:4 ; day:27 ; month:03 |
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| DOI / URN: |
10.1007/s42979-024-02686-8 |
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| Katalog-ID: |
SPR055330029 |
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| 520 | |a Abstract Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to enhance groundwater level prediction by extracting complex patterns from the previous data. In recent years, groundwater level forecasting using deep learning has received increasing attention. Recurrent neural networks (RNNs) are a common deep learning technique for predicting groundwater levels. Since RNNs are capable of learning long-range dependencies in the data, they are well suited for time-series prediction problems. Utilizing convolutional neural networks (CNNs) is an additional strategy. CNNs are frequently employed for tasks such as segmenting and classifying images, but they may also be used to predict time series. CNNs are capable of effectively identifying spatial patterns in the data, which can be helpful for predicting groundwater levels. Numerous researches have shown that groundwater level prediction models based on deep learning produce promising outcomes. But there are still some issues that need to be resolved, such as the requirement for a substantial amount of training data and the complexity of deciphering the output of deep learning models. Overall, deep learning is a promising new strategy for predicting groundwater levels. Future groundwater level prediction algorithms should become progressively more precise and trustworthy as deep learning techniques in the future. | ||
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10.1007/s42979-024-02686-8 doi (DE-627)SPR055330029 (SPR)s42979-024-02686-8-e DE-627 ger DE-627 rakwb eng Sanu, Shubha G. verfasserin aut Machine Learning-Based Water Management Strategies for Sustainable Groundwater Resources 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to enhance groundwater level prediction by extracting complex patterns from the previous data. In recent years, groundwater level forecasting using deep learning has received increasing attention. Recurrent neural networks (RNNs) are a common deep learning technique for predicting groundwater levels. Since RNNs are capable of learning long-range dependencies in the data, they are well suited for time-series prediction problems. Utilizing convolutional neural networks (CNNs) is an additional strategy. CNNs are frequently employed for tasks such as segmenting and classifying images, but they may also be used to predict time series. CNNs are capable of effectively identifying spatial patterns in the data, which can be helpful for predicting groundwater levels. Numerous researches have shown that groundwater level prediction models based on deep learning produce promising outcomes. But there are still some issues that need to be resolved, such as the requirement for a substantial amount of training data and the complexity of deciphering the output of deep learning models. Overall, deep learning is a promising new strategy for predicting groundwater levels. Future groundwater level prediction algorithms should become progressively more precise and trustworthy as deep learning techniques in the future. Accuracy (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Groundwater level (dpeaa)DE-He213 Recurrent neural networks (dpeaa)DE-He213 Math, Mallikarjun M. aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 4 vom: 27. März (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:4 day:27 month:03 https://dx.doi.org/10.1007/s42979-024-02686-8 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 4 27 03 |
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10.1007/s42979-024-02686-8 doi (DE-627)SPR055330029 (SPR)s42979-024-02686-8-e DE-627 ger DE-627 rakwb eng Sanu, Shubha G. verfasserin aut Machine Learning-Based Water Management Strategies for Sustainable Groundwater Resources 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to enhance groundwater level prediction by extracting complex patterns from the previous data. In recent years, groundwater level forecasting using deep learning has received increasing attention. Recurrent neural networks (RNNs) are a common deep learning technique for predicting groundwater levels. Since RNNs are capable of learning long-range dependencies in the data, they are well suited for time-series prediction problems. Utilizing convolutional neural networks (CNNs) is an additional strategy. CNNs are frequently employed for tasks such as segmenting and classifying images, but they may also be used to predict time series. CNNs are capable of effectively identifying spatial patterns in the data, which can be helpful for predicting groundwater levels. Numerous researches have shown that groundwater level prediction models based on deep learning produce promising outcomes. But there are still some issues that need to be resolved, such as the requirement for a substantial amount of training data and the complexity of deciphering the output of deep learning models. Overall, deep learning is a promising new strategy for predicting groundwater levels. Future groundwater level prediction algorithms should become progressively more precise and trustworthy as deep learning techniques in the future. Accuracy (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Groundwater level (dpeaa)DE-He213 Recurrent neural networks (dpeaa)DE-He213 Math, Mallikarjun M. aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 4 vom: 27. März (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:4 day:27 month:03 https://dx.doi.org/10.1007/s42979-024-02686-8 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 4 27 03 |
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10.1007/s42979-024-02686-8 doi (DE-627)SPR055330029 (SPR)s42979-024-02686-8-e DE-627 ger DE-627 rakwb eng Sanu, Shubha G. verfasserin aut Machine Learning-Based Water Management Strategies for Sustainable Groundwater Resources 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to enhance groundwater level prediction by extracting complex patterns from the previous data. In recent years, groundwater level forecasting using deep learning has received increasing attention. Recurrent neural networks (RNNs) are a common deep learning technique for predicting groundwater levels. Since RNNs are capable of learning long-range dependencies in the data, they are well suited for time-series prediction problems. Utilizing convolutional neural networks (CNNs) is an additional strategy. CNNs are frequently employed for tasks such as segmenting and classifying images, but they may also be used to predict time series. CNNs are capable of effectively identifying spatial patterns in the data, which can be helpful for predicting groundwater levels. Numerous researches have shown that groundwater level prediction models based on deep learning produce promising outcomes. But there are still some issues that need to be resolved, such as the requirement for a substantial amount of training data and the complexity of deciphering the output of deep learning models. Overall, deep learning is a promising new strategy for predicting groundwater levels. Future groundwater level prediction algorithms should become progressively more precise and trustworthy as deep learning techniques in the future. Accuracy (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Groundwater level (dpeaa)DE-He213 Recurrent neural networks (dpeaa)DE-He213 Math, Mallikarjun M. aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 4 vom: 27. März (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:4 day:27 month:03 https://dx.doi.org/10.1007/s42979-024-02686-8 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 4 27 03 |
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10.1007/s42979-024-02686-8 doi (DE-627)SPR055330029 (SPR)s42979-024-02686-8-e DE-627 ger DE-627 rakwb eng Sanu, Shubha G. verfasserin aut Machine Learning-Based Water Management Strategies for Sustainable Groundwater Resources 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to enhance groundwater level prediction by extracting complex patterns from the previous data. In recent years, groundwater level forecasting using deep learning has received increasing attention. Recurrent neural networks (RNNs) are a common deep learning technique for predicting groundwater levels. Since RNNs are capable of learning long-range dependencies in the data, they are well suited for time-series prediction problems. Utilizing convolutional neural networks (CNNs) is an additional strategy. CNNs are frequently employed for tasks such as segmenting and classifying images, but they may also be used to predict time series. CNNs are capable of effectively identifying spatial patterns in the data, which can be helpful for predicting groundwater levels. Numerous researches have shown that groundwater level prediction models based on deep learning produce promising outcomes. But there are still some issues that need to be resolved, such as the requirement for a substantial amount of training data and the complexity of deciphering the output of deep learning models. Overall, deep learning is a promising new strategy for predicting groundwater levels. Future groundwater level prediction algorithms should become progressively more precise and trustworthy as deep learning techniques in the future. Accuracy (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Groundwater level (dpeaa)DE-He213 Recurrent neural networks (dpeaa)DE-He213 Math, Mallikarjun M. aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 4 vom: 27. März (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:4 day:27 month:03 https://dx.doi.org/10.1007/s42979-024-02686-8 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 4 27 03 |
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10.1007/s42979-024-02686-8 doi (DE-627)SPR055330029 (SPR)s42979-024-02686-8-e DE-627 ger DE-627 rakwb eng Sanu, Shubha G. verfasserin aut Machine Learning-Based Water Management Strategies for Sustainable Groundwater Resources 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to enhance groundwater level prediction by extracting complex patterns from the previous data. In recent years, groundwater level forecasting using deep learning has received increasing attention. Recurrent neural networks (RNNs) are a common deep learning technique for predicting groundwater levels. Since RNNs are capable of learning long-range dependencies in the data, they are well suited for time-series prediction problems. Utilizing convolutional neural networks (CNNs) is an additional strategy. CNNs are frequently employed for tasks such as segmenting and classifying images, but they may also be used to predict time series. CNNs are capable of effectively identifying spatial patterns in the data, which can be helpful for predicting groundwater levels. Numerous researches have shown that groundwater level prediction models based on deep learning produce promising outcomes. But there are still some issues that need to be resolved, such as the requirement for a substantial amount of training data and the complexity of deciphering the output of deep learning models. Overall, deep learning is a promising new strategy for predicting groundwater levels. Future groundwater level prediction algorithms should become progressively more precise and trustworthy as deep learning techniques in the future. Accuracy (dpeaa)DE-He213 Convolutional neural networks (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Groundwater level (dpeaa)DE-He213 Recurrent neural networks (dpeaa)DE-He213 Math, Mallikarjun M. aut Enthalten in SN Computer Science Springer Nature Singapore, 2020 5(2024), 4 vom: 27. März (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:5 year:2024 number:4 day:27 month:03 https://dx.doi.org/10.1007/s42979-024-02686-8 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 5 2024 4 27 03 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to enhance groundwater level prediction by extracting complex patterns from the previous data. In recent years, groundwater level forecasting using deep learning has received increasing attention. 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Machine Learning-Based Water Management Strategies for Sustainable Groundwater Resources |
| abstract |
Abstract Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to enhance groundwater level prediction by extracting complex patterns from the previous data. In recent years, groundwater level forecasting using deep learning has received increasing attention. Recurrent neural networks (RNNs) are a common deep learning technique for predicting groundwater levels. Since RNNs are capable of learning long-range dependencies in the data, they are well suited for time-series prediction problems. Utilizing convolutional neural networks (CNNs) is an additional strategy. CNNs are frequently employed for tasks such as segmenting and classifying images, but they may also be used to predict time series. CNNs are capable of effectively identifying spatial patterns in the data, which can be helpful for predicting groundwater levels. Numerous researches have shown that groundwater level prediction models based on deep learning produce promising outcomes. But there are still some issues that need to be resolved, such as the requirement for a substantial amount of training data and the complexity of deciphering the output of deep learning models. Overall, deep learning is a promising new strategy for predicting groundwater levels. Future groundwater level prediction algorithms should become progressively more precise and trustworthy as deep learning techniques in the future. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to enhance groundwater level prediction by extracting complex patterns from the previous data. In recent years, groundwater level forecasting using deep learning has received increasing attention. Recurrent neural networks (RNNs) are a common deep learning technique for predicting groundwater levels. Since RNNs are capable of learning long-range dependencies in the data, they are well suited for time-series prediction problems. Utilizing convolutional neural networks (CNNs) is an additional strategy. CNNs are frequently employed for tasks such as segmenting and classifying images, but they may also be used to predict time series. CNNs are capable of effectively identifying spatial patterns in the data, which can be helpful for predicting groundwater levels. Numerous researches have shown that groundwater level prediction models based on deep learning produce promising outcomes. But there are still some issues that need to be resolved, such as the requirement for a substantial amount of training data and the complexity of deciphering the output of deep learning models. Overall, deep learning is a promising new strategy for predicting groundwater levels. Future groundwater level prediction algorithms should become progressively more precise and trustworthy as deep learning techniques in the future. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Groundwater resources are under increasing pressure, nevertheless, as a result of population growth, climate change, and overuse. Accurate estimates of groundwater levels are essential for the management of water resources to be sustainable. Deep learning algorithms have the potential to enhance groundwater level prediction by extracting complex patterns from the previous data. In recent years, groundwater level forecasting using deep learning has received increasing attention. Recurrent neural networks (RNNs) are a common deep learning technique for predicting groundwater levels. Since RNNs are capable of learning long-range dependencies in the data, they are well suited for time-series prediction problems. Utilizing convolutional neural networks (CNNs) is an additional strategy. CNNs are frequently employed for tasks such as segmenting and classifying images, but they may also be used to predict time series. CNNs are capable of effectively identifying spatial patterns in the data, which can be helpful for predicting groundwater levels. Numerous researches have shown that groundwater level prediction models based on deep learning produce promising outcomes. But there are still some issues that need to be resolved, such as the requirement for a substantial amount of training data and the complexity of deciphering the output of deep learning models. Overall, deep learning is a promising new strategy for predicting groundwater levels. Future groundwater level prediction algorithms should become progressively more precise and trustworthy as deep learning techniques in the future. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Machine Learning-Based Water Management Strategies for Sustainable Groundwater Resources |
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Math, Mallikarjun M. |
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