Spatiotemporal semantic network for ENSO forecasting over long time horizon
Abstract El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing eco...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhao, Jiakun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 53(2022), 6 vom: 09. Juli, Seite 6464-6480 |
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| Übergeordnetes Werk: |
volume:53 ; year:2022 ; number:6 ; day:09 ; month:07 ; pages:6464-6480 |
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| DOI / URN: |
10.1007/s10489-022-03861-1 |
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SPR049482963 |
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| 520 | |a Abstract El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing economic and social adverse effects. Recently, deep learning models show great potential in this task. However, despite decades of efforts, predicting ENSO at lead times of more than one year remains a major challenge and most of these existing studies focus on the single channel of meteorological data, ignoring the spatial and temporal dependence of these factors. To capture the spatiotemporal information of meteorological factors as well as promote the skill of ENSO forecasting over longer time horizon, we propose an end-to-end deep learning model SpatioTemporal Semantic Network named STSNet, which consists of three main modules: (1) Geographic Semantic Enhancement Module (GSEM) distinguishes the geographic semantics of various latitudes and longitudes through a learnable adaptive weight matrix. (2) A novel SpatioTemporal Convolutional Module(STCM) is designed specially to extract the multidimensional features by alternating the execution of temporal and spatial convolution and temporal attention. (3) Multi-scale temporal information is combined and exploited in Three-stream Temporal Scale Module (3sTSM) to further enhance the performance. Integrating these modules gives a powerful feature extractor STSNet, which has multi-scale receptive fields across both spatial and temporal dimensions. In order to verify the effectiveness and progressiveness of our model, we execute experiments on Historical Climate Observation and Simulation Dataset. The results show that STSNet can simultaneously provide effective ENSO prediction for 16 months, with higher correlation and lower deviation compared with other deep learning models. | ||
| 650 | 4 | |a ENSO forecasting |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Spatiotemporal semantic |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Deep learning |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Luo, Hailun |4 aut | |
| 700 | 1 | |a Sang, Weiguang |4 aut | |
| 700 | 1 | |a Sun, Kun |4 aut | |
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10.1007/s10489-022-03861-1 doi (DE-627)SPR049482963 (SPR)s10489-022-03861-1-e DE-627 ger DE-627 rakwb eng Zhao, Jiakun verfasserin aut Spatiotemporal semantic network for ENSO forecasting over long time horizon 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing economic and social adverse effects. Recently, deep learning models show great potential in this task. However, despite decades of efforts, predicting ENSO at lead times of more than one year remains a major challenge and most of these existing studies focus on the single channel of meteorological data, ignoring the spatial and temporal dependence of these factors. To capture the spatiotemporal information of meteorological factors as well as promote the skill of ENSO forecasting over longer time horizon, we propose an end-to-end deep learning model SpatioTemporal Semantic Network named STSNet, which consists of three main modules: (1) Geographic Semantic Enhancement Module (GSEM) distinguishes the geographic semantics of various latitudes and longitudes through a learnable adaptive weight matrix. (2) A novel SpatioTemporal Convolutional Module(STCM) is designed specially to extract the multidimensional features by alternating the execution of temporal and spatial convolution and temporal attention. (3) Multi-scale temporal information is combined and exploited in Three-stream Temporal Scale Module (3sTSM) to further enhance the performance. Integrating these modules gives a powerful feature extractor STSNet, which has multi-scale receptive fields across both spatial and temporal dimensions. In order to verify the effectiveness and progressiveness of our model, we execute experiments on Historical Climate Observation and Simulation Dataset. The results show that STSNet can simultaneously provide effective ENSO prediction for 16 months, with higher correlation and lower deviation compared with other deep learning models. ENSO forecasting (dpeaa)DE-He213 Spatiotemporal semantic (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Luo, Hailun aut Sang, Weiguang aut Sun, Kun aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2022), 6 vom: 09. Juli, Seite 6464-6480 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2022 number:6 day:09 month:07 pages:6464-6480 https://dx.doi.org/10.1007/s10489-022-03861-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2008 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_2188 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_2548 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 53 2022 6 09 07 6464-6480 |
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10.1007/s10489-022-03861-1 doi (DE-627)SPR049482963 (SPR)s10489-022-03861-1-e DE-627 ger DE-627 rakwb eng Zhao, Jiakun verfasserin aut Spatiotemporal semantic network for ENSO forecasting over long time horizon 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing economic and social adverse effects. Recently, deep learning models show great potential in this task. However, despite decades of efforts, predicting ENSO at lead times of more than one year remains a major challenge and most of these existing studies focus on the single channel of meteorological data, ignoring the spatial and temporal dependence of these factors. To capture the spatiotemporal information of meteorological factors as well as promote the skill of ENSO forecasting over longer time horizon, we propose an end-to-end deep learning model SpatioTemporal Semantic Network named STSNet, which consists of three main modules: (1) Geographic Semantic Enhancement Module (GSEM) distinguishes the geographic semantics of various latitudes and longitudes through a learnable adaptive weight matrix. (2) A novel SpatioTemporal Convolutional Module(STCM) is designed specially to extract the multidimensional features by alternating the execution of temporal and spatial convolution and temporal attention. (3) Multi-scale temporal information is combined and exploited in Three-stream Temporal Scale Module (3sTSM) to further enhance the performance. Integrating these modules gives a powerful feature extractor STSNet, which has multi-scale receptive fields across both spatial and temporal dimensions. In order to verify the effectiveness and progressiveness of our model, we execute experiments on Historical Climate Observation and Simulation Dataset. The results show that STSNet can simultaneously provide effective ENSO prediction for 16 months, with higher correlation and lower deviation compared with other deep learning models. ENSO forecasting (dpeaa)DE-He213 Spatiotemporal semantic (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Luo, Hailun aut Sang, Weiguang aut Sun, Kun aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2022), 6 vom: 09. Juli, Seite 6464-6480 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2022 number:6 day:09 month:07 pages:6464-6480 https://dx.doi.org/10.1007/s10489-022-03861-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2008 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_2188 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_2548 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 53 2022 6 09 07 6464-6480 |
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10.1007/s10489-022-03861-1 doi (DE-627)SPR049482963 (SPR)s10489-022-03861-1-e DE-627 ger DE-627 rakwb eng Zhao, Jiakun verfasserin aut Spatiotemporal semantic network for ENSO forecasting over long time horizon 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing economic and social adverse effects. Recently, deep learning models show great potential in this task. However, despite decades of efforts, predicting ENSO at lead times of more than one year remains a major challenge and most of these existing studies focus on the single channel of meteorological data, ignoring the spatial and temporal dependence of these factors. To capture the spatiotemporal information of meteorological factors as well as promote the skill of ENSO forecasting over longer time horizon, we propose an end-to-end deep learning model SpatioTemporal Semantic Network named STSNet, which consists of three main modules: (1) Geographic Semantic Enhancement Module (GSEM) distinguishes the geographic semantics of various latitudes and longitudes through a learnable adaptive weight matrix. (2) A novel SpatioTemporal Convolutional Module(STCM) is designed specially to extract the multidimensional features by alternating the execution of temporal and spatial convolution and temporal attention. (3) Multi-scale temporal information is combined and exploited in Three-stream Temporal Scale Module (3sTSM) to further enhance the performance. Integrating these modules gives a powerful feature extractor STSNet, which has multi-scale receptive fields across both spatial and temporal dimensions. In order to verify the effectiveness and progressiveness of our model, we execute experiments on Historical Climate Observation and Simulation Dataset. The results show that STSNet can simultaneously provide effective ENSO prediction for 16 months, with higher correlation and lower deviation compared with other deep learning models. ENSO forecasting (dpeaa)DE-He213 Spatiotemporal semantic (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Luo, Hailun aut Sang, Weiguang aut Sun, Kun aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2022), 6 vom: 09. Juli, Seite 6464-6480 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2022 number:6 day:09 month:07 pages:6464-6480 https://dx.doi.org/10.1007/s10489-022-03861-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2008 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_2188 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_2548 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 53 2022 6 09 07 6464-6480 |
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10.1007/s10489-022-03861-1 doi (DE-627)SPR049482963 (SPR)s10489-022-03861-1-e DE-627 ger DE-627 rakwb eng Zhao, Jiakun verfasserin aut Spatiotemporal semantic network for ENSO forecasting over long time horizon 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing economic and social adverse effects. Recently, deep learning models show great potential in this task. However, despite decades of efforts, predicting ENSO at lead times of more than one year remains a major challenge and most of these existing studies focus on the single channel of meteorological data, ignoring the spatial and temporal dependence of these factors. To capture the spatiotemporal information of meteorological factors as well as promote the skill of ENSO forecasting over longer time horizon, we propose an end-to-end deep learning model SpatioTemporal Semantic Network named STSNet, which consists of three main modules: (1) Geographic Semantic Enhancement Module (GSEM) distinguishes the geographic semantics of various latitudes and longitudes through a learnable adaptive weight matrix. (2) A novel SpatioTemporal Convolutional Module(STCM) is designed specially to extract the multidimensional features by alternating the execution of temporal and spatial convolution and temporal attention. (3) Multi-scale temporal information is combined and exploited in Three-stream Temporal Scale Module (3sTSM) to further enhance the performance. Integrating these modules gives a powerful feature extractor STSNet, which has multi-scale receptive fields across both spatial and temporal dimensions. In order to verify the effectiveness and progressiveness of our model, we execute experiments on Historical Climate Observation and Simulation Dataset. The results show that STSNet can simultaneously provide effective ENSO prediction for 16 months, with higher correlation and lower deviation compared with other deep learning models. ENSO forecasting (dpeaa)DE-He213 Spatiotemporal semantic (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Luo, Hailun aut Sang, Weiguang aut Sun, Kun aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2022), 6 vom: 09. Juli, Seite 6464-6480 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2022 number:6 day:09 month:07 pages:6464-6480 https://dx.doi.org/10.1007/s10489-022-03861-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2008 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_2188 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_2548 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 53 2022 6 09 07 6464-6480 |
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10.1007/s10489-022-03861-1 doi (DE-627)SPR049482963 (SPR)s10489-022-03861-1-e DE-627 ger DE-627 rakwb eng Zhao, Jiakun verfasserin aut Spatiotemporal semantic network for ENSO forecasting over long time horizon 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing economic and social adverse effects. Recently, deep learning models show great potential in this task. However, despite decades of efforts, predicting ENSO at lead times of more than one year remains a major challenge and most of these existing studies focus on the single channel of meteorological data, ignoring the spatial and temporal dependence of these factors. To capture the spatiotemporal information of meteorological factors as well as promote the skill of ENSO forecasting over longer time horizon, we propose an end-to-end deep learning model SpatioTemporal Semantic Network named STSNet, which consists of three main modules: (1) Geographic Semantic Enhancement Module (GSEM) distinguishes the geographic semantics of various latitudes and longitudes through a learnable adaptive weight matrix. (2) A novel SpatioTemporal Convolutional Module(STCM) is designed specially to extract the multidimensional features by alternating the execution of temporal and spatial convolution and temporal attention. (3) Multi-scale temporal information is combined and exploited in Three-stream Temporal Scale Module (3sTSM) to further enhance the performance. Integrating these modules gives a powerful feature extractor STSNet, which has multi-scale receptive fields across both spatial and temporal dimensions. In order to verify the effectiveness and progressiveness of our model, we execute experiments on Historical Climate Observation and Simulation Dataset. The results show that STSNet can simultaneously provide effective ENSO prediction for 16 months, with higher correlation and lower deviation compared with other deep learning models. ENSO forecasting (dpeaa)DE-He213 Spatiotemporal semantic (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Luo, Hailun aut Sang, Weiguang aut Sun, Kun aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2022), 6 vom: 09. Juli, Seite 6464-6480 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2022 number:6 day:09 month:07 pages:6464-6480 https://dx.doi.org/10.1007/s10489-022-03861-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 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_2008 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_2188 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_2548 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 53 2022 6 09 07 6464-6480 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR049482963</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230510064119.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-022-03861-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049482963</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10489-022-03861-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhao, Jiakun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Spatiotemporal semantic network for ENSO forecasting over long time horizon</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing economic and social adverse effects. Recently, deep learning models show great potential in this task. However, despite decades of efforts, predicting ENSO at lead times of more than one year remains a major challenge and most of these existing studies focus on the single channel of meteorological data, ignoring the spatial and temporal dependence of these factors. To capture the spatiotemporal information of meteorological factors as well as promote the skill of ENSO forecasting over longer time horizon, we propose an end-to-end deep learning model SpatioTemporal Semantic Network named STSNet, which consists of three main modules: (1) Geographic Semantic Enhancement Module (GSEM) distinguishes the geographic semantics of various latitudes and longitudes through a learnable adaptive weight matrix. (2) A novel SpatioTemporal Convolutional Module(STCM) is designed specially to extract the multidimensional features by alternating the execution of temporal and spatial convolution and temporal attention. (3) Multi-scale temporal information is combined and exploited in Three-stream Temporal Scale Module (3sTSM) to further enhance the performance. Integrating these modules gives a powerful feature extractor STSNet, which has multi-scale receptive fields across both spatial and temporal dimensions. In order to verify the effectiveness and progressiveness of our model, we execute experiments on Historical Climate Observation and Simulation Dataset. The results show that STSNet can simultaneously provide effective ENSO prediction for 16 months, with higher correlation and lower deviation compared with other deep learning models.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ENSO forecasting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spatiotemporal semantic</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Hailun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sang, Weiguang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Kun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991</subfield><subfield code="g">53(2022), 6 vom: 09. Juli, Seite 6464-6480</subfield><subfield code="w">(DE-627)271180919</subfield><subfield code="w">(DE-600)1479519-X</subfield><subfield code="x">1573-7497</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:53</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:6</subfield><subfield code="g">day:09</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:6464-6480</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10489-022-03861-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield 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spatiotemporal semantic network for enso forecasting over long time horizon |
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Spatiotemporal semantic network for ENSO forecasting over long time horizon |
| abstract |
Abstract El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing economic and social adverse effects. Recently, deep learning models show great potential in this task. However, despite decades of efforts, predicting ENSO at lead times of more than one year remains a major challenge and most of these existing studies focus on the single channel of meteorological data, ignoring the spatial and temporal dependence of these factors. To capture the spatiotemporal information of meteorological factors as well as promote the skill of ENSO forecasting over longer time horizon, we propose an end-to-end deep learning model SpatioTemporal Semantic Network named STSNet, which consists of three main modules: (1) Geographic Semantic Enhancement Module (GSEM) distinguishes the geographic semantics of various latitudes and longitudes through a learnable adaptive weight matrix. (2) A novel SpatioTemporal Convolutional Module(STCM) is designed specially to extract the multidimensional features by alternating the execution of temporal and spatial convolution and temporal attention. (3) Multi-scale temporal information is combined and exploited in Three-stream Temporal Scale Module (3sTSM) to further enhance the performance. Integrating these modules gives a powerful feature extractor STSNet, which has multi-scale receptive fields across both spatial and temporal dimensions. In order to verify the effectiveness and progressiveness of our model, we execute experiments on Historical Climate Observation and Simulation Dataset. The results show that STSNet can simultaneously provide effective ENSO prediction for 16 months, with higher correlation and lower deviation compared with other deep learning models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing economic and social adverse effects. Recently, deep learning models show great potential in this task. However, despite decades of efforts, predicting ENSO at lead times of more than one year remains a major challenge and most of these existing studies focus on the single channel of meteorological data, ignoring the spatial and temporal dependence of these factors. To capture the spatiotemporal information of meteorological factors as well as promote the skill of ENSO forecasting over longer time horizon, we propose an end-to-end deep learning model SpatioTemporal Semantic Network named STSNet, which consists of three main modules: (1) Geographic Semantic Enhancement Module (GSEM) distinguishes the geographic semantics of various latitudes and longitudes through a learnable adaptive weight matrix. (2) A novel SpatioTemporal Convolutional Module(STCM) is designed specially to extract the multidimensional features by alternating the execution of temporal and spatial convolution and temporal attention. (3) Multi-scale temporal information is combined and exploited in Three-stream Temporal Scale Module (3sTSM) to further enhance the performance. Integrating these modules gives a powerful feature extractor STSNet, which has multi-scale receptive fields across both spatial and temporal dimensions. In order to verify the effectiveness and progressiveness of our model, we execute experiments on Historical Climate Observation and Simulation Dataset. The results show that STSNet can simultaneously provide effective ENSO prediction for 16 months, with higher correlation and lower deviation compared with other deep learning models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract El Niño-Southern Oscillation (ENSO) has substantial influence on global climate variability and is responsible for extreme weather events such as drought and heavy rains, along with global ecosystem modifications. The successful prediction of ENSO is of considerable interest to reducing economic and social adverse effects. Recently, deep learning models show great potential in this task. However, despite decades of efforts, predicting ENSO at lead times of more than one year remains a major challenge and most of these existing studies focus on the single channel of meteorological data, ignoring the spatial and temporal dependence of these factors. To capture the spatiotemporal information of meteorological factors as well as promote the skill of ENSO forecasting over longer time horizon, we propose an end-to-end deep learning model SpatioTemporal Semantic Network named STSNet, which consists of three main modules: (1) Geographic Semantic Enhancement Module (GSEM) distinguishes the geographic semantics of various latitudes and longitudes through a learnable adaptive weight matrix. (2) A novel SpatioTemporal Convolutional Module(STCM) is designed specially to extract the multidimensional features by alternating the execution of temporal and spatial convolution and temporal attention. (3) Multi-scale temporal information is combined and exploited in Three-stream Temporal Scale Module (3sTSM) to further enhance the performance. Integrating these modules gives a powerful feature extractor STSNet, which has multi-scale receptive fields across both spatial and temporal dimensions. In order to verify the effectiveness and progressiveness of our model, we execute experiments on Historical Climate Observation and Simulation Dataset. The results show that STSNet can simultaneously provide effective ENSO prediction for 16 months, with higher correlation and lower deviation compared with other deep learning models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Spatiotemporal semantic network for ENSO forecasting over long time horizon |
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https://dx.doi.org/10.1007/s10489-022-03861-1 |
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Luo, Hailun Sang, Weiguang Sun, Kun |
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10.1007/s10489-022-03861-1 |
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| score |
7.3985558 |