A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling

Soil temperature (Ts) plays an important role in earth sciences. The temporal and spatial variations of Ts are affected by several factors. To acquire the deterministic component and the stochastic component of time series data and further improve estimation performance, a multichanne long short-ter...
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Autor*in:	Qingliang Li [verfasserIn] Yang Zhao [verfasserIn] Fanhua Yu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Machine learning soil temperature modeling long short-term memory autoregressive integrated moving average

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 182026-182043
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:182026-182043

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3028995

Katalog-ID:	DOAJ05585527X

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520			\|a Soil temperature (Ts) plays an important role in earth sciences. The temporal and spatial variations of Ts are affected by several factors. To acquire the deterministic component and the stochastic component of time series data and further improve estimation performance, a multichanne long short-term memory model (MC-LSTM) is developed to address the challenge of accurate Ts estimation. Specifically, we connect multiple channels in an LSTM structure and an autoregressive integrated moving average model (ARIMA) directly to the output layer to characterize the deterministic part and the stochastic component of time series data. Meanwhile, to improve the correlations in the time series by increasing the number of memory units, we also connect the units in the different steps directly to the output layer in the LSTM structure to learn the long-term pattern of historical Ts by connecting the unit in an earlier step directly to the output layer, to learn the features that occurred in a short-term pattern by connecting the unit to the later step captures. In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R<sup<2</sup<. As expected, the proposed model achieves the highest relative R<sup<2</sup< values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. Consequently, the proposed model can serve as an alternative approach for Ts profile estimation.
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allfields	10.1109/ACCESS.2020.3028995 doi (DE-627)DOAJ05585527X (DE-599)DOAJ1aaca823f2aa4efe84791b2bd2b91450 DE-627 ger DE-627 rakwb eng TK1-9971 Qingliang Li verfasserin aut A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Soil temperature (Ts) plays an important role in earth sciences. The temporal and spatial variations of Ts are affected by several factors. To acquire the deterministic component and the stochastic component of time series data and further improve estimation performance, a multichanne long short-term memory model (MC-LSTM) is developed to address the challenge of accurate Ts estimation. Specifically, we connect multiple channels in an LSTM structure and an autoregressive integrated moving average model (ARIMA) directly to the output layer to characterize the deterministic part and the stochastic component of time series data. Meanwhile, to improve the correlations in the time series by increasing the number of memory units, we also connect the units in the different steps directly to the output layer in the LSTM structure to learn the long-term pattern of historical Ts by connecting the unit in an earlier step directly to the output layer, to learn the features that occurred in a short-term pattern by connecting the unit to the later step captures. In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R<sup<2</sup<. As expected, the proposed model achieves the highest relative R<sup<2</sup< values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. Consequently, the proposed model can serve as an alternative approach for Ts profile estimation. Machine learning soil temperature modeling long short-term memory autoregressive integrated moving average Electrical engineering. Electronics. Nuclear engineering Yang Zhao verfasserin aut Fanhua Yu verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 182026-182043 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:182026-182043 https://doi.org/10.1109/ACCESS.2020.3028995 kostenfrei https://doaj.org/article/1aaca823f2aa4efe84791b2bd2b91450 kostenfrei https://ieeexplore.ieee.org/document/9214479/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 182026-182043
spelling	10.1109/ACCESS.2020.3028995 doi (DE-627)DOAJ05585527X (DE-599)DOAJ1aaca823f2aa4efe84791b2bd2b91450 DE-627 ger DE-627 rakwb eng TK1-9971 Qingliang Li verfasserin aut A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Soil temperature (Ts) plays an important role in earth sciences. The temporal and spatial variations of Ts are affected by several factors. To acquire the deterministic component and the stochastic component of time series data and further improve estimation performance, a multichanne long short-term memory model (MC-LSTM) is developed to address the challenge of accurate Ts estimation. Specifically, we connect multiple channels in an LSTM structure and an autoregressive integrated moving average model (ARIMA) directly to the output layer to characterize the deterministic part and the stochastic component of time series data. Meanwhile, to improve the correlations in the time series by increasing the number of memory units, we also connect the units in the different steps directly to the output layer in the LSTM structure to learn the long-term pattern of historical Ts by connecting the unit in an earlier step directly to the output layer, to learn the features that occurred in a short-term pattern by connecting the unit to the later step captures. In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R<sup<2</sup<. As expected, the proposed model achieves the highest relative R<sup<2</sup< values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. Consequently, the proposed model can serve as an alternative approach for Ts profile estimation. Machine learning soil temperature modeling long short-term memory autoregressive integrated moving average Electrical engineering. Electronics. Nuclear engineering Yang Zhao verfasserin aut Fanhua Yu verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 182026-182043 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:182026-182043 https://doi.org/10.1109/ACCESS.2020.3028995 kostenfrei https://doaj.org/article/1aaca823f2aa4efe84791b2bd2b91450 kostenfrei https://ieeexplore.ieee.org/document/9214479/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 182026-182043
allfields_unstemmed	10.1109/ACCESS.2020.3028995 doi (DE-627)DOAJ05585527X (DE-599)DOAJ1aaca823f2aa4efe84791b2bd2b91450 DE-627 ger DE-627 rakwb eng TK1-9971 Qingliang Li verfasserin aut A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Soil temperature (Ts) plays an important role in earth sciences. The temporal and spatial variations of Ts are affected by several factors. To acquire the deterministic component and the stochastic component of time series data and further improve estimation performance, a multichanne long short-term memory model (MC-LSTM) is developed to address the challenge of accurate Ts estimation. Specifically, we connect multiple channels in an LSTM structure and an autoregressive integrated moving average model (ARIMA) directly to the output layer to characterize the deterministic part and the stochastic component of time series data. Meanwhile, to improve the correlations in the time series by increasing the number of memory units, we also connect the units in the different steps directly to the output layer in the LSTM structure to learn the long-term pattern of historical Ts by connecting the unit in an earlier step directly to the output layer, to learn the features that occurred in a short-term pattern by connecting the unit to the later step captures. In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R<sup<2</sup<. As expected, the proposed model achieves the highest relative R<sup<2</sup< values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. Consequently, the proposed model can serve as an alternative approach for Ts profile estimation. Machine learning soil temperature modeling long short-term memory autoregressive integrated moving average Electrical engineering. Electronics. Nuclear engineering Yang Zhao verfasserin aut Fanhua Yu verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 182026-182043 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:182026-182043 https://doi.org/10.1109/ACCESS.2020.3028995 kostenfrei https://doaj.org/article/1aaca823f2aa4efe84791b2bd2b91450 kostenfrei https://ieeexplore.ieee.org/document/9214479/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 182026-182043
allfieldsGer	10.1109/ACCESS.2020.3028995 doi (DE-627)DOAJ05585527X (DE-599)DOAJ1aaca823f2aa4efe84791b2bd2b91450 DE-627 ger DE-627 rakwb eng TK1-9971 Qingliang Li verfasserin aut A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Soil temperature (Ts) plays an important role in earth sciences. The temporal and spatial variations of Ts are affected by several factors. To acquire the deterministic component and the stochastic component of time series data and further improve estimation performance, a multichanne long short-term memory model (MC-LSTM) is developed to address the challenge of accurate Ts estimation. Specifically, we connect multiple channels in an LSTM structure and an autoregressive integrated moving average model (ARIMA) directly to the output layer to characterize the deterministic part and the stochastic component of time series data. Meanwhile, to improve the correlations in the time series by increasing the number of memory units, we also connect the units in the different steps directly to the output layer in the LSTM structure to learn the long-term pattern of historical Ts by connecting the unit in an earlier step directly to the output layer, to learn the features that occurred in a short-term pattern by connecting the unit to the later step captures. In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R<sup<2</sup<. As expected, the proposed model achieves the highest relative R<sup<2</sup< values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. Consequently, the proposed model can serve as an alternative approach for Ts profile estimation. Machine learning soil temperature modeling long short-term memory autoregressive integrated moving average Electrical engineering. Electronics. Nuclear engineering Yang Zhao verfasserin aut Fanhua Yu verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 182026-182043 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:182026-182043 https://doi.org/10.1109/ACCESS.2020.3028995 kostenfrei https://doaj.org/article/1aaca823f2aa4efe84791b2bd2b91450 kostenfrei https://ieeexplore.ieee.org/document/9214479/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 182026-182043
allfieldsSound	10.1109/ACCESS.2020.3028995 doi (DE-627)DOAJ05585527X (DE-599)DOAJ1aaca823f2aa4efe84791b2bd2b91450 DE-627 ger DE-627 rakwb eng TK1-9971 Qingliang Li verfasserin aut A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Soil temperature (Ts) plays an important role in earth sciences. The temporal and spatial variations of Ts are affected by several factors. To acquire the deterministic component and the stochastic component of time series data and further improve estimation performance, a multichanne long short-term memory model (MC-LSTM) is developed to address the challenge of accurate Ts estimation. Specifically, we connect multiple channels in an LSTM structure and an autoregressive integrated moving average model (ARIMA) directly to the output layer to characterize the deterministic part and the stochastic component of time series data. Meanwhile, to improve the correlations in the time series by increasing the number of memory units, we also connect the units in the different steps directly to the output layer in the LSTM structure to learn the long-term pattern of historical Ts by connecting the unit in an earlier step directly to the output layer, to learn the features that occurred in a short-term pattern by connecting the unit to the later step captures. In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R<sup<2</sup<. As expected, the proposed model achieves the highest relative R<sup<2</sup< values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. Consequently, the proposed model can serve as an alternative approach for Ts profile estimation. Machine learning soil temperature modeling long short-term memory autoregressive integrated moving average Electrical engineering. Electronics. Nuclear engineering Yang Zhao verfasserin aut Fanhua Yu verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 182026-182043 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:182026-182043 https://doi.org/10.1109/ACCESS.2020.3028995 kostenfrei https://doaj.org/article/1aaca823f2aa4efe84791b2bd2b91450 kostenfrei https://ieeexplore.ieee.org/document/9214479/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 182026-182043
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In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R<sup<2</sup<. As expected, the proposed model achieves the highest relative R<sup<2</sup< values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. 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callnumber-first	T - Technology
author	Qingliang Li
spellingShingle	Qingliang Li misc TK1-9971 misc Machine learning misc soil temperature modeling misc long short-term memory misc autoregressive integrated moving average misc Electrical engineering. Electronics. Nuclear engineering A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling
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topic_title	TK1-9971 A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling Machine learning soil temperature modeling long short-term memory autoregressive integrated moving average
topic	misc TK1-9971 misc Machine learning misc soil temperature modeling misc long short-term memory misc autoregressive integrated moving average misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Machine learning misc soil temperature modeling misc long short-term memory misc autoregressive integrated moving average misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Machine learning misc soil temperature modeling misc long short-term memory misc autoregressive integrated moving average misc Electrical engineering. Electronics. Nuclear engineering
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title	A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling
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title_full	A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling
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title_sort	novel multichannel long short-term memory method with time series for soil temperature modeling
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title_auth	A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling
abstract	Soil temperature (Ts) plays an important role in earth sciences. The temporal and spatial variations of Ts are affected by several factors. To acquire the deterministic component and the stochastic component of time series data and further improve estimation performance, a multichanne long short-term memory model (MC-LSTM) is developed to address the challenge of accurate Ts estimation. Specifically, we connect multiple channels in an LSTM structure and an autoregressive integrated moving average model (ARIMA) directly to the output layer to characterize the deterministic part and the stochastic component of time series data. Meanwhile, to improve the correlations in the time series by increasing the number of memory units, we also connect the units in the different steps directly to the output layer in the LSTM structure to learn the long-term pattern of historical Ts by connecting the unit in an earlier step directly to the output layer, to learn the features that occurred in a short-term pattern by connecting the unit to the later step captures. In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R<sup<2</sup<. As expected, the proposed model achieves the highest relative R<sup<2</sup< values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. Consequently, the proposed model can serve as an alternative approach for Ts profile estimation.
abstractGer	Soil temperature (Ts) plays an important role in earth sciences. The temporal and spatial variations of Ts are affected by several factors. To acquire the deterministic component and the stochastic component of time series data and further improve estimation performance, a multichanne long short-term memory model (MC-LSTM) is developed to address the challenge of accurate Ts estimation. Specifically, we connect multiple channels in an LSTM structure and an autoregressive integrated moving average model (ARIMA) directly to the output layer to characterize the deterministic part and the stochastic component of time series data. Meanwhile, to improve the correlations in the time series by increasing the number of memory units, we also connect the units in the different steps directly to the output layer in the LSTM structure to learn the long-term pattern of historical Ts by connecting the unit in an earlier step directly to the output layer, to learn the features that occurred in a short-term pattern by connecting the unit to the later step captures. In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R<sup<2</sup<. As expected, the proposed model achieves the highest relative R<sup<2</sup< values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. Consequently, the proposed model can serve as an alternative approach for Ts profile estimation.
abstract_unstemmed	Soil temperature (Ts) plays an important role in earth sciences. The temporal and spatial variations of Ts are affected by several factors. To acquire the deterministic component and the stochastic component of time series data and further improve estimation performance, a multichanne long short-term memory model (MC-LSTM) is developed to address the challenge of accurate Ts estimation. Specifically, we connect multiple channels in an LSTM structure and an autoregressive integrated moving average model (ARIMA) directly to the output layer to characterize the deterministic part and the stochastic component of time series data. Meanwhile, to improve the correlations in the time series by increasing the number of memory units, we also connect the units in the different steps directly to the output layer in the LSTM structure to learn the long-term pattern of historical Ts by connecting the unit in an earlier step directly to the output layer, to learn the features that occurred in a short-term pattern by connecting the unit to the later step captures. In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R<sup<2</sup<. As expected, the proposed model achieves the highest relative R<sup<2</sup< values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. Consequently, the proposed model can serve as an alternative approach for Ts profile estimation.
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title_short	A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling
url	https://doi.org/10.1109/ACCESS.2020.3028995 https://doaj.org/article/1aaca823f2aa4efe84791b2bd2b91450 https://ieeexplore.ieee.org/document/9214479/ https://doaj.org/toc/2169-3536
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up_date	2024-07-03T17:25:51.791Z
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In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R<sup<2</sup<. As expected, the proposed model achieves the highest relative R<sup<2</sup< values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. 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A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling

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