Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster
Abstract Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this resear...
Ausführliche Beschreibung
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| Autor*in: |
Afan, Haitham Abdulmohsin [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 Nature B.V. 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Natural hazards - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988, 112(2022), 2 vom: 18. Feb., Seite 1527-1545 |
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| Übergeordnetes Werk: |
volume:112 ; year:2022 ; number:2 ; day:18 ; month:02 ; pages:1527-1545 |
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| DOI / URN: |
10.1007/s11069-022-05237-7 |
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SPR047047410 |
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| 245 | 1 | 0 | |a Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
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| 520 | |a Abstract Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep learning algorithms. These sampling approaches are linear and stratified selection in deep learning algorithms. This investigation has been performed on the Tigris River data set in terms of 2 scenarios. The first scenario: implementation of 12 different linear and stratified sampling selection in deep learning models. This scenario is trained and tested as much as a number of months per year—12 months. The second scenario: the complete time series is taken into consideration while performing the two approaches that are utilized in this research. Furthermore, the optimal input combination is identified via correlation analysis. To evaluate the performance of the algorithms utilized in this research, a number of metrics have been used which are Root Mean Square Error RMSE, Absolute Error AE, Relative Error RE, Relative Error Lenient REL, Relative Error Strict RES, Root Relative Squared Error RRSE, Coefficient of determination R2, Spearman rho and Kendall tau. The results have indicated that in both scenarios, stratified-deep learning (SDL) improves the accuracy by about 7.96–94.6 with respect to several assessment criteria. Thus, finally, it is worth mentioning that SDL outperforms Linear-deep learning (LDL) in monthly streamflow modelling. | ||
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| 700 | 1 | |a El-Shafie, Ahmed |4 aut | |
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10.1007/s11069-022-05237-7 doi (DE-627)SPR047047410 (SPR)s11069-022-05237-7-e DE-627 ger DE-627 rakwb eng Afan, Haitham Abdulmohsin verfasserin aut Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep learning algorithms. These sampling approaches are linear and stratified selection in deep learning algorithms. This investigation has been performed on the Tigris River data set in terms of 2 scenarios. The first scenario: implementation of 12 different linear and stratified sampling selection in deep learning models. This scenario is trained and tested as much as a number of months per year—12 months. The second scenario: the complete time series is taken into consideration while performing the two approaches that are utilized in this research. Furthermore, the optimal input combination is identified via correlation analysis. To evaluate the performance of the algorithms utilized in this research, a number of metrics have been used which are Root Mean Square Error RMSE, Absolute Error AE, Relative Error RE, Relative Error Lenient REL, Relative Error Strict RES, Root Relative Squared Error RRSE, Coefficient of determination R2, Spearman rho and Kendall tau. The results have indicated that in both scenarios, stratified-deep learning (SDL) improves the accuracy by about 7.96–94.6 with respect to several assessment criteria. Thus, finally, it is worth mentioning that SDL outperforms Linear-deep learning (LDL) in monthly streamflow modelling. Deep learning (dpeaa)DE-He213 Linear sampling selection (dpeaa)DE-He213 Stratified sampling selection (dpeaa)DE-He213 Yafouz, Ayman (orcid)0000-0002-0932-1295 aut Birima, Ahmed H. aut Ahmed, Ali Najah aut Kisi, Ozgur aut Chaplot, Barkha aut El-Shafie, Ahmed aut Enthalten in Natural hazards Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 112(2022), 2 vom: 18. Feb., Seite 1527-1545 (DE-627)315621729 (DE-600)2017806-2 1573-0840 nnns volume:112 year:2022 number:2 day:18 month:02 pages:1527-1545 https://dx.doi.org/10.1007/s11069-022-05237-7 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_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_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 112 2022 2 18 02 1527-1545 |
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10.1007/s11069-022-05237-7 doi (DE-627)SPR047047410 (SPR)s11069-022-05237-7-e DE-627 ger DE-627 rakwb eng Afan, Haitham Abdulmohsin verfasserin aut Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep learning algorithms. These sampling approaches are linear and stratified selection in deep learning algorithms. This investigation has been performed on the Tigris River data set in terms of 2 scenarios. The first scenario: implementation of 12 different linear and stratified sampling selection in deep learning models. This scenario is trained and tested as much as a number of months per year—12 months. The second scenario: the complete time series is taken into consideration while performing the two approaches that are utilized in this research. Furthermore, the optimal input combination is identified via correlation analysis. To evaluate the performance of the algorithms utilized in this research, a number of metrics have been used which are Root Mean Square Error RMSE, Absolute Error AE, Relative Error RE, Relative Error Lenient REL, Relative Error Strict RES, Root Relative Squared Error RRSE, Coefficient of determination R2, Spearman rho and Kendall tau. The results have indicated that in both scenarios, stratified-deep learning (SDL) improves the accuracy by about 7.96–94.6 with respect to several assessment criteria. Thus, finally, it is worth mentioning that SDL outperforms Linear-deep learning (LDL) in monthly streamflow modelling. Deep learning (dpeaa)DE-He213 Linear sampling selection (dpeaa)DE-He213 Stratified sampling selection (dpeaa)DE-He213 Yafouz, Ayman (orcid)0000-0002-0932-1295 aut Birima, Ahmed H. aut Ahmed, Ali Najah aut Kisi, Ozgur aut Chaplot, Barkha aut El-Shafie, Ahmed aut Enthalten in Natural hazards Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 112(2022), 2 vom: 18. Feb., Seite 1527-1545 (DE-627)315621729 (DE-600)2017806-2 1573-0840 nnns volume:112 year:2022 number:2 day:18 month:02 pages:1527-1545 https://dx.doi.org/10.1007/s11069-022-05237-7 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_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_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 112 2022 2 18 02 1527-1545 |
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10.1007/s11069-022-05237-7 doi (DE-627)SPR047047410 (SPR)s11069-022-05237-7-e DE-627 ger DE-627 rakwb eng Afan, Haitham Abdulmohsin verfasserin aut Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep learning algorithms. These sampling approaches are linear and stratified selection in deep learning algorithms. This investigation has been performed on the Tigris River data set in terms of 2 scenarios. The first scenario: implementation of 12 different linear and stratified sampling selection in deep learning models. This scenario is trained and tested as much as a number of months per year—12 months. The second scenario: the complete time series is taken into consideration while performing the two approaches that are utilized in this research. Furthermore, the optimal input combination is identified via correlation analysis. To evaluate the performance of the algorithms utilized in this research, a number of metrics have been used which are Root Mean Square Error RMSE, Absolute Error AE, Relative Error RE, Relative Error Lenient REL, Relative Error Strict RES, Root Relative Squared Error RRSE, Coefficient of determination R2, Spearman rho and Kendall tau. The results have indicated that in both scenarios, stratified-deep learning (SDL) improves the accuracy by about 7.96–94.6 with respect to several assessment criteria. Thus, finally, it is worth mentioning that SDL outperforms Linear-deep learning (LDL) in monthly streamflow modelling. Deep learning (dpeaa)DE-He213 Linear sampling selection (dpeaa)DE-He213 Stratified sampling selection (dpeaa)DE-He213 Yafouz, Ayman (orcid)0000-0002-0932-1295 aut Birima, Ahmed H. aut Ahmed, Ali Najah aut Kisi, Ozgur aut Chaplot, Barkha aut El-Shafie, Ahmed aut Enthalten in Natural hazards Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 112(2022), 2 vom: 18. Feb., Seite 1527-1545 (DE-627)315621729 (DE-600)2017806-2 1573-0840 nnns volume:112 year:2022 number:2 day:18 month:02 pages:1527-1545 https://dx.doi.org/10.1007/s11069-022-05237-7 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_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_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 112 2022 2 18 02 1527-1545 |
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10.1007/s11069-022-05237-7 doi (DE-627)SPR047047410 (SPR)s11069-022-05237-7-e DE-627 ger DE-627 rakwb eng Afan, Haitham Abdulmohsin verfasserin aut Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep learning algorithms. These sampling approaches are linear and stratified selection in deep learning algorithms. This investigation has been performed on the Tigris River data set in terms of 2 scenarios. The first scenario: implementation of 12 different linear and stratified sampling selection in deep learning models. This scenario is trained and tested as much as a number of months per year—12 months. The second scenario: the complete time series is taken into consideration while performing the two approaches that are utilized in this research. Furthermore, the optimal input combination is identified via correlation analysis. To evaluate the performance of the algorithms utilized in this research, a number of metrics have been used which are Root Mean Square Error RMSE, Absolute Error AE, Relative Error RE, Relative Error Lenient REL, Relative Error Strict RES, Root Relative Squared Error RRSE, Coefficient of determination R2, Spearman rho and Kendall tau. The results have indicated that in both scenarios, stratified-deep learning (SDL) improves the accuracy by about 7.96–94.6 with respect to several assessment criteria. Thus, finally, it is worth mentioning that SDL outperforms Linear-deep learning (LDL) in monthly streamflow modelling. Deep learning (dpeaa)DE-He213 Linear sampling selection (dpeaa)DE-He213 Stratified sampling selection (dpeaa)DE-He213 Yafouz, Ayman (orcid)0000-0002-0932-1295 aut Birima, Ahmed H. aut Ahmed, Ali Najah aut Kisi, Ozgur aut Chaplot, Barkha aut El-Shafie, Ahmed aut Enthalten in Natural hazards Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 112(2022), 2 vom: 18. Feb., Seite 1527-1545 (DE-627)315621729 (DE-600)2017806-2 1573-0840 nnns volume:112 year:2022 number:2 day:18 month:02 pages:1527-1545 https://dx.doi.org/10.1007/s11069-022-05237-7 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_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_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 112 2022 2 18 02 1527-1545 |
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10.1007/s11069-022-05237-7 doi (DE-627)SPR047047410 (SPR)s11069-022-05237-7-e DE-627 ger DE-627 rakwb eng Afan, Haitham Abdulmohsin verfasserin aut Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep learning algorithms. These sampling approaches are linear and stratified selection in deep learning algorithms. This investigation has been performed on the Tigris River data set in terms of 2 scenarios. The first scenario: implementation of 12 different linear and stratified sampling selection in deep learning models. This scenario is trained and tested as much as a number of months per year—12 months. The second scenario: the complete time series is taken into consideration while performing the two approaches that are utilized in this research. Furthermore, the optimal input combination is identified via correlation analysis. To evaluate the performance of the algorithms utilized in this research, a number of metrics have been used which are Root Mean Square Error RMSE, Absolute Error AE, Relative Error RE, Relative Error Lenient REL, Relative Error Strict RES, Root Relative Squared Error RRSE, Coefficient of determination R2, Spearman rho and Kendall tau. The results have indicated that in both scenarios, stratified-deep learning (SDL) improves the accuracy by about 7.96–94.6 with respect to several assessment criteria. Thus, finally, it is worth mentioning that SDL outperforms Linear-deep learning (LDL) in monthly streamflow modelling. Deep learning (dpeaa)DE-He213 Linear sampling selection (dpeaa)DE-He213 Stratified sampling selection (dpeaa)DE-He213 Yafouz, Ayman (orcid)0000-0002-0932-1295 aut Birima, Ahmed H. aut Ahmed, Ali Najah aut Kisi, Ozgur aut Chaplot, Barkha aut El-Shafie, Ahmed aut Enthalten in Natural hazards Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 112(2022), 2 vom: 18. Feb., Seite 1527-1545 (DE-627)315621729 (DE-600)2017806-2 1573-0840 nnns volume:112 year:2022 number:2 day:18 month:02 pages:1527-1545 https://dx.doi.org/10.1007/s11069-022-05237-7 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_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_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 112 2022 2 18 02 1527-1545 |
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Afan, Haitham Abdulmohsin |
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linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
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Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
| abstract |
Abstract Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep learning algorithms. These sampling approaches are linear and stratified selection in deep learning algorithms. This investigation has been performed on the Tigris River data set in terms of 2 scenarios. The first scenario: implementation of 12 different linear and stratified sampling selection in deep learning models. This scenario is trained and tested as much as a number of months per year—12 months. The second scenario: the complete time series is taken into consideration while performing the two approaches that are utilized in this research. Furthermore, the optimal input combination is identified via correlation analysis. To evaluate the performance of the algorithms utilized in this research, a number of metrics have been used which are Root Mean Square Error RMSE, Absolute Error AE, Relative Error RE, Relative Error Lenient REL, Relative Error Strict RES, Root Relative Squared Error RRSE, Coefficient of determination R2, Spearman rho and Kendall tau. The results have indicated that in both scenarios, stratified-deep learning (SDL) improves the accuracy by about 7.96–94.6 with respect to several assessment criteria. Thus, finally, it is worth mentioning that SDL outperforms Linear-deep learning (LDL) in monthly streamflow modelling. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
| abstractGer |
Abstract Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep learning algorithms. These sampling approaches are linear and stratified selection in deep learning algorithms. This investigation has been performed on the Tigris River data set in terms of 2 scenarios. The first scenario: implementation of 12 different linear and stratified sampling selection in deep learning models. This scenario is trained and tested as much as a number of months per year—12 months. The second scenario: the complete time series is taken into consideration while performing the two approaches that are utilized in this research. Furthermore, the optimal input combination is identified via correlation analysis. To evaluate the performance of the algorithms utilized in this research, a number of metrics have been used which are Root Mean Square Error RMSE, Absolute Error AE, Relative Error RE, Relative Error Lenient REL, Relative Error Strict RES, Root Relative Squared Error RRSE, Coefficient of determination R2, Spearman rho and Kendall tau. The results have indicated that in both scenarios, stratified-deep learning (SDL) improves the accuracy by about 7.96–94.6 with respect to several assessment criteria. Thus, finally, it is worth mentioning that SDL outperforms Linear-deep learning (LDL) in monthly streamflow modelling. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
| abstract_unstemmed |
Abstract Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep learning algorithms. These sampling approaches are linear and stratified selection in deep learning algorithms. This investigation has been performed on the Tigris River data set in terms of 2 scenarios. The first scenario: implementation of 12 different linear and stratified sampling selection in deep learning models. This scenario is trained and tested as much as a number of months per year—12 months. The second scenario: the complete time series is taken into consideration while performing the two approaches that are utilized in this research. Furthermore, the optimal input combination is identified via correlation analysis. To evaluate the performance of the algorithms utilized in this research, a number of metrics have been used which are Root Mean Square Error RMSE, Absolute Error AE, Relative Error RE, Relative Error Lenient REL, Relative Error Strict RES, Root Relative Squared Error RRSE, Coefficient of determination R2, Spearman rho and Kendall tau. The results have indicated that in both scenarios, stratified-deep learning (SDL) improves the accuracy by about 7.96–94.6 with respect to several assessment criteria. Thus, finally, it is worth mentioning that SDL outperforms Linear-deep learning (LDL) in monthly streamflow modelling. © The Author(s), under exclusive licence to Springer Nature B.V. 2022 |
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Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
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https://dx.doi.org/10.1007/s11069-022-05237-7 |
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Yafouz, Ayman Birima, Ahmed H. Ahmed, Ali Najah Kisi, Ozgur Chaplot, Barkha El-Shafie, Ahmed |
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Yafouz, Ayman Birima, Ahmed H. Ahmed, Ali Najah Kisi, Ozgur Chaplot, Barkha El-Shafie, Ahmed |
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10.1007/s11069-022-05237-7 |
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2024-07-04T01:37:48.692Z |
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| score |
7.3998547 |