Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models

Abstract Nowadays, the use of artificial intelligence is extended to various scientific and engineering fields including water management and planning. This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reser...
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Autor*in:	Pishgah Hadiyan, Pedram [verfasserIn] Moeini, Ramtin Ehsanzadeh, Eghbal Karvanpour, Monire

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Dynamic artificial neural networks Dam inflow Trend analysis Sefidruod reservoir

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature B.V. 2022

Übergeordnetes Werk:	Enthalten in: Water resources management - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 36(2022), 8 vom: 27. Mai, Seite 2703-2723
Übergeordnetes Werk:	volume:36 ; year:2022 ; number:8 ; day:27 ; month:05 ; pages:2703-2723

Links:	Volltext

DOI / URN:	10.1007/s11269-022-03170-9

Katalog-ID:	SPR047265116

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520			\|a Abstract Nowadays, the use of artificial intelligence is extended to various scientific and engineering fields including water management and planning. This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reservoir (Iran). For this purpose, first, the discharge time series of tributaries of the Sefidruod dam were analyzed for trends for a 47 year time period (1967 to 2014) using parametric regression and non-parametric Mann–Kendall tests considering independence, short-term, and long-term persistence assumptions. Also, the homogeneity of the data was investigated using three statistical tests including Cumulative Deviations, Worsley's Likelihood Ratio, and Bayesian inference. Then, the inflow discharges into the reservoir of Sefidruod dam from GhezelOzan and Shahroud tributaries were simulated using dynamic Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive with exogenous input (NARX) models. Further, water inflow values of both rivers were predicted for the next 5 years in future using dynamic NAR and NARX models. Finally, the simulated results were tested for trends. Obtained results showed a significant decreasing trend in both rivers. Results also showed a continuous downward trend for the following 5-year period predicted by NAR and NARX models. In addition, it was found that the results obtained by the NARX model were less accurate compared to those by the NAR model.
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912			\|a GBV_ILN_281
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allfields	10.1007/s11269-022-03170-9 doi (DE-627)SPR047265116 (SPR)s11269-022-03170-9-e DE-627 ger DE-627 rakwb eng Pishgah Hadiyan, Pedram verfasserin aut Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract Nowadays, the use of artificial intelligence is extended to various scientific and engineering fields including water management and planning. This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reservoir (Iran). For this purpose, first, the discharge time series of tributaries of the Sefidruod dam were analyzed for trends for a 47 year time period (1967 to 2014) using parametric regression and non-parametric Mann–Kendall tests considering independence, short-term, and long-term persistence assumptions. Also, the homogeneity of the data was investigated using three statistical tests including Cumulative Deviations, Worsley's Likelihood Ratio, and Bayesian inference. Then, the inflow discharges into the reservoir of Sefidruod dam from GhezelOzan and Shahroud tributaries were simulated using dynamic Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive with exogenous input (NARX) models. Further, water inflow values of both rivers were predicted for the next 5 years in future using dynamic NAR and NARX models. Finally, the simulated results were tested for trends. Obtained results showed a significant decreasing trend in both rivers. Results also showed a continuous downward trend for the following 5-year period predicted by NAR and NARX models. In addition, it was found that the results obtained by the NARX model were less accurate compared to those by the NAR model. Dynamic artificial neural networks (dpeaa)DE-He213 Dam inflow (dpeaa)DE-He213 Trend analysis (dpeaa)DE-He213 Sefidruod reservoir (dpeaa)DE-He213 Moeini, Ramtin aut Ehsanzadeh, Eghbal aut Karvanpour, Monire aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 36(2022), 8 vom: 27. Mai, Seite 2703-2723 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:36 year:2022 number:8 day:27 month:05 pages:2703-2723 https://dx.doi.org/10.1007/s11269-022-03170-9 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 36 2022 8 27 05 2703-2723
spelling	10.1007/s11269-022-03170-9 doi (DE-627)SPR047265116 (SPR)s11269-022-03170-9-e DE-627 ger DE-627 rakwb eng Pishgah Hadiyan, Pedram verfasserin aut Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract Nowadays, the use of artificial intelligence is extended to various scientific and engineering fields including water management and planning. This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reservoir (Iran). For this purpose, first, the discharge time series of tributaries of the Sefidruod dam were analyzed for trends for a 47 year time period (1967 to 2014) using parametric regression and non-parametric Mann–Kendall tests considering independence, short-term, and long-term persistence assumptions. Also, the homogeneity of the data was investigated using three statistical tests including Cumulative Deviations, Worsley's Likelihood Ratio, and Bayesian inference. Then, the inflow discharges into the reservoir of Sefidruod dam from GhezelOzan and Shahroud tributaries were simulated using dynamic Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive with exogenous input (NARX) models. Further, water inflow values of both rivers were predicted for the next 5 years in future using dynamic NAR and NARX models. Finally, the simulated results were tested for trends. Obtained results showed a significant decreasing trend in both rivers. Results also showed a continuous downward trend for the following 5-year period predicted by NAR and NARX models. In addition, it was found that the results obtained by the NARX model were less accurate compared to those by the NAR model. Dynamic artificial neural networks (dpeaa)DE-He213 Dam inflow (dpeaa)DE-He213 Trend analysis (dpeaa)DE-He213 Sefidruod reservoir (dpeaa)DE-He213 Moeini, Ramtin aut Ehsanzadeh, Eghbal aut Karvanpour, Monire aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 36(2022), 8 vom: 27. Mai, Seite 2703-2723 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:36 year:2022 number:8 day:27 month:05 pages:2703-2723 https://dx.doi.org/10.1007/s11269-022-03170-9 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 36 2022 8 27 05 2703-2723
allfields_unstemmed	10.1007/s11269-022-03170-9 doi (DE-627)SPR047265116 (SPR)s11269-022-03170-9-e DE-627 ger DE-627 rakwb eng Pishgah Hadiyan, Pedram verfasserin aut Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract Nowadays, the use of artificial intelligence is extended to various scientific and engineering fields including water management and planning. This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reservoir (Iran). For this purpose, first, the discharge time series of tributaries of the Sefidruod dam were analyzed for trends for a 47 year time period (1967 to 2014) using parametric regression and non-parametric Mann–Kendall tests considering independence, short-term, and long-term persistence assumptions. Also, the homogeneity of the data was investigated using three statistical tests including Cumulative Deviations, Worsley's Likelihood Ratio, and Bayesian inference. Then, the inflow discharges into the reservoir of Sefidruod dam from GhezelOzan and Shahroud tributaries were simulated using dynamic Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive with exogenous input (NARX) models. Further, water inflow values of both rivers were predicted for the next 5 years in future using dynamic NAR and NARX models. Finally, the simulated results were tested for trends. Obtained results showed a significant decreasing trend in both rivers. Results also showed a continuous downward trend for the following 5-year period predicted by NAR and NARX models. In addition, it was found that the results obtained by the NARX model were less accurate compared to those by the NAR model. Dynamic artificial neural networks (dpeaa)DE-He213 Dam inflow (dpeaa)DE-He213 Trend analysis (dpeaa)DE-He213 Sefidruod reservoir (dpeaa)DE-He213 Moeini, Ramtin aut Ehsanzadeh, Eghbal aut Karvanpour, Monire aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 36(2022), 8 vom: 27. Mai, Seite 2703-2723 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:36 year:2022 number:8 day:27 month:05 pages:2703-2723 https://dx.doi.org/10.1007/s11269-022-03170-9 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 36 2022 8 27 05 2703-2723
allfieldsGer	10.1007/s11269-022-03170-9 doi (DE-627)SPR047265116 (SPR)s11269-022-03170-9-e DE-627 ger DE-627 rakwb eng Pishgah Hadiyan, Pedram verfasserin aut Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract Nowadays, the use of artificial intelligence is extended to various scientific and engineering fields including water management and planning. This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reservoir (Iran). For this purpose, first, the discharge time series of tributaries of the Sefidruod dam were analyzed for trends for a 47 year time period (1967 to 2014) using parametric regression and non-parametric Mann–Kendall tests considering independence, short-term, and long-term persistence assumptions. Also, the homogeneity of the data was investigated using three statistical tests including Cumulative Deviations, Worsley's Likelihood Ratio, and Bayesian inference. Then, the inflow discharges into the reservoir of Sefidruod dam from GhezelOzan and Shahroud tributaries were simulated using dynamic Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive with exogenous input (NARX) models. Further, water inflow values of both rivers were predicted for the next 5 years in future using dynamic NAR and NARX models. Finally, the simulated results were tested for trends. Obtained results showed a significant decreasing trend in both rivers. Results also showed a continuous downward trend for the following 5-year period predicted by NAR and NARX models. In addition, it was found that the results obtained by the NARX model were less accurate compared to those by the NAR model. Dynamic artificial neural networks (dpeaa)DE-He213 Dam inflow (dpeaa)DE-He213 Trend analysis (dpeaa)DE-He213 Sefidruod reservoir (dpeaa)DE-He213 Moeini, Ramtin aut Ehsanzadeh, Eghbal aut Karvanpour, Monire aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 36(2022), 8 vom: 27. Mai, Seite 2703-2723 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:36 year:2022 number:8 day:27 month:05 pages:2703-2723 https://dx.doi.org/10.1007/s11269-022-03170-9 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 36 2022 8 27 05 2703-2723
allfieldsSound	10.1007/s11269-022-03170-9 doi (DE-627)SPR047265116 (SPR)s11269-022-03170-9-e DE-627 ger DE-627 rakwb eng Pishgah Hadiyan, Pedram verfasserin aut Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022 Abstract Nowadays, the use of artificial intelligence is extended to various scientific and engineering fields including water management and planning. This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reservoir (Iran). For this purpose, first, the discharge time series of tributaries of the Sefidruod dam were analyzed for trends for a 47 year time period (1967 to 2014) using parametric regression and non-parametric Mann–Kendall tests considering independence, short-term, and long-term persistence assumptions. Also, the homogeneity of the data was investigated using three statistical tests including Cumulative Deviations, Worsley's Likelihood Ratio, and Bayesian inference. Then, the inflow discharges into the reservoir of Sefidruod dam from GhezelOzan and Shahroud tributaries were simulated using dynamic Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive with exogenous input (NARX) models. Further, water inflow values of both rivers were predicted for the next 5 years in future using dynamic NAR and NARX models. Finally, the simulated results were tested for trends. Obtained results showed a significant decreasing trend in both rivers. Results also showed a continuous downward trend for the following 5-year period predicted by NAR and NARX models. In addition, it was found that the results obtained by the NARX model were less accurate compared to those by the NAR model. Dynamic artificial neural networks (dpeaa)DE-He213 Dam inflow (dpeaa)DE-He213 Trend analysis (dpeaa)DE-He213 Sefidruod reservoir (dpeaa)DE-He213 Moeini, Ramtin aut Ehsanzadeh, Eghbal aut Karvanpour, Monire aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 36(2022), 8 vom: 27. Mai, Seite 2703-2723 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:36 year:2022 number:8 day:27 month:05 pages:2703-2723 https://dx.doi.org/10.1007/s11269-022-03170-9 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 36 2022 8 27 05 2703-2723
language	English
source	Enthalten in Water resources management 36(2022), 8 vom: 27. Mai, Seite 2703-2723 volume:36 year:2022 number:8 day:27 month:05 pages:2703-2723
sourceStr	Enthalten in Water resources management 36(2022), 8 vom: 27. Mai, Seite 2703-2723 volume:36 year:2022 number:8 day:27 month:05 pages:2703-2723
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Dynamic artificial neural networks Dam inflow Trend analysis Sefidruod reservoir
isfreeaccess_bool	false
container_title	Water resources management
authorswithroles_txt_mv	Pishgah Hadiyan, Pedram @@aut@@ Moeini, Ramtin @@aut@@ Ehsanzadeh, Eghbal @@aut@@ Karvanpour, Monire @@aut@@
publishDateDaySort_date	2022-05-27T00:00:00Z
hierarchy_top_id	315299924
id	SPR047265116
language_de	englisch
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author	Pishgah Hadiyan, Pedram
spellingShingle	Pishgah Hadiyan, Pedram misc Dynamic artificial neural networks misc Dam inflow misc Trend analysis misc Sefidruod reservoir Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models
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topic_title	Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models Dynamic artificial neural networks (dpeaa)DE-He213 Dam inflow (dpeaa)DE-He213 Trend analysis (dpeaa)DE-He213 Sefidruod reservoir (dpeaa)DE-He213
topic	misc Dynamic artificial neural networks misc Dam inflow misc Trend analysis misc Sefidruod reservoir
topic_unstemmed	misc Dynamic artificial neural networks misc Dam inflow misc Trend analysis misc Sefidruod reservoir
topic_browse	misc Dynamic artificial neural networks misc Dam inflow misc Trend analysis misc Sefidruod reservoir
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title	Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models
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title_full	Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models
author_sort	Pishgah Hadiyan, Pedram
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author_browse	Pishgah Hadiyan, Pedram Moeini, Ramtin Ehsanzadeh, Eghbal Karvanpour, Monire
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doi_str_mv	10.1007/s11269-022-03170-9
title_sort	trend analysis of water inflow into the dam reservoirs under future conditions predicted by dynamic nar and narx models
title_auth	Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models
abstract	Abstract Nowadays, the use of artificial intelligence is extended to various scientific and engineering fields including water management and planning. This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reservoir (Iran). For this purpose, first, the discharge time series of tributaries of the Sefidruod dam were analyzed for trends for a 47 year time period (1967 to 2014) using parametric regression and non-parametric Mann–Kendall tests considering independence, short-term, and long-term persistence assumptions. Also, the homogeneity of the data was investigated using three statistical tests including Cumulative Deviations, Worsley's Likelihood Ratio, and Bayesian inference. Then, the inflow discharges into the reservoir of Sefidruod dam from GhezelOzan and Shahroud tributaries were simulated using dynamic Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive with exogenous input (NARX) models. Further, water inflow values of both rivers were predicted for the next 5 years in future using dynamic NAR and NARX models. Finally, the simulated results were tested for trends. Obtained results showed a significant decreasing trend in both rivers. Results also showed a continuous downward trend for the following 5-year period predicted by NAR and NARX models. In addition, it was found that the results obtained by the NARX model were less accurate compared to those by the NAR model. © The Author(s), under exclusive licence to Springer Nature B.V. 2022
abstractGer	Abstract Nowadays, the use of artificial intelligence is extended to various scientific and engineering fields including water management and planning. This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reservoir (Iran). For this purpose, first, the discharge time series of tributaries of the Sefidruod dam were analyzed for trends for a 47 year time period (1967 to 2014) using parametric regression and non-parametric Mann–Kendall tests considering independence, short-term, and long-term persistence assumptions. Also, the homogeneity of the data was investigated using three statistical tests including Cumulative Deviations, Worsley's Likelihood Ratio, and Bayesian inference. Then, the inflow discharges into the reservoir of Sefidruod dam from GhezelOzan and Shahroud tributaries were simulated using dynamic Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive with exogenous input (NARX) models. Further, water inflow values of both rivers were predicted for the next 5 years in future using dynamic NAR and NARX models. Finally, the simulated results were tested for trends. Obtained results showed a significant decreasing trend in both rivers. Results also showed a continuous downward trend for the following 5-year period predicted by NAR and NARX models. In addition, it was found that the results obtained by the NARX model were less accurate compared to those by the NAR model. © The Author(s), under exclusive licence to Springer Nature B.V. 2022
abstract_unstemmed	Abstract Nowadays, the use of artificial intelligence is extended to various scientific and engineering fields including water management and planning. This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reservoir (Iran). For this purpose, first, the discharge time series of tributaries of the Sefidruod dam were analyzed for trends for a 47 year time period (1967 to 2014) using parametric regression and non-parametric Mann–Kendall tests considering independence, short-term, and long-term persistence assumptions. Also, the homogeneity of the data was investigated using three statistical tests including Cumulative Deviations, Worsley's Likelihood Ratio, and Bayesian inference. Then, the inflow discharges into the reservoir of Sefidruod dam from GhezelOzan and Shahroud tributaries were simulated using dynamic Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive with exogenous input (NARX) models. Further, water inflow values of both rivers were predicted for the next 5 years in future using dynamic NAR and NARX models. Finally, the simulated results were tested for trends. Obtained results showed a significant decreasing trend in both rivers. Results also showed a continuous downward trend for the following 5-year period predicted by NAR and NARX models. In addition, it was found that the results obtained by the NARX model were less accurate compared to those by the NAR model. © The Author(s), under exclusive licence to Springer Nature B.V. 2022
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title_short	Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models
url	https://dx.doi.org/10.1007/s11269-022-03170-9
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author2	Moeini, Ramtin Ehsanzadeh, Eghbal Karvanpour, Monire
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up_date	2024-07-04T02:31:25.529Z
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This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reservoir (Iran). For this purpose, first, the discharge time series of tributaries of the Sefidruod dam were analyzed for trends for a 47 year time period (1967 to 2014) using parametric regression and non-parametric Mann–Kendall tests considering independence, short-term, and long-term persistence assumptions. Also, the homogeneity of the data was investigated using three statistical tests including Cumulative Deviations, Worsley's Likelihood Ratio, and Bayesian inference. Then, the inflow discharges into the reservoir of Sefidruod dam from GhezelOzan and Shahroud tributaries were simulated using dynamic Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive with exogenous input (NARX) models. Further, water inflow values of both rivers were predicted for the next 5 years in future using dynamic NAR and NARX models. Finally, the simulated results were tested for trends. Obtained results showed a significant decreasing trend in both rivers. Results also showed a continuous downward trend for the following 5-year period predicted by NAR and NARX models. In addition, it was found that the results obtained by the NARX model were less accurate compared to those by the NAR model.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dynamic artificial neural networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dam inflow</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Trend analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sefidruod reservoir</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Moeini, Ramtin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ehsanzadeh, Eghbal</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Karvanpour, Monire</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Water resources management</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987</subfield><subfield code="g">36(2022), 8 vom: 27. 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