Annual and Monthly Dam Inflow Prediction Using Bayesian Networks

Abstract Dam inflow prediction is important in terms of optimal water allocation and reduction of potential risks of floods and droughts. It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networ...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Noorbeh, Parisa [verfasserIn] Roozbahani, Abbas [verfasserIn] Kardan Moghaddam, Hamid [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Dam inflow prediction Bayesian networks Uncertainty Clustering Zayandehrud dam

Übergeordnetes Werk:	Enthalten in: Water resources management - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 34(2020), 9 vom: 17. Juni, Seite 2933-2951
Übergeordnetes Werk:	volume:34 ; year:2020 ; number:9 ; day:17 ; month:06 ; pages:2933-2951

Links:	Volltext

DOI / URN:	10.1007/s11269-020-02591-8

Katalog-ID:	SPR040389243

Internformat


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allfields	10.1007/s11269-020-02591-8 doi (DE-627)SPR040389243 (SPR)s11269-020-02591-8-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Noorbeh, Parisa verfasserin aut Annual and Monthly Dam Inflow Prediction Using Bayesian Networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dam inflow prediction is important in terms of optimal water allocation and reduction of potential risks of floods and droughts. It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networks (BNs) was used to evaluate its efficiency in predicting inflow into reservoirs considering the uncertainties. For this purpose, continuous BNs as well as integration of K-means clustering and discrete BNs were applied for predicting magnitude and range of inflows, respectively in terms of annual and monthly prediction scenarios. In this regard, the Zayandehrud Dam reservoir in Iran was selected to test this model. To achieve the best network structure in these scenarios, different patterns were defined based on the combination of predictors. According to the magnitude predictions, the MAPE and $ R^{2} $ indicators in annual model were respectively 21% and 0.62 and in monthly model were respectively 49% and 0.71. According to the results of the inflow range prediction, the prediction accuracy of the annual and monthly patterns was 75% and 83%, respectively. Modelling results showed that BN performs better in predicting the inflow range than its numerical prediction. The proposed model can improve the decision making of reservoirs operation. Dam inflow prediction (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Zayandehrud dam (dpeaa)DE-He213 Roozbahani, Abbas verfasserin aut Kardan Moghaddam, Hamid verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 34(2020), 9 vom: 17. Juni, Seite 2933-2951 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:34 year:2020 number:9 day:17 month:06 pages:2933-2951 https://dx.doi.org/10.1007/s11269-020-02591-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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 43.33 ASE AR 34 2020 9 17 06 2933-2951
spelling	10.1007/s11269-020-02591-8 doi (DE-627)SPR040389243 (SPR)s11269-020-02591-8-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Noorbeh, Parisa verfasserin aut Annual and Monthly Dam Inflow Prediction Using Bayesian Networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dam inflow prediction is important in terms of optimal water allocation and reduction of potential risks of floods and droughts. It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networks (BNs) was used to evaluate its efficiency in predicting inflow into reservoirs considering the uncertainties. For this purpose, continuous BNs as well as integration of K-means clustering and discrete BNs were applied for predicting magnitude and range of inflows, respectively in terms of annual and monthly prediction scenarios. In this regard, the Zayandehrud Dam reservoir in Iran was selected to test this model. To achieve the best network structure in these scenarios, different patterns were defined based on the combination of predictors. According to the magnitude predictions, the MAPE and $ R^{2} $ indicators in annual model were respectively 21% and 0.62 and in monthly model were respectively 49% and 0.71. According to the results of the inflow range prediction, the prediction accuracy of the annual and monthly patterns was 75% and 83%, respectively. Modelling results showed that BN performs better in predicting the inflow range than its numerical prediction. The proposed model can improve the decision making of reservoirs operation. Dam inflow prediction (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Zayandehrud dam (dpeaa)DE-He213 Roozbahani, Abbas verfasserin aut Kardan Moghaddam, Hamid verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 34(2020), 9 vom: 17. Juni, Seite 2933-2951 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:34 year:2020 number:9 day:17 month:06 pages:2933-2951 https://dx.doi.org/10.1007/s11269-020-02591-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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 43.33 ASE AR 34 2020 9 17 06 2933-2951
allfields_unstemmed	10.1007/s11269-020-02591-8 doi (DE-627)SPR040389243 (SPR)s11269-020-02591-8-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Noorbeh, Parisa verfasserin aut Annual and Monthly Dam Inflow Prediction Using Bayesian Networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dam inflow prediction is important in terms of optimal water allocation and reduction of potential risks of floods and droughts. It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networks (BNs) was used to evaluate its efficiency in predicting inflow into reservoirs considering the uncertainties. For this purpose, continuous BNs as well as integration of K-means clustering and discrete BNs were applied for predicting magnitude and range of inflows, respectively in terms of annual and monthly prediction scenarios. In this regard, the Zayandehrud Dam reservoir in Iran was selected to test this model. To achieve the best network structure in these scenarios, different patterns were defined based on the combination of predictors. According to the magnitude predictions, the MAPE and $ R^{2} $ indicators in annual model were respectively 21% and 0.62 and in monthly model were respectively 49% and 0.71. According to the results of the inflow range prediction, the prediction accuracy of the annual and monthly patterns was 75% and 83%, respectively. Modelling results showed that BN performs better in predicting the inflow range than its numerical prediction. The proposed model can improve the decision making of reservoirs operation. Dam inflow prediction (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Zayandehrud dam (dpeaa)DE-He213 Roozbahani, Abbas verfasserin aut Kardan Moghaddam, Hamid verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 34(2020), 9 vom: 17. Juni, Seite 2933-2951 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:34 year:2020 number:9 day:17 month:06 pages:2933-2951 https://dx.doi.org/10.1007/s11269-020-02591-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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 43.33 ASE AR 34 2020 9 17 06 2933-2951
allfieldsGer	10.1007/s11269-020-02591-8 doi (DE-627)SPR040389243 (SPR)s11269-020-02591-8-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Noorbeh, Parisa verfasserin aut Annual and Monthly Dam Inflow Prediction Using Bayesian Networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dam inflow prediction is important in terms of optimal water allocation and reduction of potential risks of floods and droughts. It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networks (BNs) was used to evaluate its efficiency in predicting inflow into reservoirs considering the uncertainties. For this purpose, continuous BNs as well as integration of K-means clustering and discrete BNs were applied for predicting magnitude and range of inflows, respectively in terms of annual and monthly prediction scenarios. In this regard, the Zayandehrud Dam reservoir in Iran was selected to test this model. To achieve the best network structure in these scenarios, different patterns were defined based on the combination of predictors. According to the magnitude predictions, the MAPE and $ R^{2} $ indicators in annual model were respectively 21% and 0.62 and in monthly model were respectively 49% and 0.71. According to the results of the inflow range prediction, the prediction accuracy of the annual and monthly patterns was 75% and 83%, respectively. Modelling results showed that BN performs better in predicting the inflow range than its numerical prediction. The proposed model can improve the decision making of reservoirs operation. Dam inflow prediction (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Zayandehrud dam (dpeaa)DE-He213 Roozbahani, Abbas verfasserin aut Kardan Moghaddam, Hamid verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 34(2020), 9 vom: 17. Juni, Seite 2933-2951 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:34 year:2020 number:9 day:17 month:06 pages:2933-2951 https://dx.doi.org/10.1007/s11269-020-02591-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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 43.33 ASE AR 34 2020 9 17 06 2933-2951
allfieldsSound	10.1007/s11269-020-02591-8 doi (DE-627)SPR040389243 (SPR)s11269-020-02591-8-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Noorbeh, Parisa verfasserin aut Annual and Monthly Dam Inflow Prediction Using Bayesian Networks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dam inflow prediction is important in terms of optimal water allocation and reduction of potential risks of floods and droughts. It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networks (BNs) was used to evaluate its efficiency in predicting inflow into reservoirs considering the uncertainties. For this purpose, continuous BNs as well as integration of K-means clustering and discrete BNs were applied for predicting magnitude and range of inflows, respectively in terms of annual and monthly prediction scenarios. In this regard, the Zayandehrud Dam reservoir in Iran was selected to test this model. To achieve the best network structure in these scenarios, different patterns were defined based on the combination of predictors. According to the magnitude predictions, the MAPE and $ R^{2} $ indicators in annual model were respectively 21% and 0.62 and in monthly model were respectively 49% and 0.71. According to the results of the inflow range prediction, the prediction accuracy of the annual and monthly patterns was 75% and 83%, respectively. Modelling results showed that BN performs better in predicting the inflow range than its numerical prediction. The proposed model can improve the decision making of reservoirs operation. Dam inflow prediction (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Zayandehrud dam (dpeaa)DE-He213 Roozbahani, Abbas verfasserin aut Kardan Moghaddam, Hamid verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 34(2020), 9 vom: 17. Juni, Seite 2933-2951 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:34 year:2020 number:9 day:17 month:06 pages:2933-2951 https://dx.doi.org/10.1007/s11269-020-02591-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE 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 43.33 ASE AR 34 2020 9 17 06 2933-2951
language	English
source	Enthalten in Water resources management 34(2020), 9 vom: 17. Juni, Seite 2933-2951 volume:34 year:2020 number:9 day:17 month:06 pages:2933-2951
sourceStr	Enthalten in Water resources management 34(2020), 9 vom: 17. Juni, Seite 2933-2951 volume:34 year:2020 number:9 day:17 month:06 pages:2933-2951
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Dam inflow prediction Bayesian networks Uncertainty Clustering Zayandehrud dam
dewey-raw	550
isfreeaccess_bool	false
container_title	Water resources management
authorswithroles_txt_mv	Noorbeh, Parisa @@aut@@ Roozbahani, Abbas @@aut@@ Kardan Moghaddam, Hamid @@aut@@
publishDateDaySort_date	2020-06-17T00:00:00Z
hierarchy_top_id	315299924
dewey-sort	3550
id	SPR040389243
language_de	englisch
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It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networks (BNs) was used to evaluate its efficiency in predicting inflow into reservoirs considering the uncertainties. For this purpose, continuous BNs as well as integration of K-means clustering and discrete BNs were applied for predicting magnitude and range of inflows, respectively in terms of annual and monthly prediction scenarios. In this regard, the Zayandehrud Dam reservoir in Iran was selected to test this model. To achieve the best network structure in these scenarios, different patterns were defined based on the combination of predictors. According to the magnitude predictions, the MAPE and $ R^{2} $ indicators in annual model were respectively 21% and 0.62 and in monthly model were respectively 49% and 0.71. According to the results of the inflow range prediction, the prediction accuracy of the annual and monthly patterns was 75% and 83%, respectively. Modelling results showed that BN performs better in predicting the inflow range than its numerical prediction. 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author	Noorbeh, Parisa
spellingShingle	Noorbeh, Parisa ddc 550 bkl 43.33 misc Dam inflow prediction misc Bayesian networks misc Uncertainty misc Clustering misc Zayandehrud dam Annual and Monthly Dam Inflow Prediction Using Bayesian Networks
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topic_title	550 630 ASE 43.33 bkl Annual and Monthly Dam Inflow Prediction Using Bayesian Networks Dam inflow prediction (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Zayandehrud dam (dpeaa)DE-He213
topic	ddc 550 bkl 43.33 misc Dam inflow prediction misc Bayesian networks misc Uncertainty misc Clustering misc Zayandehrud dam
topic_unstemmed	ddc 550 bkl 43.33 misc Dam inflow prediction misc Bayesian networks misc Uncertainty misc Clustering misc Zayandehrud dam
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title	Annual and Monthly Dam Inflow Prediction Using Bayesian Networks
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title_full	Annual and Monthly Dam Inflow Prediction Using Bayesian Networks
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author_browse	Noorbeh, Parisa Roozbahani, Abbas Kardan Moghaddam, Hamid
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title_sort	annual and monthly dam inflow prediction using bayesian networks
title_auth	Annual and Monthly Dam Inflow Prediction Using Bayesian Networks
abstract	Abstract Dam inflow prediction is important in terms of optimal water allocation and reduction of potential risks of floods and droughts. It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networks (BNs) was used to evaluate its efficiency in predicting inflow into reservoirs considering the uncertainties. For this purpose, continuous BNs as well as integration of K-means clustering and discrete BNs were applied for predicting magnitude and range of inflows, respectively in terms of annual and monthly prediction scenarios. In this regard, the Zayandehrud Dam reservoir in Iran was selected to test this model. To achieve the best network structure in these scenarios, different patterns were defined based on the combination of predictors. According to the magnitude predictions, the MAPE and $ R^{2} $ indicators in annual model were respectively 21% and 0.62 and in monthly model were respectively 49% and 0.71. According to the results of the inflow range prediction, the prediction accuracy of the annual and monthly patterns was 75% and 83%, respectively. Modelling results showed that BN performs better in predicting the inflow range than its numerical prediction. The proposed model can improve the decision making of reservoirs operation.
abstractGer	Abstract Dam inflow prediction is important in terms of optimal water allocation and reduction of potential risks of floods and droughts. It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networks (BNs) was used to evaluate its efficiency in predicting inflow into reservoirs considering the uncertainties. For this purpose, continuous BNs as well as integration of K-means clustering and discrete BNs were applied for predicting magnitude and range of inflows, respectively in terms of annual and monthly prediction scenarios. In this regard, the Zayandehrud Dam reservoir in Iran was selected to test this model. To achieve the best network structure in these scenarios, different patterns were defined based on the combination of predictors. According to the magnitude predictions, the MAPE and $ R^{2} $ indicators in annual model were respectively 21% and 0.62 and in monthly model were respectively 49% and 0.71. According to the results of the inflow range prediction, the prediction accuracy of the annual and monthly patterns was 75% and 83%, respectively. Modelling results showed that BN performs better in predicting the inflow range than its numerical prediction. The proposed model can improve the decision making of reservoirs operation.
abstract_unstemmed	Abstract Dam inflow prediction is important in terms of optimal water allocation and reduction of potential risks of floods and droughts. It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networks (BNs) was used to evaluate its efficiency in predicting inflow into reservoirs considering the uncertainties. For this purpose, continuous BNs as well as integration of K-means clustering and discrete BNs were applied for predicting magnitude and range of inflows, respectively in terms of annual and monthly prediction scenarios. In this regard, the Zayandehrud Dam reservoir in Iran was selected to test this model. To achieve the best network structure in these scenarios, different patterns were defined based on the combination of predictors. According to the magnitude predictions, the MAPE and $ R^{2} $ indicators in annual model were respectively 21% and 0.62 and in monthly model were respectively 49% and 0.71. According to the results of the inflow range prediction, the prediction accuracy of the annual and monthly patterns was 75% and 83%, respectively. Modelling results showed that BN performs better in predicting the inflow range than its numerical prediction. The proposed model can improve the decision making of reservoirs operation.
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container_issue	9
title_short	Annual and Monthly Dam Inflow Prediction Using Bayesian Networks
url	https://dx.doi.org/10.1007/s11269-020-02591-8
remote_bool	true
author2	Roozbahani, Abbas Kardan Moghaddam, Hamid
author2Str	Roozbahani, Abbas Kardan Moghaddam, Hamid
ppnlink	315299924
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s11269-020-02591-8
up_date	2024-07-03T15:40:41.547Z
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Annual and Monthly Dam Inflow Prediction Using Bayesian Networks

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