State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting

Abstract Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This pap...
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Autor*in:	Tan, Woon Yang [verfasserIn] Lai, Sai Hin Teo, Fang Yenn El-Shafie, Ahmed

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Anmerkung:	© The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022

Übergeordnetes Werk:	Enthalten in: Archives of computational methods in engineering - Dordrecht [u.a.] : Springer, 1994, 29(2022), 7 vom: 11. Juni, Seite 5185-5211
Übergeordnetes Werk:	volume:29 ; year:2022 ; number:7 ; day:11 ; month:06 ; pages:5185-5211

Links:	Volltext

DOI / URN:	10.1007/s11831-022-09763-2

Katalog-ID:	SPR048583596

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520			\|a Abstract Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This paper is to review the recent development of the artificial intelligence (AI) methods for river streamflow forecasting over the last two decades. The original establishments of forecasting model are derived from the neural network theory, supervised machine learning and fuzzy logic theory. The first wave of development comes with evolutional theory of optimization algorithm and the probabilistic theory. The distinction of AI model is the ability to process complex data that are non-linear, non-stationary and stochastic without prior physical knowledge. However, due to the limitations of the AI model, issues arise in the form of over-fitting, trap in local minima and slow learning process; and subsequently limit the possibility of gaining higher accuracy. This has prompted researchers to try numerous new approaches to increase the efficiency of data processing. The second wave comes with the hybrid models, modular techniques and ensemble models which has revolutionize into contemporary streamflow forecasting. With the latest hybrid models, results have shown retrieval of additional information are made possible that lead to better conclusion. This paper has suggested the research gaps and some future recommendations. It will be useful for new researcher and existing one to identify and be acquainted with the trends of different techniques of artificial intelligence.
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allfields	10.1007/s11831-022-09763-2 doi (DE-627)SPR048583596 (SPR)s11831-022-09763-2-e DE-627 ger DE-627 rakwb eng Tan, Woon Yang verfasserin aut State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022 Abstract Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This paper is to review the recent development of the artificial intelligence (AI) methods for river streamflow forecasting over the last two decades. The original establishments of forecasting model are derived from the neural network theory, supervised machine learning and fuzzy logic theory. The first wave of development comes with evolutional theory of optimization algorithm and the probabilistic theory. The distinction of AI model is the ability to process complex data that are non-linear, non-stationary and stochastic without prior physical knowledge. However, due to the limitations of the AI model, issues arise in the form of over-fitting, trap in local minima and slow learning process; and subsequently limit the possibility of gaining higher accuracy. This has prompted researchers to try numerous new approaches to increase the efficiency of data processing. The second wave comes with the hybrid models, modular techniques and ensemble models which has revolutionize into contemporary streamflow forecasting. With the latest hybrid models, results have shown retrieval of additional information are made possible that lead to better conclusion. This paper has suggested the research gaps and some future recommendations. It will be useful for new researcher and existing one to identify and be acquainted with the trends of different techniques of artificial intelligence. Lai, Sai Hin (orcid)0000-0002-7143-4805 aut Teo, Fang Yenn aut El-Shafie, Ahmed aut Enthalten in Archives of computational methods in engineering Dordrecht [u.a.] : Springer, 1994 29(2022), 7 vom: 11. Juni, Seite 5185-5211 (DE-627)527575682 (DE-600)2276736-8 1886-1784 nnns volume:29 year:2022 number:7 day:11 month:06 pages:5185-5211 https://dx.doi.org/10.1007/s11831-022-09763-2 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_65 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_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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 29 2022 7 11 06 5185-5211
spelling	10.1007/s11831-022-09763-2 doi (DE-627)SPR048583596 (SPR)s11831-022-09763-2-e DE-627 ger DE-627 rakwb eng Tan, Woon Yang verfasserin aut State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022 Abstract Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This paper is to review the recent development of the artificial intelligence (AI) methods for river streamflow forecasting over the last two decades. The original establishments of forecasting model are derived from the neural network theory, supervised machine learning and fuzzy logic theory. The first wave of development comes with evolutional theory of optimization algorithm and the probabilistic theory. The distinction of AI model is the ability to process complex data that are non-linear, non-stationary and stochastic without prior physical knowledge. However, due to the limitations of the AI model, issues arise in the form of over-fitting, trap in local minima and slow learning process; and subsequently limit the possibility of gaining higher accuracy. This has prompted researchers to try numerous new approaches to increase the efficiency of data processing. The second wave comes with the hybrid models, modular techniques and ensemble models which has revolutionize into contemporary streamflow forecasting. With the latest hybrid models, results have shown retrieval of additional information are made possible that lead to better conclusion. This paper has suggested the research gaps and some future recommendations. It will be useful for new researcher and existing one to identify and be acquainted with the trends of different techniques of artificial intelligence. Lai, Sai Hin (orcid)0000-0002-7143-4805 aut Teo, Fang Yenn aut El-Shafie, Ahmed aut Enthalten in Archives of computational methods in engineering Dordrecht [u.a.] : Springer, 1994 29(2022), 7 vom: 11. Juni, Seite 5185-5211 (DE-627)527575682 (DE-600)2276736-8 1886-1784 nnns volume:29 year:2022 number:7 day:11 month:06 pages:5185-5211 https://dx.doi.org/10.1007/s11831-022-09763-2 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_65 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_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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 29 2022 7 11 06 5185-5211
allfields_unstemmed	10.1007/s11831-022-09763-2 doi (DE-627)SPR048583596 (SPR)s11831-022-09763-2-e DE-627 ger DE-627 rakwb eng Tan, Woon Yang verfasserin aut State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022 Abstract Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This paper is to review the recent development of the artificial intelligence (AI) methods for river streamflow forecasting over the last two decades. The original establishments of forecasting model are derived from the neural network theory, supervised machine learning and fuzzy logic theory. The first wave of development comes with evolutional theory of optimization algorithm and the probabilistic theory. The distinction of AI model is the ability to process complex data that are non-linear, non-stationary and stochastic without prior physical knowledge. However, due to the limitations of the AI model, issues arise in the form of over-fitting, trap in local minima and slow learning process; and subsequently limit the possibility of gaining higher accuracy. This has prompted researchers to try numerous new approaches to increase the efficiency of data processing. The second wave comes with the hybrid models, modular techniques and ensemble models which has revolutionize into contemporary streamflow forecasting. With the latest hybrid models, results have shown retrieval of additional information are made possible that lead to better conclusion. This paper has suggested the research gaps and some future recommendations. It will be useful for new researcher and existing one to identify and be acquainted with the trends of different techniques of artificial intelligence. Lai, Sai Hin (orcid)0000-0002-7143-4805 aut Teo, Fang Yenn aut El-Shafie, Ahmed aut Enthalten in Archives of computational methods in engineering Dordrecht [u.a.] : Springer, 1994 29(2022), 7 vom: 11. Juni, Seite 5185-5211 (DE-627)527575682 (DE-600)2276736-8 1886-1784 nnns volume:29 year:2022 number:7 day:11 month:06 pages:5185-5211 https://dx.doi.org/10.1007/s11831-022-09763-2 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_65 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_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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 29 2022 7 11 06 5185-5211
allfieldsGer	10.1007/s11831-022-09763-2 doi (DE-627)SPR048583596 (SPR)s11831-022-09763-2-e DE-627 ger DE-627 rakwb eng Tan, Woon Yang verfasserin aut State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022 Abstract Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This paper is to review the recent development of the artificial intelligence (AI) methods for river streamflow forecasting over the last two decades. The original establishments of forecasting model are derived from the neural network theory, supervised machine learning and fuzzy logic theory. The first wave of development comes with evolutional theory of optimization algorithm and the probabilistic theory. The distinction of AI model is the ability to process complex data that are non-linear, non-stationary and stochastic without prior physical knowledge. However, due to the limitations of the AI model, issues arise in the form of over-fitting, trap in local minima and slow learning process; and subsequently limit the possibility of gaining higher accuracy. This has prompted researchers to try numerous new approaches to increase the efficiency of data processing. The second wave comes with the hybrid models, modular techniques and ensemble models which has revolutionize into contemporary streamflow forecasting. With the latest hybrid models, results have shown retrieval of additional information are made possible that lead to better conclusion. This paper has suggested the research gaps and some future recommendations. It will be useful for new researcher and existing one to identify and be acquainted with the trends of different techniques of artificial intelligence. Lai, Sai Hin (orcid)0000-0002-7143-4805 aut Teo, Fang Yenn aut El-Shafie, Ahmed aut Enthalten in Archives of computational methods in engineering Dordrecht [u.a.] : Springer, 1994 29(2022), 7 vom: 11. Juni, Seite 5185-5211 (DE-627)527575682 (DE-600)2276736-8 1886-1784 nnns volume:29 year:2022 number:7 day:11 month:06 pages:5185-5211 https://dx.doi.org/10.1007/s11831-022-09763-2 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_65 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_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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 29 2022 7 11 06 5185-5211
allfieldsSound	10.1007/s11831-022-09763-2 doi (DE-627)SPR048583596 (SPR)s11831-022-09763-2-e DE-627 ger DE-627 rakwb eng Tan, Woon Yang verfasserin aut State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022 Abstract Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This paper is to review the recent development of the artificial intelligence (AI) methods for river streamflow forecasting over the last two decades. The original establishments of forecasting model are derived from the neural network theory, supervised machine learning and fuzzy logic theory. The first wave of development comes with evolutional theory of optimization algorithm and the probabilistic theory. The distinction of AI model is the ability to process complex data that are non-linear, non-stationary and stochastic without prior physical knowledge. However, due to the limitations of the AI model, issues arise in the form of over-fitting, trap in local minima and slow learning process; and subsequently limit the possibility of gaining higher accuracy. This has prompted researchers to try numerous new approaches to increase the efficiency of data processing. The second wave comes with the hybrid models, modular techniques and ensemble models which has revolutionize into contemporary streamflow forecasting. With the latest hybrid models, results have shown retrieval of additional information are made possible that lead to better conclusion. This paper has suggested the research gaps and some future recommendations. It will be useful for new researcher and existing one to identify and be acquainted with the trends of different techniques of artificial intelligence. Lai, Sai Hin (orcid)0000-0002-7143-4805 aut Teo, Fang Yenn aut El-Shafie, Ahmed aut Enthalten in Archives of computational methods in engineering Dordrecht [u.a.] : Springer, 1994 29(2022), 7 vom: 11. Juni, Seite 5185-5211 (DE-627)527575682 (DE-600)2276736-8 1886-1784 nnns volume:29 year:2022 number:7 day:11 month:06 pages:5185-5211 https://dx.doi.org/10.1007/s11831-022-09763-2 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_65 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_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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 29 2022 7 11 06 5185-5211
language	English
source	Enthalten in Archives of computational methods in engineering 29(2022), 7 vom: 11. Juni, Seite 5185-5211 volume:29 year:2022 number:7 day:11 month:06 pages:5185-5211
sourceStr	Enthalten in Archives of computational methods in engineering 29(2022), 7 vom: 11. Juni, Seite 5185-5211 volume:29 year:2022 number:7 day:11 month:06 pages:5185-5211
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container_title	Archives of computational methods in engineering
authorswithroles_txt_mv	Tan, Woon Yang @@aut@@ Lai, Sai Hin @@aut@@ Teo, Fang Yenn @@aut@@ El-Shafie, Ahmed @@aut@@
publishDateDaySort_date	2022-06-11T00:00:00Z
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author	Tan, Woon Yang
spellingShingle	Tan, Woon Yang State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting
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topic_title	State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting
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title	State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting
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title_full	State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting
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title_sort	state-of-the-art development of two-waves artificial intelligence modeling techniques for river streamflow forecasting
title_auth	State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting
abstract	Abstract Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This paper is to review the recent development of the artificial intelligence (AI) methods for river streamflow forecasting over the last two decades. The original establishments of forecasting model are derived from the neural network theory, supervised machine learning and fuzzy logic theory. The first wave of development comes with evolutional theory of optimization algorithm and the probabilistic theory. The distinction of AI model is the ability to process complex data that are non-linear, non-stationary and stochastic without prior physical knowledge. However, due to the limitations of the AI model, issues arise in the form of over-fitting, trap in local minima and slow learning process; and subsequently limit the possibility of gaining higher accuracy. This has prompted researchers to try numerous new approaches to increase the efficiency of data processing. The second wave comes with the hybrid models, modular techniques and ensemble models which has revolutionize into contemporary streamflow forecasting. With the latest hybrid models, results have shown retrieval of additional information are made possible that lead to better conclusion. This paper has suggested the research gaps and some future recommendations. It will be useful for new researcher and existing one to identify and be acquainted with the trends of different techniques of artificial intelligence. © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022
abstractGer	Abstract Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This paper is to review the recent development of the artificial intelligence (AI) methods for river streamflow forecasting over the last two decades. The original establishments of forecasting model are derived from the neural network theory, supervised machine learning and fuzzy logic theory. The first wave of development comes with evolutional theory of optimization algorithm and the probabilistic theory. The distinction of AI model is the ability to process complex data that are non-linear, non-stationary and stochastic without prior physical knowledge. However, due to the limitations of the AI model, issues arise in the form of over-fitting, trap in local minima and slow learning process; and subsequently limit the possibility of gaining higher accuracy. This has prompted researchers to try numerous new approaches to increase the efficiency of data processing. The second wave comes with the hybrid models, modular techniques and ensemble models which has revolutionize into contemporary streamflow forecasting. With the latest hybrid models, results have shown retrieval of additional information are made possible that lead to better conclusion. This paper has suggested the research gaps and some future recommendations. It will be useful for new researcher and existing one to identify and be acquainted with the trends of different techniques of artificial intelligence. © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022
abstract_unstemmed	Abstract Streamflow forecasting is the most well studied hydrological science but still portray numerous unknown knowledge. The conventional physical-based model is unable to yield a high accuracy of forecast due to the embedded noises, non-linear and stochastic nature of hydrological data. This paper is to review the recent development of the artificial intelligence (AI) methods for river streamflow forecasting over the last two decades. The original establishments of forecasting model are derived from the neural network theory, supervised machine learning and fuzzy logic theory. The first wave of development comes with evolutional theory of optimization algorithm and the probabilistic theory. The distinction of AI model is the ability to process complex data that are non-linear, non-stationary and stochastic without prior physical knowledge. However, due to the limitations of the AI model, issues arise in the form of over-fitting, trap in local minima and slow learning process; and subsequently limit the possibility of gaining higher accuracy. This has prompted researchers to try numerous new approaches to increase the efficiency of data processing. The second wave comes with the hybrid models, modular techniques and ensemble models which has revolutionize into contemporary streamflow forecasting. With the latest hybrid models, results have shown retrieval of additional information are made possible that lead to better conclusion. This paper has suggested the research gaps and some future recommendations. It will be useful for new researcher and existing one to identify and be acquainted with the trends of different techniques of artificial intelligence. © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022
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title_short	State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting
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State-of-the-Art Development of Two-Waves Artificial Intelligence Modeling Techniques for River Streamflow Forecasting

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