Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis

In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potent...
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Autor*in:	Mohammad Ehteram [verfasserIn] Ahmad Ferdowsi [verfasserIn] Mahtab Faramarzpour [verfasserIn] Ahmed Mohammed Sami Al-Janabi [verfasserIn] Nadhir Al-Ansari [verfasserIn] Neeraj Dhanraj Bokde [verfasserIn] Zaher Mundher Yaseen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Hybrid artificial intelligence Urmia Lake Water level prediction Machine learning Water resources

Übergeordnetes Werk:	In: Alexandria Engineering Journal - Elsevier, 2016, 60(2021), 2, Seite 2193-2208
Übergeordnetes Werk:	volume:60 ; year:2021 ; number:2 ; pages:2193-2208

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.aej.2020.12.034

Katalog-ID:	DOAJ003443302

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245	1	0	\|a Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis
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520			\|a In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level.
650		4	\|a Hybrid artificial intelligence
650		4	\|a Urmia Lake
650		4	\|a Water level prediction
650		4	\|a Machine learning
650		4	\|a Water resources
653		0	\|a Engineering (General). Civil engineering (General)
700	0		\|a Ahmad Ferdowsi \|e verfasserin \|4 aut
700	0		\|a Mahtab Faramarzpour \|e verfasserin \|4 aut
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700	0		\|a Nadhir Al-Ansari \|e verfasserin \|4 aut
700	0		\|a Neeraj Dhanraj Bokde \|e verfasserin \|4 aut
700	0		\|a Zaher Mundher Yaseen \|e verfasserin \|4 aut
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allfields	10.1016/j.aej.2020.12.034 doi (DE-627)DOAJ003443302 (DE-599)DOAJ9104d14ada9a45c2a82f462566961241 DE-627 ger DE-627 rakwb eng TA1-2040 Mohammad Ehteram verfasserin aut Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level. Hybrid artificial intelligence Urmia Lake Water level prediction Machine learning Water resources Engineering (General). Civil engineering (General) Ahmad Ferdowsi verfasserin aut Mahtab Faramarzpour verfasserin aut Ahmed Mohammed Sami Al-Janabi verfasserin aut Nadhir Al-Ansari verfasserin aut Neeraj Dhanraj Bokde verfasserin aut Zaher Mundher Yaseen verfasserin aut In Alexandria Engineering Journal Elsevier, 2016 60(2021), 2, Seite 2193-2208 (DE-627)669887609 (DE-600)2631413-7 20902670 nnns volume:60 year:2021 number:2 pages:2193-2208 https://doi.org/10.1016/j.aej.2020.12.034 kostenfrei https://doaj.org/article/9104d14ada9a45c2a82f462566961241 kostenfrei http://www.sciencedirect.com/science/article/pii/S1110016820306840 kostenfrei https://doaj.org/toc/1110-0168 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 60 2021 2 2193-2208
spelling	10.1016/j.aej.2020.12.034 doi (DE-627)DOAJ003443302 (DE-599)DOAJ9104d14ada9a45c2a82f462566961241 DE-627 ger DE-627 rakwb eng TA1-2040 Mohammad Ehteram verfasserin aut Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level. Hybrid artificial intelligence Urmia Lake Water level prediction Machine learning Water resources Engineering (General). Civil engineering (General) Ahmad Ferdowsi verfasserin aut Mahtab Faramarzpour verfasserin aut Ahmed Mohammed Sami Al-Janabi verfasserin aut Nadhir Al-Ansari verfasserin aut Neeraj Dhanraj Bokde verfasserin aut Zaher Mundher Yaseen verfasserin aut In Alexandria Engineering Journal Elsevier, 2016 60(2021), 2, Seite 2193-2208 (DE-627)669887609 (DE-600)2631413-7 20902670 nnns volume:60 year:2021 number:2 pages:2193-2208 https://doi.org/10.1016/j.aej.2020.12.034 kostenfrei https://doaj.org/article/9104d14ada9a45c2a82f462566961241 kostenfrei http://www.sciencedirect.com/science/article/pii/S1110016820306840 kostenfrei https://doaj.org/toc/1110-0168 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 60 2021 2 2193-2208
allfields_unstemmed	10.1016/j.aej.2020.12.034 doi (DE-627)DOAJ003443302 (DE-599)DOAJ9104d14ada9a45c2a82f462566961241 DE-627 ger DE-627 rakwb eng TA1-2040 Mohammad Ehteram verfasserin aut Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level. Hybrid artificial intelligence Urmia Lake Water level prediction Machine learning Water resources Engineering (General). Civil engineering (General) Ahmad Ferdowsi verfasserin aut Mahtab Faramarzpour verfasserin aut Ahmed Mohammed Sami Al-Janabi verfasserin aut Nadhir Al-Ansari verfasserin aut Neeraj Dhanraj Bokde verfasserin aut Zaher Mundher Yaseen verfasserin aut In Alexandria Engineering Journal Elsevier, 2016 60(2021), 2, Seite 2193-2208 (DE-627)669887609 (DE-600)2631413-7 20902670 nnns volume:60 year:2021 number:2 pages:2193-2208 https://doi.org/10.1016/j.aej.2020.12.034 kostenfrei https://doaj.org/article/9104d14ada9a45c2a82f462566961241 kostenfrei http://www.sciencedirect.com/science/article/pii/S1110016820306840 kostenfrei https://doaj.org/toc/1110-0168 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 60 2021 2 2193-2208
allfieldsGer	10.1016/j.aej.2020.12.034 doi (DE-627)DOAJ003443302 (DE-599)DOAJ9104d14ada9a45c2a82f462566961241 DE-627 ger DE-627 rakwb eng TA1-2040 Mohammad Ehteram verfasserin aut Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level. Hybrid artificial intelligence Urmia Lake Water level prediction Machine learning Water resources Engineering (General). Civil engineering (General) Ahmad Ferdowsi verfasserin aut Mahtab Faramarzpour verfasserin aut Ahmed Mohammed Sami Al-Janabi verfasserin aut Nadhir Al-Ansari verfasserin aut Neeraj Dhanraj Bokde verfasserin aut Zaher Mundher Yaseen verfasserin aut In Alexandria Engineering Journal Elsevier, 2016 60(2021), 2, Seite 2193-2208 (DE-627)669887609 (DE-600)2631413-7 20902670 nnns volume:60 year:2021 number:2 pages:2193-2208 https://doi.org/10.1016/j.aej.2020.12.034 kostenfrei https://doaj.org/article/9104d14ada9a45c2a82f462566961241 kostenfrei http://www.sciencedirect.com/science/article/pii/S1110016820306840 kostenfrei https://doaj.org/toc/1110-0168 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 60 2021 2 2193-2208
allfieldsSound	10.1016/j.aej.2020.12.034 doi (DE-627)DOAJ003443302 (DE-599)DOAJ9104d14ada9a45c2a82f462566961241 DE-627 ger DE-627 rakwb eng TA1-2040 Mohammad Ehteram verfasserin aut Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level. Hybrid artificial intelligence Urmia Lake Water level prediction Machine learning Water resources Engineering (General). Civil engineering (General) Ahmad Ferdowsi verfasserin aut Mahtab Faramarzpour verfasserin aut Ahmed Mohammed Sami Al-Janabi verfasserin aut Nadhir Al-Ansari verfasserin aut Neeraj Dhanraj Bokde verfasserin aut Zaher Mundher Yaseen verfasserin aut In Alexandria Engineering Journal Elsevier, 2016 60(2021), 2, Seite 2193-2208 (DE-627)669887609 (DE-600)2631413-7 20902670 nnns volume:60 year:2021 number:2 pages:2193-2208 https://doi.org/10.1016/j.aej.2020.12.034 kostenfrei https://doaj.org/article/9104d14ada9a45c2a82f462566961241 kostenfrei http://www.sciencedirect.com/science/article/pii/S1110016820306840 kostenfrei https://doaj.org/toc/1110-0168 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 60 2021 2 2193-2208
language	English
source	In Alexandria Engineering Journal 60(2021), 2, Seite 2193-2208 volume:60 year:2021 number:2 pages:2193-2208
sourceStr	In Alexandria Engineering Journal 60(2021), 2, Seite 2193-2208 volume:60 year:2021 number:2 pages:2193-2208
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Hybrid artificial intelligence Urmia Lake Water level prediction Machine learning Water resources Engineering (General). Civil engineering (General)
isfreeaccess_bool	true
container_title	Alexandria Engineering Journal
authorswithroles_txt_mv	Mohammad Ehteram @@aut@@ Ahmad Ferdowsi @@aut@@ Mahtab Faramarzpour @@aut@@ Ahmed Mohammed Sami Al-Janabi @@aut@@ Nadhir Al-Ansari @@aut@@ Neeraj Dhanraj Bokde @@aut@@ Zaher Mundher Yaseen @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	669887609
id	DOAJ003443302
language_de	englisch
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callnumber-first	T - Technology
author	Mohammad Ehteram
spellingShingle	Mohammad Ehteram misc TA1-2040 misc Hybrid artificial intelligence misc Urmia Lake misc Water level prediction misc Machine learning misc Water resources misc Engineering (General). Civil engineering (General) Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis
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topic_title	TA1-2040 Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis Hybrid artificial intelligence Urmia Lake Water level prediction Machine learning Water resources
topic	misc TA1-2040 misc Hybrid artificial intelligence misc Urmia Lake misc Water level prediction misc Machine learning misc Water resources misc Engineering (General). Civil engineering (General)
topic_unstemmed	misc TA1-2040 misc Hybrid artificial intelligence misc Urmia Lake misc Water level prediction misc Machine learning misc Water resources misc Engineering (General). Civil engineering (General)
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title	Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis
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title_full	Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis
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title_sort	hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis
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title_auth	Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis
abstract	In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level.
abstractGer	In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level.
abstract_unstemmed	In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level.
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title_short	Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis
url	https://doi.org/10.1016/j.aej.2020.12.034 https://doaj.org/article/9104d14ada9a45c2a82f462566961241 http://www.sciencedirect.com/science/article/pii/S1110016820306840 https://doaj.org/toc/1110-0168
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The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake’s water level.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hybrid artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Urmia Lake</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Water level prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Water resources</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). 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Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis

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