RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region

Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and m...
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Autor*in:	Kamel, Ammar Hatem [verfasserIn] Afan, Haitham Abdulmohsin [verfasserIn] Sherif, Mohsen [verfasserIn] Ahmed, Ali Najah [verfasserIn] El-Shafie, Ahmed [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Prediction model Sub-surface reservoir Evaporation rate estimation Arid region Neural network model

Übergeordnetes Werk:	Enthalten in: Sustainable Computing - Amsterdam [u.a.] : Elsevier, 2011, 30
Übergeordnetes Werk:	volume:30

DOI / URN:	10.1016/j.suscom.2021.100514

Katalog-ID:	ELV006060315

Internformat


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245	1	0	\|a RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region
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520			\|a Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model’s input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively.
650		4	\|a Prediction model
650		4	\|a Sub-surface reservoir
650		4	\|a Evaporation rate estimation
650		4	\|a Arid region
650		4	\|a Neural network model
700	1		\|a Afan, Haitham Abdulmohsin \|e verfasserin \|0 (orcid)0000-0002-4957-756X \|4 aut
700	1		\|a Sherif, Mohsen \|e verfasserin \|4 aut
700	1		\|a Ahmed, Ali Najah \|e verfasserin \|4 aut
700	1		\|a El-Shafie, Ahmed \|e verfasserin \|0 (orcid)0000-0002-5618-6663 \|4 aut
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912			\|a GBV_ILN_95
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912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
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912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
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912			\|a GBV_ILN_2048
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912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
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912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
951			\|a AR
952			\|d 30

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author_variant	a h k ah ahk h a a ha haa m s ms a n a an ana a e s aes
matchkey_str	article:22105379:2021----::bnvrugnmdlnapocfrusraevprtortp
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publishDate	2021
allfields	10.1016/j.suscom.2021.100514 doi (DE-627)ELV006060315 (ELSEVIER)S2210-5379(21)00007-X DE-627 ger DE-627 rda eng 004 DE-600 Kamel, Ammar Hatem verfasserin aut RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model’s input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively. Prediction model Sub-surface reservoir Evaporation rate estimation Arid region Neural network model Afan, Haitham Abdulmohsin verfasserin (orcid)0000-0002-4957-756X aut Sherif, Mohsen verfasserin aut Ahmed, Ali Najah verfasserin aut El-Shafie, Ahmed verfasserin (orcid)0000-0002-5618-6663 aut Enthalten in Sustainable Computing Amsterdam [u.a.] : Elsevier, 2011 30 Online-Ressource (DE-627)647657384 (DE-600)2596254-1 (DE-576)338519165 2210-5379 nnns volume:30 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 AR 30
spelling	10.1016/j.suscom.2021.100514 doi (DE-627)ELV006060315 (ELSEVIER)S2210-5379(21)00007-X DE-627 ger DE-627 rda eng 004 DE-600 Kamel, Ammar Hatem verfasserin aut RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model’s input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively. Prediction model Sub-surface reservoir Evaporation rate estimation Arid region Neural network model Afan, Haitham Abdulmohsin verfasserin (orcid)0000-0002-4957-756X aut Sherif, Mohsen verfasserin aut Ahmed, Ali Najah verfasserin aut El-Shafie, Ahmed verfasserin (orcid)0000-0002-5618-6663 aut Enthalten in Sustainable Computing Amsterdam [u.a.] : Elsevier, 2011 30 Online-Ressource (DE-627)647657384 (DE-600)2596254-1 (DE-576)338519165 2210-5379 nnns volume:30 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 AR 30
allfields_unstemmed	10.1016/j.suscom.2021.100514 doi (DE-627)ELV006060315 (ELSEVIER)S2210-5379(21)00007-X DE-627 ger DE-627 rda eng 004 DE-600 Kamel, Ammar Hatem verfasserin aut RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model’s input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively. Prediction model Sub-surface reservoir Evaporation rate estimation Arid region Neural network model Afan, Haitham Abdulmohsin verfasserin (orcid)0000-0002-4957-756X aut Sherif, Mohsen verfasserin aut Ahmed, Ali Najah verfasserin aut El-Shafie, Ahmed verfasserin (orcid)0000-0002-5618-6663 aut Enthalten in Sustainable Computing Amsterdam [u.a.] : Elsevier, 2011 30 Online-Ressource (DE-627)647657384 (DE-600)2596254-1 (DE-576)338519165 2210-5379 nnns volume:30 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 AR 30
allfieldsGer	10.1016/j.suscom.2021.100514 doi (DE-627)ELV006060315 (ELSEVIER)S2210-5379(21)00007-X DE-627 ger DE-627 rda eng 004 DE-600 Kamel, Ammar Hatem verfasserin aut RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model’s input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively. Prediction model Sub-surface reservoir Evaporation rate estimation Arid region Neural network model Afan, Haitham Abdulmohsin verfasserin (orcid)0000-0002-4957-756X aut Sherif, Mohsen verfasserin aut Ahmed, Ali Najah verfasserin aut El-Shafie, Ahmed verfasserin (orcid)0000-0002-5618-6663 aut Enthalten in Sustainable Computing Amsterdam [u.a.] : Elsevier, 2011 30 Online-Ressource (DE-627)647657384 (DE-600)2596254-1 (DE-576)338519165 2210-5379 nnns volume:30 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 AR 30
allfieldsSound	10.1016/j.suscom.2021.100514 doi (DE-627)ELV006060315 (ELSEVIER)S2210-5379(21)00007-X DE-627 ger DE-627 rda eng 004 DE-600 Kamel, Ammar Hatem verfasserin aut RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model’s input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively. Prediction model Sub-surface reservoir Evaporation rate estimation Arid region Neural network model Afan, Haitham Abdulmohsin verfasserin (orcid)0000-0002-4957-756X aut Sherif, Mohsen verfasserin aut Ahmed, Ali Najah verfasserin aut El-Shafie, Ahmed verfasserin (orcid)0000-0002-5618-6663 aut Enthalten in Sustainable Computing Amsterdam [u.a.] : Elsevier, 2011 30 Online-Ressource (DE-627)647657384 (DE-600)2596254-1 (DE-576)338519165 2210-5379 nnns volume:30 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 AR 30
language	English
source	Enthalten in Sustainable Computing 30 volume:30
sourceStr	Enthalten in Sustainable Computing 30 volume:30
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Prediction model Sub-surface reservoir Evaporation rate estimation Arid region Neural network model
dewey-raw	004
isfreeaccess_bool	false
container_title	Sustainable Computing
authorswithroles_txt_mv	Kamel, Ammar Hatem @@aut@@ Afan, Haitham Abdulmohsin @@aut@@ Sherif, Mohsen @@aut@@ Ahmed, Ali Najah @@aut@@ El-Shafie, Ahmed @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	647657384
dewey-sort	14
id	ELV006060315
language_de	englisch
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author	Kamel, Ammar Hatem
spellingShingle	Kamel, Ammar Hatem ddc 004 misc Prediction model misc Sub-surface reservoir misc Evaporation rate estimation misc Arid region misc Neural network model RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region
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topic_title	004 DE-600 RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region Prediction model Sub-surface reservoir Evaporation rate estimation Arid region Neural network model
topic	ddc 004 misc Prediction model misc Sub-surface reservoir misc Evaporation rate estimation misc Arid region misc Neural network model
topic_unstemmed	ddc 004 misc Prediction model misc Sub-surface reservoir misc Evaporation rate estimation misc Arid region misc Neural network model
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title	RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region
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title_full	RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region
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author_browse	Kamel, Ammar Hatem Afan, Haitham Abdulmohsin Sherif, Mohsen Ahmed, Ali Najah El-Shafie, Ahmed
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title_sort	rbfnn versus grnn modeling approach for sub-surface evaporation rate prediction in arid region
title_auth	RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region
abstract	Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model’s input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively.
abstractGer	Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model’s input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively.
abstract_unstemmed	Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model’s input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively.
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title_short	RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region
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author2	Afan, Haitham Abdulmohsin Sherif, Mohsen Ahmed, Ali Najah El-Shafie, Ahmed
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doi_str	10.1016/j.suscom.2021.100514
up_date	2024-07-06T20:05:16.631Z
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RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region

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