Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia
Abstract The management of river water quality is one the most significant environmental challenges. Water quality index (WQI) describes several water quality variables at a certain aquatic environment and time. Classically, WQI is commonly computed using the traditional methods which involved lengt...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hameed, Mohammed [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© The Natural Computing Applications Forum 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 28(2016), Suppl 1 vom: 08. Juni, Seite 893-905 |
|---|---|
| Übergeordnetes Werk: |
volume:28 ; year:2016 ; number:Suppl 1 ; day:08 ; month:06 ; pages:893-905 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-016-2404-7 |
|---|
| Katalog-ID: |
OLC2025599412 |
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| 520 | |a Abstract The management of river water quality is one the most significant environmental challenges. Water quality index (WQI) describes several water quality variables at a certain aquatic environment and time. Classically, WQI is commonly computed using the traditional methods which involved lengthy computation, consume timing and occasionally associated with accidental errors during subindex calculation. Thus, providing an accurate prediction model for WQI is highly required. Recently, the artificial neural networks (ANNs) have been examined for similar prediction applications and exhibited a remarkable ability to capture the nonlinearity pattern between predictors and predictand. In the current research, two different ANN algorithms, namely radial basis function neural network (RBFNN) and back propagation neural networks models, have been applied to examine and mimic the relationship of WQI with the water quality variables in a tropical environment (Malaysia). The input variables categorized into two different architectures and have been inspected. In addition, comprehensive analysis for the performance evaluation and the sensitivity analysis of the variables have been conducted. The results achieved are positively promising with high performance accuracy belonging to RBFNN model for both scenarios. Furthermore, the proposed approach offers an effective alternative to compute and predict WQI, to the fact that WQI manual calculation methods involved lengthy computations, transformations, use of various subindex formulae for each value of the constituent water quality variables, and consuming time. | ||
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10.1007/s00521-016-2404-7 doi (DE-627)OLC2025599412 (DE-He213)s00521-016-2404-7-p DE-627 ger DE-627 rakwb eng 004 VZ Hameed, Mohammed verfasserin aut Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2016 Abstract The management of river water quality is one the most significant environmental challenges. Water quality index (WQI) describes several water quality variables at a certain aquatic environment and time. Classically, WQI is commonly computed using the traditional methods which involved lengthy computation, consume timing and occasionally associated with accidental errors during subindex calculation. Thus, providing an accurate prediction model for WQI is highly required. Recently, the artificial neural networks (ANNs) have been examined for similar prediction applications and exhibited a remarkable ability to capture the nonlinearity pattern between predictors and predictand. In the current research, two different ANN algorithms, namely radial basis function neural network (RBFNN) and back propagation neural networks models, have been applied to examine and mimic the relationship of WQI with the water quality variables in a tropical environment (Malaysia). The input variables categorized into two different architectures and have been inspected. In addition, comprehensive analysis for the performance evaluation and the sensitivity analysis of the variables have been conducted. The results achieved are positively promising with high performance accuracy belonging to RBFNN model for both scenarios. Furthermore, the proposed approach offers an effective alternative to compute and predict WQI, to the fact that WQI manual calculation methods involved lengthy computations, transformations, use of various subindex formulae for each value of the constituent water quality variables, and consuming time. Artificial neural networks Water quality index Tropical environment RBFNN BPNN Water quality variables Sharqi, Saadi Shartooh aut Yaseen, Zaher Mundher aut Afan, Haitham Abdulmohsin aut Hussain, Aini aut Elshafie, Ahmed aut Enthalten in Neural computing & applications Springer London, 1993 28(2016), Suppl 1 vom: 08. Juni, Seite 893-905 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:28 year:2016 number:Suppl 1 day:08 month:06 pages:893-905 https://doi.org/10.1007/s00521-016-2404-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 28 2016 Suppl 1 08 06 893-905 |
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10.1007/s00521-016-2404-7 doi (DE-627)OLC2025599412 (DE-He213)s00521-016-2404-7-p DE-627 ger DE-627 rakwb eng 004 VZ Hameed, Mohammed verfasserin aut Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2016 Abstract The management of river water quality is one the most significant environmental challenges. Water quality index (WQI) describes several water quality variables at a certain aquatic environment and time. Classically, WQI is commonly computed using the traditional methods which involved lengthy computation, consume timing and occasionally associated with accidental errors during subindex calculation. Thus, providing an accurate prediction model for WQI is highly required. Recently, the artificial neural networks (ANNs) have been examined for similar prediction applications and exhibited a remarkable ability to capture the nonlinearity pattern between predictors and predictand. In the current research, two different ANN algorithms, namely radial basis function neural network (RBFNN) and back propagation neural networks models, have been applied to examine and mimic the relationship of WQI with the water quality variables in a tropical environment (Malaysia). The input variables categorized into two different architectures and have been inspected. In addition, comprehensive analysis for the performance evaluation and the sensitivity analysis of the variables have been conducted. The results achieved are positively promising with high performance accuracy belonging to RBFNN model for both scenarios. Furthermore, the proposed approach offers an effective alternative to compute and predict WQI, to the fact that WQI manual calculation methods involved lengthy computations, transformations, use of various subindex formulae for each value of the constituent water quality variables, and consuming time. Artificial neural networks Water quality index Tropical environment RBFNN BPNN Water quality variables Sharqi, Saadi Shartooh aut Yaseen, Zaher Mundher aut Afan, Haitham Abdulmohsin aut Hussain, Aini aut Elshafie, Ahmed aut Enthalten in Neural computing & applications Springer London, 1993 28(2016), Suppl 1 vom: 08. Juni, Seite 893-905 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:28 year:2016 number:Suppl 1 day:08 month:06 pages:893-905 https://doi.org/10.1007/s00521-016-2404-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 28 2016 Suppl 1 08 06 893-905 |
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Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia |
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Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia |
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Hameed, Mohammed |
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Hameed, Mohammed Sharqi, Saadi Shartooh Yaseen, Zaher Mundher Afan, Haitham Abdulmohsin Hussain, Aini Elshafie, Ahmed |
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application of artificial intelligence (ai) techniques in water quality index prediction: a case study in tropical region, malaysia |
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Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia |
| abstract |
Abstract The management of river water quality is one the most significant environmental challenges. Water quality index (WQI) describes several water quality variables at a certain aquatic environment and time. Classically, WQI is commonly computed using the traditional methods which involved lengthy computation, consume timing and occasionally associated with accidental errors during subindex calculation. Thus, providing an accurate prediction model for WQI is highly required. Recently, the artificial neural networks (ANNs) have been examined for similar prediction applications and exhibited a remarkable ability to capture the nonlinearity pattern between predictors and predictand. In the current research, two different ANN algorithms, namely radial basis function neural network (RBFNN) and back propagation neural networks models, have been applied to examine and mimic the relationship of WQI with the water quality variables in a tropical environment (Malaysia). The input variables categorized into two different architectures and have been inspected. In addition, comprehensive analysis for the performance evaluation and the sensitivity analysis of the variables have been conducted. The results achieved are positively promising with high performance accuracy belonging to RBFNN model for both scenarios. Furthermore, the proposed approach offers an effective alternative to compute and predict WQI, to the fact that WQI manual calculation methods involved lengthy computations, transformations, use of various subindex formulae for each value of the constituent water quality variables, and consuming time. © The Natural Computing Applications Forum 2016 |
| abstractGer |
Abstract The management of river water quality is one the most significant environmental challenges. Water quality index (WQI) describes several water quality variables at a certain aquatic environment and time. Classically, WQI is commonly computed using the traditional methods which involved lengthy computation, consume timing and occasionally associated with accidental errors during subindex calculation. Thus, providing an accurate prediction model for WQI is highly required. Recently, the artificial neural networks (ANNs) have been examined for similar prediction applications and exhibited a remarkable ability to capture the nonlinearity pattern between predictors and predictand. In the current research, two different ANN algorithms, namely radial basis function neural network (RBFNN) and back propagation neural networks models, have been applied to examine and mimic the relationship of WQI with the water quality variables in a tropical environment (Malaysia). The input variables categorized into two different architectures and have been inspected. In addition, comprehensive analysis for the performance evaluation and the sensitivity analysis of the variables have been conducted. The results achieved are positively promising with high performance accuracy belonging to RBFNN model for both scenarios. Furthermore, the proposed approach offers an effective alternative to compute and predict WQI, to the fact that WQI manual calculation methods involved lengthy computations, transformations, use of various subindex formulae for each value of the constituent water quality variables, and consuming time. © The Natural Computing Applications Forum 2016 |
| abstract_unstemmed |
Abstract The management of river water quality is one the most significant environmental challenges. Water quality index (WQI) describes several water quality variables at a certain aquatic environment and time. Classically, WQI is commonly computed using the traditional methods which involved lengthy computation, consume timing and occasionally associated with accidental errors during subindex calculation. Thus, providing an accurate prediction model for WQI is highly required. Recently, the artificial neural networks (ANNs) have been examined for similar prediction applications and exhibited a remarkable ability to capture the nonlinearity pattern between predictors and predictand. In the current research, two different ANN algorithms, namely radial basis function neural network (RBFNN) and back propagation neural networks models, have been applied to examine and mimic the relationship of WQI with the water quality variables in a tropical environment (Malaysia). The input variables categorized into two different architectures and have been inspected. In addition, comprehensive analysis for the performance evaluation and the sensitivity analysis of the variables have been conducted. The results achieved are positively promising with high performance accuracy belonging to RBFNN model for both scenarios. Furthermore, the proposed approach offers an effective alternative to compute and predict WQI, to the fact that WQI manual calculation methods involved lengthy computations, transformations, use of various subindex formulae for each value of the constituent water quality variables, and consuming time. © The Natural Computing Applications Forum 2016 |
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