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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-13 |
| Number of pages | 13 |
| Journal | Neural Computing and Applications |
| DOIs | |
| Publication status | Accepted/In press - 8 Jun 2016 |
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Keywords
- Artificial neural networks
- BPNN
- RBFNN
- Tropical environment
- Water quality index
- Water quality variables
ASJC Scopus subject areas
- Artificial Intelligence
- Software
Cite this
Application of artificial intelligence (AI) techniques in water quality index prediction : a case study in tropical region, Malaysia. / Hameed, Mohammed; Sharqi, Saadi Shartooh; Yaseen, Zaher Mundher; Afan, Haitham Abdulmohsin; Hussain, Aini; Elshafie, Ahmed.
In: Neural Computing and Applications, 08.06.2016, p. 1-13.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Application of artificial intelligence (AI) techniques in water quality index prediction
T2 - a case study in tropical region, Malaysia
AU - Hameed, Mohammed
AU - Sharqi, Saadi Shartooh
AU - Yaseen, Zaher Mundher
AU - Afan, Haitham Abdulmohsin
AU - Hussain, Aini
AU - Elshafie, Ahmed
PY - 2016/6/8
Y1 - 2016/6/8
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - BPNN
KW - RBFNN
KW - Tropical environment
KW - Water quality index
KW - Water quality variables
UR - http://www.scopus.com/inward/record.url?scp=84976319262&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84976319262&partnerID=8YFLogxK
U2 - 10.1007/s00521-016-2404-7
DO - 10.1007/s00521-016-2404-7
M3 - Article
SP - 1
EP - 13
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
ER -