Paper Info
Title
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 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.
Year
DOI
Venue
2017
10.1007/s00521-016-2404-7
Neural Computing and Applications
Keywords
Field
DocType
Artificial neural networks, Water quality index, Tropical environment, RBFNN, BPNN, Water quality variables
Radial basis function neural,Back propagation neural network,Artificial intelligence,River water,Artificial neural network,Machine learning,Mathematics,Water quality,Computation
Journal
Volume
Issue
ISSN
28
S-1
1433-3058
Citations 
PageRank 
References 
7
0.63
7
Authors
6
Name
Order
Citations
PageRank
Mohammed Hameed170.63
Saadi Shartooh Sharqi270.63
zaher mundher yaseen3359.67
haitham abdulmohsin afan4222.81
Aini Hussain513032.24
Ahmed El-Shafie624725.83