NDLI: Predicting Quality of River's Water Based on Algae Composition Using Artificial Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Isa, N.A.M. Hashim, F.R. Fong Wai Mei Ramli, D.A. Wan Omar, W.M. Zamli, K.Z.
Copyright Year	2006
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Univ. Sains Malaysia, Nibong Tebal (Isa, N.A.M.; Hashim, F.R.; Fong Wai Mei; Ramli, D.A.)
Abstract	Artificial neural networks which are inspired by the concept of the biological neurons are commonly used in many applications including in the field of water quality management. The neural network approaches have provided an educated solution to aid in the decision-making process for river system as well as a viable means of the forecasting for water quality parameters. This paper attempts to determine the suitability and the applicability of artificial neural networks for detecting quality of river's water based on algae composition. 21 different types of algae have been used as input data and the river's water was classified into 4 categories, namely clean, polluted, brackish and moderate. Multilayered perceptron network with three different learning algorithms have been studied. The multilayered perceptron trained using Bayesian Regularization algorithm has been proven to produce the best results with high accuracy percentage (93.50%) as compared to the Lavenberg Marquadt (93.00%) and back propagation (63.505%). Further analysis (i.e. more testing data, new architecture of neural network) will be carried out to further improve the system.
Starting Page	1340
Ending Page	1345
File Size	124014
Page Count	6
File Format	PDF
ISBN	0780397002
DOI	10.1109/INDIN.2006.275854
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-16
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	System testing Neurons Decision making Algae Artificial neural networks Rivers Bayesian methods river's water quality prediction algae composition Multilayer perceptrons Water pollution multilayered perceptron network Quality management
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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