Prediction of drinking water quality using back-propagation neural network / Wan Radziah Wan Abdul Rahim

Wan Abdul Rahim, Wan Radziah (2006) Prediction of drinking water quality using back-propagation neural network / Wan Radziah Wan Abdul Rahim. Degree thesis, Universiti Teknologi MARA.

Download

[thumbnail of TB_WAN RADZIAH WAN ABDUL RAHIM CS 06_5 P01.pdf]
Preview
Text
TB_WAN RADZIAH WAN ABDUL RAHIM CS 06_5 P01.pdf

Download (55kB) | Preview

Abstract

Water is very important in our daily life. Without safety water every living
thing in this planet will die. People can survive 7 to 10 days without foods but
can survive 1 to 3 days without water ("National Ag Safety Database", 2002).
Human bodies consist of 70% of water. This statement proved that water is
very important element in this planet to make sure every living thing can
continue their life. Nowadays, people are concerned about water sources such
as from fresh water, ground water and river for drink. Some of them are not
safe and does not achieve standard of safe and healthy to drink and use. The
purposed of this project is to solve this problem by predict the drinking water
quality using Artificial Neural Network (ANN). It is focus on pH, manganese,
iron and turbidity of water. A Back-propagation neural network is used in this
project and it is fully develop using MATLAB. With the development of
drinking water quality prediction, it provides the result either the water quality
or not based on the trained water data. Within this result, the water company
can improved the drinking water quality level to make sure the consumer get
the healthy water.

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email
Wan Abdul Rahim, Wan Radziah
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Item ID: 1009
URI: https://ir.uitm.edu.my/id/eprint/1009

Fulltext

Fulltext is available at:
  • UNSPECIFIED
  • ID Number

    1009

    Indexing


    View in Google Scholar

    Edit Item
    Edit Item