Development of wwm system using mlnn-ga for ph prediction of water quality in catchment area / (Record no. 96388)

MARC details
000 -LEADER
fixed length control field 04551ntm a2200361 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field MY-KuUP
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251125105951.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field t||||fr|||| 000 0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220315s2021 my a|||frm||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number THE0009162(Local)
Qualifying information hardback
040 ## - CATALOGING SOURCE
Original cataloging agency UMP
Language of cataloging eng
Transcribing agency UMP
Description conventions rda
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) FTKEE .A96 2021 r Thesis
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Muhammad Aznil Ab Aziz,
Relator term author.
245 10 - TITLE STATEMENT
Title Development of wwm system using mlnn-ga for ph prediction of water quality in catchment area /
Statement of responsibility, etc. Muhammad Aznil Ab Aziz
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Kuantan, Pahang :
Name of producer, publisher, distributor, manufacturer UMP,
Date of production, publication, distribution, manufacture, or copyright notice 2021
264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice © 2021
300 ## - PHYSICAL DESCRIPTION
Extent xiv, 114 pages :
Other physical details illustrations (some color) ;
Dimensions 30 cm. +
Accompanying material 1 CD ROM
336 ## - CONTENT TYPE
Content type term text
Source rdacontent
336 ## - CONTENT TYPE
Content type term text
Source rdacontent
337 ## - MEDIA TYPE
Media type term unmediated
Source rdamedia
337 ## - MEDIA TYPE
Media type term computer
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term volume
Source rdacarrier
338 ## - CARRIER TYPE
Carrier type term computer disc
Source rdacarrier
347 ## - DIGITAL FILE CHARACTERISTICS
File type text file
Encoding format PDF
Source rda
500 ## - GENERAL NOTE
General note Faculty of Electrical & Electronics Engineering
502 ## - DISSERTATION NOTE
Dissertation note Thesis (Master of Science) -- Universiti Malaysia Pahang – 2021
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references
520 3# - SUMMARY, ETC.
Summary, etc. Water quality is crucial to maintain as water is a necessity in our daily life. In aquatic ecosystem, good water quality allows aquatic life to have good health and improving their productivity leads to significant benefits to the environment. However, the method of collecting data is still in manual way. Data samples need to be collected and evaluated in the laboratory. It leads the results of water quality took a long time to retrieve and cannot be obtained continuously. Hence, a system is required to monitor the water quality that capable to obtain the results fast and continuously. In this research studies, Wireless Passive Water Quality Catchment Monitoring System or WMM System is introduced to collect the water quality parameters. Five parameters are measured in the system, which are potential hydrogen (pH), temperature, light intensity of water, coordinate of collecting data, and wave velocity. Primary lake at University Malaysia Pahang is selected as the experimental area for the WWM System. However, coverage area of the lake is large and require many WWM Systems to be developed. This method is expensive in terms of budget expenditure. Thus, prediction of water quality is applied to solve this problem with a number of WWM Systems used for collecting data will be reduced. Artificial Neural Network or ANN is one of the methods that commonly used to predict the quality of water. The structure and function of ANN is based on biological neural network. ANN process consists of three layer which are input layer, hidden layer, and output layer and weight of the network usually adjusted by using back-propagation (BP) technique. This technique has several advantages which are fast, simple, and easy to program. However, there are some weaknesses identified in BP such as being often trapped in local minima, sensitive to noisy data, and the performance of BP on certain problems is dependent on the input data. In this research study, Multi-Layer Neural Network optimized by Genetic Algorithm (MLNN-GA) is introduced to predict the water quality. This model will predict the pH value obtained by WWM System based on the value of pH surrounding within 100m2 area. MLNN-GA contain three hidden layers and Genetic Algorithm (GA) will optimize the weight of neural network during the training process. GA has several advantages, which are less often trapped in local minima and can operate in a complex process such as extended neural network. In training process of MLNN-GA, number of neurons in a hidden layer will be tested to obtain the best accuracy. The parameter of pH is chosen for predicting water quality as pH is one important parameter that can influence the condition of water. MLNN-GA is a new method proposed in this research study for predicting the water quality in terms of pH value. The results of prediction will be compared with BP technique and the average value of pH within the 100m2 area. The results of prediction obtained by MLNN-GA model with 99.64% accuracy has a significant potential to be used in the future and WWM System has a great potential to be used in monitoring water quality.
610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Faculty of Electrical & Electronics Engineering Technology
General subdivision Dissertations
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Universities and colleges
General subdivision Dissertations
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Theses
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Thesis
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection Home library Current library Shelving location Date acquired Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
  Not lost Library of Congress Classification     Reference UMPLIB PEKAN UMPLIB PEKAN Reference 15/03/2022   FTKEE .A96 2021 r Thesis T000001529 09/05/2022 1 15/03/2022 Thesis
  Not lost Library of Congress Classification     Reference UMPLIB PEKAN UMPLIB PEKAN Reference 15/03/2022   CD12934 T000001530 04/07/2022 1 15/03/2022 Thesis

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