MARC details
| 000 -LEADER |
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| fixed length control field |
03361ntm a2200289 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000092313 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251117113300.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
151130t2015 my da f abm 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0000574(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
201905160932 |
| Level of effort used to assign nonsubject heading access points |
nazirah |
| Level of effort used to assign subject headings |
201711301230 |
| Level of effort used to assign classification |
fateeha |
| Level of effort used to assign subject headings |
201512011420 |
| Level of effort used to assign classification |
asma |
| -- |
201511301433 |
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asma |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKASA .M87 2015 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Nurul Murshida Mohd Sabri |
| 245 10 - TITLE STATEMENT |
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| Title |
Development of forecasting in Sungai Muda, Kuala Muda, Kedah by utilizing artificial neural network (ANN) / |
| Statement of responsibility, etc. |
Nurul Murshida Mohd Sabri |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2015 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiv, 57 p. : |
| Other physical details |
ill. (some col.) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD ROM |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Civil Engineering and Earth Resources |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Project Paper (Bachelors of Civil Engineering) -- Universiti Malaysia Pahang – 2015 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Bibliography : p. 57 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
This report deals with flood problem which is usually happened in Malaysia when it coincides with monsoon and gave harm and damages to human life, as it had took many lives each time it happens. A case study of flood is going to be conduct to analyze the pattern of water level and to determine other causes that contributes to the flood. The main aim of the study is to minimize the effect of flood problems. It is also used to develop high accuracy model utilizing Artificial Neural Network (ANN) in predicting flood. Furthermore, it used to forecast flood occasion in the study area of station number of 5606410 of Sungai Muda (Jambatan Syed Omar) which is the main river that supplies water to Kedah and Penang. Besides, it used to investigate whether water level data alone can be used to produce modelling and to determine whether ANN is functioning in the forecasting. In this case study, a computational model will be used to stimulate the input data and generate the result, which is called Artificial Neural Network. ANN, which are modelled on the operating behaviour of the brain, are tolerant of some imprecision and are especially useful for classification and function approximation or mapping problems, to which hard and fast rules cannot be applied easily. The terminology of artificial neural networks has created form an organic biological model of neural system, which it comprises an asset of joined cells, the neurons. The neurons receive impulses or response from either input cells or any other neurons. It will perform some kind of transformation of the input and then, it will transfer the outcome to other neurons or also known as output cells. The neural networks are developed from many layers of connected neurons. The result showed that input 7+1 had the highest NSC value of 0.979 with RMSE value of 288.332 for 6 hour interval time, while input 6+1 had the highest NSC value of 0.977 with RMSE value of 134.801 for 3 hour interval time. In conclusion, this research contributes toward the development of forecasting using Artificial Neural Network for flood problems |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Civil Engineering and Earth Resources |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and Colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Theses |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="http://ecollib.ump.edu.my/id/eprint/25922">http://ecollib.ump.edu.my/id/eprint/25922</a> |
| Public note |
Access in library only |