MARC details
| 000 -LEADER |
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| fixed length control field |
02945ntm a2200361 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125105419.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
191105t20182018my a|||frm||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0008324(Local) |
| Qualifying information |
hardback |
| 040 ## - CATALOGING SOURCE |
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| 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) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKASA .I33 2018 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Noorfarhana Idayu Ibrahim, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Flood forecasting by using artificial neural network (ANN) in Kuala Krai, Kelantan / |
| Statement of responsibility, etc. |
Noorfarhana Idayu Ibrahim |
| 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 |
2018 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2018 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 53 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
unmediated |
| Source |
rdamedia |
| 337 ## - MEDIA TYPE |
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| Media type term |
computer |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
volume |
| Source |
rdacarrier |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
computer disc |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 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 – 2018 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Developing flood forecasting is necessity especially for east coast peninsular Malaysia that experienced flood every year due to northeast monsoon and when it coincides with monsoon that gave harm and damages to human life. 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. A strong performance of forecasting model for water level could be a solution to minimize bad impact of flood. It is also used to develop high accuracy model utilizing Artificial Neural Network (ANN) in predicting flood. Furthermore, Artificial Neural Network (ANN) use historical data to find data pattern to make data forecasting. Historical data require generating the result by forecast a model. In the study area of station number is 5222452, Sungai Lebir at Kelantan River where hourly water level data for past 30 years dated from 1986 until 2016 that gained from Drainage and Irrigation Department, Ampang have been used to forecast hourly water level. In this study were using Multilayer Perceptron Neural Network (MLP) technique. MLP is known as a supervised feed forward back propagation learning ANN model. Besides that, three type of time interval 1, 3 and 6 hour and 6 types of data input which are 2, 3, 4, 5, 6 and 7 were analyzed. Result showed that all data input successfully achieve high accuracy forecasting result where 0.8 to 1 for NSC value were recorded as strong performance. Therefore, the best performance was occurred in one hour interval time with the architecture 2-2-1. |
| 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 |
Disertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| General subdivision |
Theses |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Restricted Collection |