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999			_c91067 _d91073
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008			191105t20182018my a|||frm||| 000 0 eng d
020			_aTHE0008324(Local) _qhardback
040			_aUMP _beng _cUMP _erda
090			_aFKASA .I33 2018 r Bc.
100	0		_aNoorfarhana Idayu Ibrahim, _eauthor.
245	1	0	_aFlood forecasting by using artificial neural network (ANN) in Kuala Krai, Kelantan / _cNoorfarhana Idayu Ibrahim
264		1	_aKuantan, Pahang : _bUMP, _c2018
264		4	_c© 2018
300			_axi, 53 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-ROM
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aFaculty of Civil Engineering and Earth Resources
502			_aProject Paper (Bachelors of Civil Engineering) -- Universiti Malaysia Pahang – 2018
504			_aIncludes bibliographical references
520	3		_aDeveloping 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	2	0	_aFaculty of Civil Engineering and Earth Resources _xDissertations
650		0	_aUniversities and colleges _xDisertations
650		0	_xTheses
942			_2lcc _cRESTRICT