02945ntm a2200361 i 4500 0 0 lcc 0 1 REF 10000 10000 REF 2019-11-05 0 FKASA .I33 2018 r Bc. 0000127222 2020-09-30 00:00:00 2019-11-05 THESIS 0 0 lcc 0 1 REF 10000 10000 REF 2019-11-05 0 CD12106 0000127223 2020-11-20 00:00:00 2019-11-05 THESIS 91067 91073 MY-KuUP 20251125105419.0 t||||fr|||| 000 0 ta 191105t20182018my a|||frm||| 000 0 eng d THE0008324(Local) hardback UMP eng UMP rda FKASA .I33 2018 r Bc. Noorfarhana Idayu Ibrahim, author. Flood forecasting by using artificial neural network (ANN) in Kuala Krai, Kelantan / Noorfarhana Idayu Ibrahim Kuantan, Pahang : UMP, 2018 © 2018 xi, 53 pages : illustrations (some color) ; 30 cm. + 1 CD-ROM text rdacontent unmediated rdamedia computer rdamedia volume rdacarrier computer disc rdacarrier text file PDF rda Faculty of Civil Engineering and Earth Resources Project Paper (Bachelors of Civil Engineering) -- Universiti Malaysia Pahang – 2018 Includes bibliographical references 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. Faculty of Civil Engineering and Earth Resources Dissertations Universities and colleges Disertations Theses lcc RESTRICT