02631nam a22003617a 4500 0 0 lcc 0 1 REF 10000 10000 REF 2019-10-16 0 FKASA .Z87 2018 r Bc. 0000127023 2019-10-16 00:00:00 2019-10-16 THESIS 0 0 lcc 0 1 REF 10000 10000 2019-10-16 0 CD11932 0000127024 2020-07-16 00:00:00 2019-10-16 THESIS 90899 90905 MY-KuUP 20251125105406.0 ta 191016t20182018my a|||| |||| 00| 0 eng d THE0008229(Local) UMP eng UMP rda FKASA .Z87 2018 r Bc. Zuriani Halwa Abdullah, author. Flood forecasting modelling using artificial neural network (ann) in Chukai Kemaman / Zuriani Halwa Abdullah Kuantan, Pahang : UMP, 2018 © 2018 xi, 34 pages : Illustrations (some color) ; 30 cm. + 1 CD-ROM rdacontent text rdacontent text rdamedia unmediated rdamedia computer rdacarrier volume rdacarrier computer disc rda text file PDF Faculty of Civil Engineering and Earth Resources Project Paper (Bachelor of Civil Engineering) -- Universiti Malaysia Pahang – 2018 Includes bibliographical references Flood has a big potential to obliterate away an entire city, area or coastline and can cause substantial damages to life and properties. Flood is the most intense and the only natural disaster happens in Malaysia. Floods occur when the amount of water flowing from a catchment surpass the capacity of its drains, creeks, and rivers. This process begins with rainfall, but is an affected by many other factors. Flood can happen at any time of the year and are caused by heavy rainfall or non-stop rainfall. Hence, flood forecasting is requiring providing better warning for people. Artificial Neural Network (ANN) is used to develop accurate flood forecasting modelling. ANN is chosen to be use because of its simplicity, easy implementation and demonstrated success in forecasting studies, since ANN have been found to be powerful tools for solving different problems in variety of applications. Artificial Neural Network (ANN) also can be used to develop accurate flood forecasting modelling. This study is based on predicting flood event at Chukai, Kemaman, Terengganu. In Terengganu, flood happen due to monsoon rainfall and cause of backwater phenomenon during high tides. The hourly water levels for 20 to 30 years are needed to develop flood forecasting. The hourly water level data can be taken from the Department of Irrigation and Drainage (DID), Malaysia. Faculty of Civil Engineering and Earth Resources Dissertations Universities and colleges Theses lcc RESTRICT