02507ntm a2200373 i 4500 0 0 lcc 0 1 REF 10000 10000 REF 2020-07-10 0 FKASA .F395 2019 r Bc. T000000747 2020-10-08 00:00:00 1 2020-07-10 PSM 0 0 lcc 0 0 REF 10000 10000 2020-07-10 0 CD12601 T000000748 2020-11-18 00:00:00 1 2020-07-10 PSM 92584 92590 MY-KuUP 20251125105553.0 t||||fr|||| 000 0 ta 200710t20192019my a|||fram|| 000 0 eng d THE0008729(Local) hardback UMP eng UMP rda FKASA .F395 2019 r Bc. Siti Fazlina Mohd Suhaimi, author. Prediction of grey water footprint of Sungai Lembing, Bukit Sagu and Bukit Ubi water treatment plants / Siti Fazlina Mohd Suhaimi Kuantan, Pahang : UMP, 2019 © 2019 xii, 80 pages : illustrations (some color) ; 30 cm. + 1 CD-ROM text rdacontent 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 – 2019 Includes bibliographical references The most important factors affecting water scarcity in local and global and the availability of fresh water resources are not only a growing world population but also an increasing water demand. From this study, the level pollution of water in Kuantan river basin is recorded according to each water treatment plant (WTP) and grey water footprint assessment was used as an approach to account the total amount of freshwater used to assimilate the pollutant‟s concentration. Hence, this study is aimed to calculate the total grey water footprint, to predict the trend of total grey water footprint and to compare the best algorithm between Artificial Neural Network (ANN) and Bayesian Networks (BN) in grey water footprint prediction at Sungai Lembing WTP, Bukit Sagu WTP and Bukit Ubi WTP in 2015 until 2017. As the end result of this study, the total grey water footprint in Sungai Lembing, Bukit Sagu and Bukit Ubi water treatment plant in Kuantan river basin is calculated. Prediction trend of total grey water footprint in three water treatment plants has able to be produced. Artificial Neural Network (ANN) algorithm is also be chosen as the best algorithm. Faculty of Civil Engineering and Earth Resources Disertations Universities and colleges Disertations Theses lcc PSM