Prediction of blue water footprint at Semambu and Panching water treatment plants - Muhammad Syazwan, Mat Akhir (2019)
Water stress in the world is becoming more alarming issue due to urbanisation. There are a lot of water related researches to address this issue (Chang, Chang, Huang, & Kao, 2016; Nguyen-ky et al., 2017; Rosecrans, Nolan, & Gronberg, 2017). This study focused on blue water footprint (WFblue) assessment in Semambu and Panching water treatment plants (WTPs). Then, the total WFblue will be modelled and undergo a series of training to predict the trend by using 2 algorithms which is Artificial Neural Network (ANN) and Random Forest (RF). In order to choose the best algorithm, comparison has been made between those two algorithms. The objectives of this research are; (1) to calculate the total WFblue in Semambu & Panching WTPs which are located in Kuantan river basin for the 2015-2017 period; (2) to predict the trend of total blue water footprint Semambu & Panching water treatment plants in Kuantan river basin; and, (3) to compare the best algorithm between ANN and RF in WFblue prediction model. Water intake, rainfall utilization and total evaporation will be taken into account in total WFblue calculation where WFblue can be defined as total water consumption within a product chain. at the end result of this research, the total blue water footprint prediction trend has been produced. The predicted trend of WFblue showed a decrement from 2015 until 2017 after undergoes training in WEKA software. From this research, correct monitoring of water intake amount need to be implemented and it is suggested that all WTPs applies water footprint assessment as an approach to ensure the efficiency of water utilization