Water footprint of water supply for industrial sector within kuantan river basin - Noraini, Yussof (2023)
In Malaysia, industrialization is an economic activity generating national income and currently showing rapid growth. Hence, it can be anticipated that the water demand from industrial activities is increasingly intensified. Since 2016, Kuantan City has been facing water shortage problems due to several circumstances such as economic activities. Thus, it is important for water regulators to manage the water supply efficiently. This study aims to determine and predict the water footprint (WF) of water supply for industrial sectors within the Kuantan River Basin (KRB). All calculations followed the WF Manual and estimated two industrial areas within KRB. Meanwhile, the deep machine learning tool Artificial Neural Network (ANN) was utilized for prediction purposes. The factor influencing the calculation of total WFi can be categorized into environmental, social, and economic factors. The results indicated that from 2015 to 2019, the heavy industry category contributed the highest value of blue water footprint (WFbi), followed by the medium and light industry categories. The highest value of WFbi for the heavy industry was in 2016, with 20.21 million m3, which decreases in 2019 by 17%. This study also shows that the manufacturing activity of all industry categories consumes the most of KRB’s water supply which is responsible for almost 60% of the water consumption in the industrial sector. Regarding the calculated industrial grey water footprint (WFgi), the light industry category depicted the highest value, followed by medium and heavy industries. The trend of WFgi for the light industry category showed an increment from 2015 and reached a peak in 2017 with 1.26 million m3. However, in 2019, the trend decreased by 54% - 62%. Meanwhile, the total industrial water footprint (WFi) was the highest in 2016 at 27.14 million m3. It might be due to the largest volume of water consumption and the greatest number of industries during that particular year. The results also indicated that the heavy industry category contributed the highest value of total WFi, followed by the medium and light industries. In attempting to predict the total WFi trend using ANN, Semambu and Panching industrial areas showed an increment of 33% and 100%, respectively. The predicted value for WFbi falls on average by 30% in Semambu industrial area. It rises to 85% in the Panching industrial area. Meanwhile, the predicted value of WFgi increased on average by 90% for both areas. Thus, it can be concluded that an increasing heavy industry activity contributed to the increasing calculation of current total WFi and the prediction trend. Finally, the environment factor could be defined as the significant factor that influences the total of WFi compared to social and economy factors. In conclusion, this study highlights the significance of WF determination in assisting the state government in managing the water supply appropriation within the river basin as one way to ensure the sustainability of our water resources.