Prediction of grey water footprint by using artificial neural network and random forest
Water Treatment Plant (WTP) is a place to treat raw water from earth resources like river, lake, ocean and underground water which will supply to society. However, the water treatment plant in Malaysia still using conventional WTP to treat the raw water. So, the footprint of water usage is not yet i...
| Main Author: | |
|---|---|
| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30306/ http://umpir.ump.edu.my/id/eprint/30306/1/Prediction%20of%20grey%20water%20footprint%20by%20using%20artificial%20neural%20network%20and%20random%20forest.pdf |
| _version_ | 1848823479485333504 |
|---|---|
| author | Ruziana, Kamarzaman |
| author_facet | Ruziana, Kamarzaman |
| author_sort | Ruziana, Kamarzaman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Water Treatment Plant (WTP) is a place to treat raw water from earth resources like river, lake, ocean and underground water which will supply to society. However, the water treatment plant in Malaysia still using conventional WTP to treat the raw water. So, the footprint of water usage is not yet in the recorded. By using water footprint (WF) approach, the grey water footprint (WFgrey) is assessed in order to evaluate the raw water quality. Water footprint is the indicator of freshwater use that looks not only at direct water use but also at indirect water use. Meanwhile, grey water footprint is the amount of freshwater needed to assimilate the pollutant. This study focused in calculating grey water footprint of two WTPs which is Semambu WTP and Panching WTP. The study period start from 2015 until 2017. There are factors influenced the calculation of total grey water footprint such as the concentration of pollutant considered, the discharge rate and the amount of water intake. In this study, the increment of total grey water footprint is due to high amount iron and ammonia in water intake which mostly come from bauxite mining in Kuantan. However, the overall grey water footprint trend shows decrement which is good sign for river sustainability. As a conclusion, the total grey water footprint is predicted to decrease in future. But, there is also chance for the grey water to increase if the river is polluted. This study suggested that the industrial activities near the river must be carried out with Standard Operation Procedure and the factory also must treated the effluent before discharge it into river. |
| first_indexed | 2025-11-15T02:57:47Z |
| format | Undergraduates Project Papers |
| id | ump-30306 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:57:47Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-303062023-11-03T02:18:42Z http://umpir.ump.edu.my/id/eprint/30306/ Prediction of grey water footprint by using artificial neural network and random forest Ruziana, Kamarzaman TD Environmental technology. Sanitary engineering Water Treatment Plant (WTP) is a place to treat raw water from earth resources like river, lake, ocean and underground water which will supply to society. However, the water treatment plant in Malaysia still using conventional WTP to treat the raw water. So, the footprint of water usage is not yet in the recorded. By using water footprint (WF) approach, the grey water footprint (WFgrey) is assessed in order to evaluate the raw water quality. Water footprint is the indicator of freshwater use that looks not only at direct water use but also at indirect water use. Meanwhile, grey water footprint is the amount of freshwater needed to assimilate the pollutant. This study focused in calculating grey water footprint of two WTPs which is Semambu WTP and Panching WTP. The study period start from 2015 until 2017. There are factors influenced the calculation of total grey water footprint such as the concentration of pollutant considered, the discharge rate and the amount of water intake. In this study, the increment of total grey water footprint is due to high amount iron and ammonia in water intake which mostly come from bauxite mining in Kuantan. However, the overall grey water footprint trend shows decrement which is good sign for river sustainability. As a conclusion, the total grey water footprint is predicted to decrease in future. But, there is also chance for the grey water to increase if the river is polluted. This study suggested that the industrial activities near the river must be carried out with Standard Operation Procedure and the factory also must treated the effluent before discharge it into river. 2019-05 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30306/1/Prediction%20of%20grey%20water%20footprint%20by%20using%20artificial%20neural%20network%20and%20random%20forest.pdf Ruziana, Kamarzaman (2019) Prediction of grey water footprint by using artificial neural network and random forest. Faculty of Civil Engineering and Earth Resources, Universiti Malaysia Pahang. |
| spellingShingle | TD Environmental technology. Sanitary engineering Ruziana, Kamarzaman Prediction of grey water footprint by using artificial neural network and random forest |
| title | Prediction of grey water footprint by using artificial neural network and random forest |
| title_full | Prediction of grey water footprint by using artificial neural network and random forest |
| title_fullStr | Prediction of grey water footprint by using artificial neural network and random forest |
| title_full_unstemmed | Prediction of grey water footprint by using artificial neural network and random forest |
| title_short | Prediction of grey water footprint by using artificial neural network and random forest |
| title_sort | prediction of grey water footprint by using artificial neural network and random forest |
| topic | TD Environmental technology. Sanitary engineering |
| url | http://umpir.ump.edu.my/id/eprint/30306/ http://umpir.ump.edu.my/id/eprint/30306/1/Prediction%20of%20grey%20water%20footprint%20by%20using%20artificial%20neural%20network%20and%20random%20forest.pdf |