Monitoring the Industrial waste polluted stream - Integrated analytics and machine learning for water quality index assessment
文献类型：期刊论文
| 作者 | Ejaz, Ujala5,6; Khan, Shujaul Mulk6; Jehangir, Sadia5,6; Ahmad, Zeeshan4; Abdullah, Abdullah6; Iqbal, Majid3,6; Khalid, Noreen2; Nazir, Aisha1; Svenning, Jens -Christian5 |
| 刊名 | JOURNAL OF CLEANER PRODUCTION
 |
| 出版日期 | 2024-04-15 |
| 卷号 | 450页码:19 |
| 关键词 | Industrial wastewater Machine learning Assessment and monitoring Water Quality Index Artificial Intelligence |
| ISSN号 | 0959-6526 |
| DOI | 10.1016/j.jclepro.2024.141877 |
| 通讯作者 | Khan, Shujaul Mulk(smkhan@qau.edu.pk) ; Ahmad, Zeeshan(zeeshanahmad@xtbg.ac.cn) ; Svenning, Jens -Christian(svenning@bio.au.dk) |
| 英文摘要 | The Water Quality Index (WQI) is a primary metric used to evaluate and categorize surface water quality which plays a crucial role in the management of fresh water resources. Machine Learning (ML) modeling offers potential insights into water quality index prediction. This study employed advanced ML models to get potential insights into the prediction of water quality index for the Aik-Stream, an industrially polluted natural water resource in Pakistan with 19 input water quality variables aligning them with surrounding land use and anthropogenic activities. Six machine learning algorithms, i.e. Adaptive Boosting (AdaBoost), K -Nearest Neighbors (K -NN), Gradient Boosting (GB), Random Forests (RF), Support Vector Regression (SVR), and Bayesian Regression (BR) were employed as benchmark models to predict the Water Quality Index (WQI) values of the polluted stream to achieve our objectives. For model calibration, 80% of the dataset was reserved for training, while 20% was set aside for testing. In our comparative analyses of predictive models for water quality index, the Gradient Boost (GB) model stood out the fittest for its precision, utilizing a combination of just seven parameters (chemical oxygen demand, total organic carbon, oil & grease, Ammonia- nitrogen, arsenic, nickel and zinc), surpassing other models by achieving better results in both training (R 2 = 0.88, RMSE = 7.24) and testing (R 2 = 0.85, RMSE = 8.67). Analyzing feature importance showed that all the selected variables, except for NO 3 N, TDS and temperature had an impact on the accuracy of the models predictions. It is concluded that the application of machine learning to assess water quality in polluted environments enhances accuracy and facilitates real-time tracking, enabling proactive risk mitigations. |
| WOS关键词 | RIVER CHENAB ; CONTAMINATION ; ALGORITHMS ; MANAGEMENT ; PAKISTAN ; SOILS |
| 资助项目 | Commission Pakistan ; International Research Support Initiative Program (IRSIP) - Danish National Research Foundation[DNRF173] ; VILLUM FONDEN[16549] |
| WOS研究方向 | Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001218933900001 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | Commission Pakistan ; International Research Support Initiative Program (IRSIP) - Danish National Research Foundation ; VILLUM FONDEN |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205875]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Khan, Shujaul Mulk; Ahmad, Zeeshan; Svenning, Jens -Christian |
| 作者单位 | 1.Univ Punjab, Inst Bot, Environm Biotechnol Res Lab, Lahore, Pakistan 2.Govt Coll Women Univ, Dept Bot, Sialkot, Pakistan 3.Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 4.Chinese Acad Sci, CAS Key Lab Trop Forest Ecol, Xishuangbanna Trop Bot Garden, Mengl 666303, Peoples R China 5.Aarhus Univ, Ctr Ecol Dynam Novel Biosphere ECONOVO, Dept Biol, Ny Munkegade 114, DK-8000 Aarhus, Denmark 6.Quaid I Azam Univ, Dept Plant Sci, Islamabad 45320, Pakistan |
| 推荐引用方式 GB/T 7714 | Ejaz, Ujala,Khan, Shujaul Mulk,Jehangir, Sadia,et al. Monitoring the Industrial waste polluted stream - Integrated analytics and machine learning for water quality index assessment[J]. JOURNAL OF CLEANER PRODUCTION,2024,450:19. |
| APA | Ejaz, Ujala.,Khan, Shujaul Mulk.,Jehangir, Sadia.,Ahmad, Zeeshan.,Abdullah, Abdullah.,...&Svenning, Jens -Christian.(2024).Monitoring the Industrial waste polluted stream - Integrated analytics and machine learning for water quality index assessment.JOURNAL OF CLEANER PRODUCTION,450,19. |
| MLA | Ejaz, Ujala,et al."Monitoring the Industrial waste polluted stream - Integrated analytics and machine learning for water quality index assessment".JOURNAL OF CLEANER PRODUCTION 450(2024):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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