Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research
Ground water contamination with Arsenic (As) is one of the foremost issues in the South Asian countries where ground water is one of the foremost sources of drinking water. In Asian countries, especially people of Pakistan living in rural areas are devouring ground water for drinking purpose, and cl...
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| Format: | Article |
| Language: | English |
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Sir Syed University of Engineering and Technology, Karachi.
2020-11-01
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| Series: | Sir Syed University Research Journal of Engineering and Technology |
| Online Access: | http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/232 |
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| author | Sobia Iftikhar Sania Bhatti Zulfiqar Ali Bhatti Mohsin Ali Memon Faisal Memon |
| author_facet | Sobia Iftikhar Sania Bhatti Zulfiqar Ali Bhatti Mohsin Ali Memon Faisal Memon |
| author_sort | Sobia Iftikhar |
| collection | DOAJ |
| description | Ground water contamination with Arsenic (As) is one of the foremost issues in the South Asian countries where ground water is one of the foremost sources of drinking water. In Asian countries, especially people of Pakistan living in rural areas are devouring ground water for drinking purpose, and cleaned water is not accessible to them. This arsenic contaminated water is hazardous for human health. The persistence of this study is to study the increasing level of arsenic in ground water in coming years for Khairpur, Sindh Pakistan, which is also increasing the cancer rate (skin cancer, blood cancer) gradually in human body. To predict the arsenic value and cancer risk for the next five years, we have developed two models via Microsoft Azure machine learning with algorithms include Support Vector Machine (SVM), Linear Regression (LR), Bayesian Linear Regression (BLR), Boosted Decision tree (BDT), exponential smoothing ETS, Autoregressive Integrated Moving Average (ARIMA). The developed predictive model named as Arsenic Contamination and Cancer Risk Assessment Prediction Model (ACCRAP model) will help us to forecast the arsenic contamination levels and the cancer rate. The results demonstrated that BLR pose highest prediction accuracy of cancer rate among the four deployed machine learning algorithms.
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| format | Article |
| id | doaj-art-21a59fcb4cbd4b85b18d5bb0e8f682ed |
| institution | Kabale University |
| issn | 1997-0641 2415-2048 |
| language | English |
| publishDate | 2020-11-01 |
| publisher | Sir Syed University of Engineering and Technology, Karachi. |
| record_format | Article |
| series | Sir Syed University Research Journal of Engineering and Technology |
| spelling | doaj-art-21a59fcb4cbd4b85b18d5bb0e8f682ed2025-08-20T03:28:02ZengSir Syed University of Engineering and Technology, Karachi.Sir Syed University Research Journal of Engineering and Technology1997-06412415-20482020-11-01102Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic ResearchSobia Iftikhar0Sania Bhatti1Zulfiqar Ali Bhatti2Mohsin Ali Memon3Faisal Memon4Department of software engineering, Mehran University of engineering & TechnologyMehran University of engineering & TechnologyDepartment of chemical Engineering Mehran University of Engineering and technologyDepartment of software Engineering Mehran University of Engineering and technology, JamshoroHead Of ICT Operations Fauji Fertilizer Bin Qasim Limited KarachiGround water contamination with Arsenic (As) is one of the foremost issues in the South Asian countries where ground water is one of the foremost sources of drinking water. In Asian countries, especially people of Pakistan living in rural areas are devouring ground water for drinking purpose, and cleaned water is not accessible to them. This arsenic contaminated water is hazardous for human health. The persistence of this study is to study the increasing level of arsenic in ground water in coming years for Khairpur, Sindh Pakistan, which is also increasing the cancer rate (skin cancer, blood cancer) gradually in human body. To predict the arsenic value and cancer risk for the next five years, we have developed two models via Microsoft Azure machine learning with algorithms include Support Vector Machine (SVM), Linear Regression (LR), Bayesian Linear Regression (BLR), Boosted Decision tree (BDT), exponential smoothing ETS, Autoregressive Integrated Moving Average (ARIMA). The developed predictive model named as Arsenic Contamination and Cancer Risk Assessment Prediction Model (ACCRAP model) will help us to forecast the arsenic contamination levels and the cancer rate. The results demonstrated that BLR pose highest prediction accuracy of cancer rate among the four deployed machine learning algorithms. http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/232 |
| spellingShingle | Sobia Iftikhar Sania Bhatti Zulfiqar Ali Bhatti Mohsin Ali Memon Faisal Memon Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research Sir Syed University Research Journal of Engineering and Technology |
| title | Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research |
| title_full | Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research |
| title_fullStr | Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research |
| title_full_unstemmed | Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research |
| title_short | Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research |
| title_sort | groundwater arsenic and cancer risk assessment prediction model via machine learning a step towards modernizing academic research |
| url | http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/232 |
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