Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research

Sobia Iftikhar; Sania Bhatti; Zulfiqar Ali Bhatti; Mohsin Ali Memon; Faisal Memon

doi:10.33317/ssurj.232

Authors

Sobia Iftikhar Department of software engineering, Mehran University of engineering & Technology
Sania Bhatti Mehran University of engineering & Technology
Zulfiqar Ali Bhatti Department of chemical Engineering Mehran University of Engineering and technology
Mohsin Ali Memon Department of software Engineering Mehran University of Engineering and technology, Jamshoro
Faisal Memon Head Of ICT Operations Fauji Fertilizer Bin Qasim Limited Karachi

DOI:

https://doi.org/10.33317/ssurj.232

Abstract

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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