Groundwater Arsenic and Health Risk Prediction Model using Machine Learning for T.M Khan Sindh, Pakistan

Arsenic is a natural element of the earth’s crust and is commonly distributed all over the environment in the air, water and land. It is extremely poisonous in its inorganic form. Arsenic (As) contamination is one of the leading issues in the south Asian countries, ground water is major sources of drinking water. The highest risk to public health from arsenic originates from polluted groundwater. Arsenic is naturally present at high levels in the groundwater of south Asian countries. Pakistan also one of them which is highly affected by this toxic element, especially rural areas of Sindh Pakistan, where Ground water is the only source of drinking. Due to climates changes day by day value of arsenic is increased in Ground water, that effects the human health in form of many diseases like skin cancer, blood cancer. The purpose of this study is to figure out the increasing level of Arsenic and Cancer rate in Tando Muhamad Khan Sindh Pakistan for next coming five years. For this we have developed model using Microsoft Azure Machine learning Techniques and algorithms including Bayesian Linear Regression (BLR), support vector machine (SVM), Linear Regression (LR), Boosted Decision tree (BDT), exponential smoothing ETS, Autoregressive Integrated Moving Average (ARIMA). Developed model will help us to forecast the increasing rate of Arsenic and its effects on human health in form of cancer.