Design and Analysis of Deep Learning based Water Potability Prediction

According to the United Nations, having access to clean water for consumption is a fundamental human right. General Assembly in 2010. The importance of safe drinking water cannot be overstated. Unsafe drinking water access is a significant public health concern, especially in poor nations. In many regions of the world, waterborne illnesses including cholera, typhoid fever, and dysentery are important causes of illness and death. These diseases are especially dangerous for children, who are more vulnerable to their effects. In addition, a lack of access to clean drinking water can result in other health issues like stunted growth and malnutrition. Access to safe drinking-water is also a key component of effective policy for health protection at all levels, whether it is at the national, regional, or local level. Policies aimed at improving access to safe drinking-water can include measures such as water quality testing, water treatment, and the provision of clean water sources. Investments in water supply and sanitation can have a positive economic impact, especially in areas with inadequate access to clean drinking water. The costs of implementing the treatments may be outweighed by the reductions in unfavorable health impacts and medical expenses that may result from these investments. In conclusion, having access to clean drinking water is crucial for encouraging wellness, reducing waterborne illnesses, and raising standard of living in general. The input to the DNN could be the various water quality parameters, such as pH, hardness, chloride content, and total dissolved solids, while the output would be a binary classification indicating whether the water is potable or not. The DNN would need to be trained on a large and diverse dataset of water samples to accurately classify new samples.