  According to the United Nations World Water Assessment Programme, every day,
2 million tons of sewage and industrial and agricultural waste are discharged
into the worlds water. In order to address this pervasive issue of increasing
water pollution, while ensuring that the global population has an efficient,
accurate, and low cost method to assess whether the water they drink is
contaminated, we propose AquaSight, a novel mobile application that utilizes
deep learning methods, specifically Convolutional Neural Networks, for
automated water impurity detection. After comprehensive training with a dataset
of 105 images representing varying magnitudes of contamination, the deep
learning algorithm achieved a 96 percent accuracy and loss of 0.108.
Furthermore, the machine learning model uses efficient analysis of the
turbidity and transparency levels of water to estimate a particular sample of
waters level of contamination. When deployed, the AquaSight system will provide
an efficient way for individuals to secure an estimation of water quality,
alerting local and national government to take action and potentially saving
millions of lives worldwide.
