White Water relies on a deep neural network that takes in inputs from various samples of polluted water in order to identify abiotic or microbial particles. After being trained various types of bacteria and particulates indicative of water pollution, the app accurately names particles as one of the two types. The current model has a 90+-5% accuracy rating – higher than most commercial water quality analyses – and is capable of use in underdeveloped communities that cannot afford tradition laboratory-intensive methods of identification.

In my research, I learned that it was possible to target one of the most devastating means of death in the world – neglected zoonotic diseases. More than 60% of all preventable deaths are caused by one of 7 of the most deadly water-borne illnesses, which include malaria and schistosomiasis. While these diseases are often easily treated, they occur most frequently in areas of the world where victims are unable to afford even the simplest means of survival.

By conducting an analysis of all solutions, I discovered that the primary issue was that it was difficult to find a way to transport laboratory equipment to underdeveloped communities. The process of water quality analysis was often time intensive, preventing water samples from being sent overseas in a timely enough matter to merit the costs and resources it took for the identification to occur.

From my knowledge of computer programming and inspiration from PyCon 2016, I decided to utilize machine learning and pixel classification to help solve this problem.