Water management is a complicated challenge, and recent progress in artificial intelligence could clearly help with part of this. This article presents some food for thought for us to be able to fully take advantage of the data revolution to optimize one of our most precious resources: blue gold.
Before any project, it is crucial to understand the difference between the following data types: numerical, categorical, continuous, discrete, nominal and ordinal. This knowledge is key to fully grasp the statistical nature of the available data and to properly handle any given features. Despite its simplicity, this step is essential to achieve a robust and meaningful data analysis. In fact, data types usually dictate which imputation strategies, statistical measurements, plot designs and algorithms are the most appropriate to use.
The rise of Artificial Intelligence (AI) in recent years has become quite apparent and has reached our daily lives. Today, in 2020, we are surrounded by AI applications. It’s present in our web browsers, our social media platforms, our cell phone cameras and it’s even found in our cars.
We often tend to forget that the idea of “good” water is a matter of perspective. Lakes and rivers are used for a variety of functions such as drinking water, swimming, irrigation and therefore each person’s perspective of “good” water quality comes from how they use it. When assessing the health of a waterway, we need to be able to evaluate these different perspectives as a whole, but not much is known about how these perspectives overlap in terms of safety standards.
As a result of modern water treatment and sanitation methods in developed countries, exposure to pathogens transmitted through contaminated drinking water is uncommon. Through this blog post, I intend to explore the world of pathogens present in recreational waters in greater detail.