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Evaluation of a Predictive Water Quality Model
Identifying times when water is unsafe for recreation, for drinking, or for aquatic life is a major challenge. Traditionally, sampling has been the preferred means of determining whether water is safe. Predictive modeling based on artificial intelligence (AI) is an approach that is becoming more and more popular.
Combined Sewer Overflows in Quebec : How to mitigate their effects
In this article, we will instead focus on the legislation in place in Quebec to monitor overflows, as well as on the actions that can be taken to mitigate their impacts, taking into account the effects of climate change.
Combined Sewer Overflows in Quebec
In October 2015, during the now famous Flushgate, the city of Montreal discharged eight billion liters of wastewater into the St. Lawrence River in order to carry out repairs on the sewer system. Through this article, I intend to provide answers to several questions concerning overflows, such as: “What are overflows?” and “What is their real impact on waterways in Quebec?”
A brief introduction to time series analysis
A time series is a set of repeated measurements taken sequentially over time. The main purpose of time series analysis is to predict the future of a certain process based on what has happened in the past.
Predicting Faecal Contamination: Identifying Factors Affecting the Bacteriological Quality of Water
What factors can influence the concentration of fecal indicator bacteria (FIB) at a given location and time? The following is intended to provide an overview of the factors affecting the transport, survival and redistribution of FIB in surface waters.
Leveraging AI to Protect Our Most Precious Resource: Water
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.
Data Science: 6 Common Data Types
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.
Explaining the Rise of AI in Our Lives
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.
Water Quality, a Matter of Perspective.
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.
Exploring Pathogens In Recreational Waters
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.