Session #4: Optimizing Water Asset Management Using Machine Learning
Expires Wed Mar 17, 2021
Aging infrastructure, urbanization trends and climate change are some of the key risks facing water supplies around the world, and present complex challenges to governments and utilities. In the era of Artificial Intelligence, several organizations have been promoting the digitization of water management, based on the premise that smart algorithms can leverage IoT data to change the paradigm for the water industry.
However, poor water management decisions can have harmful, long-term consequences on public health, property, and infrastructure; and decision makers are reluctant to trust blackbox algorithms on how to manage their systems. For models to begin making decisions previously entrusted to humans, reliable and interpretable Machine Learning becomes necessary. This session will present an overview of some of the most promising answers to the question “How can we reliably turn our data into insights?” for water utilities.
At the completion of this one-hour OJT session, participants will be able to demonstrate the following:
- Better understand the history of Artificial Intelligence and the reasons behind its recent surge in popularity
- The main types of AI models in use today
- How much data is needed for water utilities to run smart algorithms
- The requirements in terms of data quality for Machine Learning
- How to assess the performance of a Machine Learning model for a utility
- Solutions to incorporate the outputs of AI algorithms to optimize operations and planning
Our workshop facilitator is Naysan Saran, co-founder and CEO of CANN Forecast (CANN), a company that helps cities to transition towards a proactive approach to water management. CANN was founded by the winning team of the 2016 AquaHacking challenge, and is now collaborating with partners such as the Montreal Institute of Learning Algorithms, McGill University, as well as 15 municipalities across Canada. Prior to founding CANN, Naysan worked as a scientific programmer at Environment Canada, where she participated in the development and deployment of a machine learning based approach for the post-processing of numerical weather forecasts. Naysan has a background in both Computer Engineering and Mathematics, and is passionate about using artificial intelligence to help solve environmental challenges.
These 1-hour virtual sessions are offered every alternate Thursday starting at 2:00 pm. Sessions are complimentary to all OMWA members and the fee for non-members is $30.00 CAD plus HST per person per session.
If you have any specific questions regarding this OJT session, please feel free to submit them when you register and we will provide them to the workshop facilitator.
The OMWA will provide a certificate recognizing participation in these On the Job Training sessions.
Attendance may also qualify as a Continuing Knowledge Activity for the PEO PEAK program