Human Interpretable Machine Learning for Smart Water Management
Speaker: Naysan Saran - Founder, CANN Forecast
Abstract:
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, human interpretable Machine Learning becomes necessary.
This talk will present an overview of some of the most promising answers to the question “Why did the model take this decision?” in environmental modelling.
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