Are Supervised Learning Algorithims The Key To A Paradigm Shift In The Way We Measure Air Pollution?
Thanks to Dr Pete Edwards from the University of York for his presentation on 14th November 2017 'Are supervised learning algorithims the key to a paradigm shift in the way we measure air pollution?'
Abstract: Low cost chemical sensors may prove to be a disruptive technology for air pollution measurements. The potential for these technologies is huge, enabling measurements on previously unachievable spatial scales and providing affordable tools to help tackle one of the largest environmental health risks in the developing world. Recent academic scrutiny has highlighted several issues with the relatively simple analytical methods used in these sensors, compared with traditional monitoring equipment, and methods to overcome these challenges need to be developed before they can reach their full potential. One of the most significant of these challenges is the problem of multiple cross-interferences on sensor signals. Correcting for these interferences with supporting high quality observations or regular calibration rapidly offsets their low cost advantage. The application of supervised machine learning algorithms to the modelling of complex sensor responses has shown significant skill in extracting useful signals, and could be the key to unlocking the technologies full potential.