A Deep Learning Paradigm for Detection of Harmful Algal Blooms

Abstract

Effective and cost-efficient monitoring is indispensable for ensuring environmental sustainability. Cyanobacterial Harmful Algal Blooms (CyanoHABs) are a major water quality and public health issue in inland water bodies. The recent popularity of online social media (OSM) platforms coupled with advances in cloud computing and data analytics has given rise to citizen science-based approaches to environmental monitoring. These approaches involve the lay community in the acquisition, collection and transmission of relevant data in the form of tweets, images, voice recordings and videos typically acquired using low-cost mobile devices such as smartphones or tablet computers. While cost effective, citizen science-based approaches are highly susceptible to noise, inaccuracies and missing data. In this paper we address the problem of automated detection of harmful algal blooms (HABs) via analysis of image data of inland water bodies. These image data are acquired using a variety of smartphones and communicated via popular OSM platforms such as Facebook, Twitter and Instagram. To account for the wide variations in imaging parameters and ambient environmental parameters we propose a deep learning approach to image feature extraction and classification for the purpose of HAB detection. The current system is a first step in the design of an automated early detection, warning and rapid response system that can be adopted to mitigate the detrimental effects of CyanoHAB contamination of inland water bodies.