Unmanned surface vehicle for intelligent water quality assessment to promote sustainable human health

Abstract

Deteriorating water quality poses significant health risks globally, with billions at risk of waterborne diseases due to contamination. Limited data on water quality heightens these risks as conventional monitoring methods lack comprehensive coverage. While technologies like Internet of Things and machine learning offer real-time monitoring capabilities, they often provide point data insufficient for assessing entire water bodies. Remote sensing, though useful, has limitations such as measuring only optical parameters and being affected by climate and resolution issues. To address these challenges, an unmanned surface vehicle named ‘AquaDrone’ has been developed. AquaDrone traverses water bodies, collecting data on four key parameters (pH, dissolved oxygen, electrical conductivity, and temperature) along with GPS coordinates. These data are transmitted to a web portal via LoRa communication and Wi-Fi, where visualizations like trendlines and color-coded heatmaps are generated. A multilayer perceptron classifies water quality into five categories, aiding in real-time assessment. The AquaDrone system offers a feasible solution for monitoring small to medium-sized water bodies, crucial for safeguarding public health.