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

Abstract

Deteriorating water quality poses a substantial risk to human health, with billions at risk of waterborne diseases due to contamination. Insufficient water quality data augment risks as conventional monitoring methods lack comprehensive coverage. Technologies like the Internet of Things and machine learning offer real-time water quality monitoring and classification. IoT nodes often provide point data insufficient for monitoring the quality of entire water bodies. Remote sensing, though useful, has limitations such as measuring only optically active 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 of four key parameters (pH, dissolved oxygen, electrical conductivity, and temperature) along with GPS coordinates. The data is transmitted to a web portal via LoRa communication and Wi-Fi, where visualizations like data tables, trendlines and color-coded heatmaps are generated. A multilayer perceptron classifies water quality into five categories, aiding in real-time classification. A comparative analysis of various oversampling techniques has been conducted in the context of water quality classification. The AquaDrone offers a feasible solution for monitoring quality of small to medium-sized water bodies, crucial for safeguarding public health. HIGHLIGHTS AquaDrone measures four physicochemical parameters of water along with GPS coordinates to monitor water quality in real-time.; A multilayer perceptron classifies water quality into five categories, with oversampling used to enhance model training.; AquaDrone wirelessly transmits data to the web portal, where trendlines, data table and a color-coded heatmap are generated to visualize water quality.; This ensures effective real-time monitoring in small to medium-sized water bodies, protecting both water sources and human health.;

Keywords

• human health
• internet of things
• machine learning
• real-time
• unmanned surface vehicle
• water quality monitoring