H2O Caliber: An IoT Enabled Surface Water Pollutant Assessment System with Deep Learning
Keywords:Deep learning, internet of things, random forest, regulatory monitoring, surface water pollutants
Water is one of the natural sources that indicate the health of all living organisms like plants, animals, human being, etc., of our ecosystem. For humans, it helps to maintain their body temperature. Further it protects our tissues and organs from shock and damage by cushioning our joints. The quality of water is determined by its biological, chemical, microbiological and physical characteristics. India's water bodies are becoming increasingly hazardous as the country develops and urbanizes. Around 70% of India's surface water is unsafe for human consumption, according to estimates given by commission of pollution control board (PCB). The proposed work is aimed to produce a regulatory water monitoring system in place through incorporating the appropriate wireless sensors that evaluates the quality which is achieved possibly using technology named as Internet of Things (IoT). From the IoT hardware unit designed, the quality data of water are gathered, the pollutant level and its readiness for drinking is assessed with the help of deep learning mechanism. In order to improve the prediction accuracy, an enhanced random forest algorithm is implemented and evaluated against conventional machine learning algorithms. The water bodies’ quality of present as well futuristic scenario which is to be maintained as per the standards set by PCB and other legislative support schemes is the predominant application of this work.
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