2019/07/17 by Ankit Gupta, Gupta, Ankit, Elliott Ruebush +1 · 1 citation
Engineering · Environmental Science · #Air Quality Monitoring and Forecasting #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Water Quality Monitoring Technologies #Water Systems and Optimization
paper · pdf · doi:10.48550/arxiv.1907.07573
openalex publication_date 2019/07/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
According to the United Nations World Water Assessment Programme, every day, 2 million tons of sewage and industrial and agricultural waste are discharged into the worlds water. In order to address this pervasive issue of increasing water pollution, while ensuring that the global population has an efficient, accurate, and low cost method to assess whether the water they drink is contaminated, we propose AquaSight, a novel mobile application that utilizes deep learning methods, specifically Convolutional Neural Networks, for automated water impurity detection. After comprehensive training with a dataset of 105 images representing varying magnitudes of contamination, the deep learning algorithm achieved a 96 percent accuracy and loss of 0.108. Furthermore, the machine learning model uses efficient analysis of the turbidity and transparency levels of water to estimate a particular sample of waters level of contamination. When deployed, the AquaSight system will provide an efficient way for individuals to secure an estimation of water quality, alerting local and national government to take action and potentially saving millions of lives worldwide.