Predicting Locations of Pollution Sources using Convolutional Neural Networks

By Yiheng Chi, Guang Lin1, Nickolas D Winovich

1. Purdue University

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Pollution is a severe problem today, and the main challenge in water pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the locations of pollution sources given diffusion information of pollution in the form of array or image data. These predictions are done using machine learning. The relations between time, location, and pollution concentration are first formulated as pollution diffusion equations, which are partial differential equations (PDEs), and then deep convolutional neural networks are built and trained to solve these PDEs. The convolutional neural networks consist of convolutional layers, reLU layers and pooling layers with chosen parameters. This model is able to solve diffusion equations with an error rate of 5.09 percent. This model of convolutional neural network can be applied to locate pollution sources and is thus helpful for pollution analysis and control.

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Researchers should cite this work as follows:

  • Yiheng Chi; Guang Lin; Nickolas D Winovich (2017), "Predicting Locations of Pollution Sources using Convolutional Neural Networks,"

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