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Assessing Environmental Oil Spill Based on Fluorescence Images of Water Samples and Deep Learning

D. P. Liu1 *, M. Liu2 *, G. Y. Sun1, Z. Q. Zhou1, D. L. Wang1, F. He1, J. X. Li1, J. C. Xie1, R. Gettler3, E. Brunson3, J. Steevens3 **, and D. Xu1 **

  1. Department of Electrical Engineering and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, USA
  2. School of Mathematics and Statistics, Changchun University of Technology, Changchun 130024, China
  3. U.S. Geological Survey Columbia Environmental Research Center, Columbia, Missouri 65201, USA

* Co-first authors.
** Corresponding author. Tel.: +573-876-1819. E-mail address: (J. Steevens).
** Corresponding author. Tel.: +573-882-2299. E-mail address: (D. Xu).


Measuring oil concentration in the aquatic environment is essential for determining the potential exposure, risk, or injury for oil spill response and natural resource damage assessment. Conventional analytical chemistry methods require samples to be collected in the field, shipped, and processed in the laboratory, which is also rather time-consuming, laborious, and costly. For rapid field response immediately after a spill, there is a need to estimate oil concentration in near real time. To make the oil analysis more portable, fast, and cost effective, we developed a plug-and-play device and a deep learning model to assess oil levels in water using fluorescent images of water samples. We constructed a 3D-printed device to collect fluorescent images of solvent-extracted water samples using an iPhone. We prepared approximately 1,300 samples of oil at different concentrations to train and test the deep learning model. The model comprises a convolutional neural network and a novel module of histogram bottleneck block with an attention mechanism to exploit the spectral features found in low-contrast images. This model predicts the oil concentration in weight per volume based on fluorescence image. We devised a confidence interval estimator by combining gradient boosting and polymodal regressor to provide a confidence assessment of our results. Our model achieved sufficient accuracy to predict oil levels for most environmental applications. We plan to improve the device and iPhone application as a near-real-time tool for oil spill responders to measure oil in water.

Keywords: water pollution, oil spill assessment, plug-and-play device, fluorescence image, deep learning, convolutional neural network, confidence assessment

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