Journal cover Journal topic
Proceedings of the ICA
Journal topic
Volume 1
Proc. Int. Cartogr. Assoc., 1, 50, 2018
https://doi.org/10.5194/ica-proc-1-50-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Proc. Int. Cartogr. Assoc., 1, 50, 2018
https://doi.org/10.5194/ica-proc-1-50-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  16 May 2018

16 May 2018

Supervised Machine Learning for Regionalization of Environmental Data: Distribution of Uranium in Groundwater in Ukraine

Michael Govorov1, Gennady Gienko2, and Viktor Putrenko3 Michael Govorov et al.
  • 1Vancouver Island University, Nanaimo, BC, Canada
  • 2University of Alaska Anchorage, Anchorage, AL, USA
  • 3National Technical University of Ukraine, Kiev, Ukraine

Keywords: Deep Machine Learning, Regionalization, Spatial Effect, Uranium, Groundwater

Abstract. In this paper, several supervised machine learning algorithms were explored to define homogeneous regions of con-centration of uranium in surface waters in Ukraine using multiple environmental parameters. The previous study was focused on finding the primary environmental parameters related to uranium in ground waters using several methods of spatial statistics and unsupervised classification. At this step, we refined the regionalization using Artifi-cial Neural Networks (ANN) techniques including Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Convolutional Neural Network (CNN). The study is focused on building local ANN models which may significantly improve the prediction results of machine learning algorithms by taking into considerations non-stationarity and autocorrelation in spatial data.

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