<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Amphibian species have been considered as useful ecological indicators. They are used as indicators of environmental contamination, ecosystem health and habitat quality., Amphibian species are sensitive to changes in the aquatic environment and therefore, may form the basis for the classification of water bodies. Water bodies in which there are a large number of amphibian species are especially valuable even if they are located in urban areas. The automation of the classification process allows for a faster evaluation of the presence of amphibian species in the water bodies. Three machine-learning methods (artificial neural networks, decision trees and the k-nearest neighbours algorithm) have been used to classify water bodies in Chorzów – one of 19 cities in the Upper Silesia Agglomeration. In this case, classification is a supervised data mining method consisting of several stages such as building the model, the testing phase and the prediction. Seven natural and anthropogenic features of water bodies (e.g. the type of water body, aquatic plants, the purpose of the water body (destination), position of the water body in relation to any possible buildings, condition of the water body, the degree of littering, the shore type and fishing activities) have been taken into account in the classification. The data set used in this study involved information about 71 different water bodies and 9 amphibian species living in them. The results showed that the best average classification accuracy was obtained with the multilayer perceptron neural network.</p></abstract>

Amphibian species have been considered as useful ecological indicators. They are used as indicators of environmental contamination, ecosystem health and habitat quality., Amphibian species are sensitive to changes in the aquatic environment and therefore, may form the basis for the classification of water bodies. Water bodies in which there are a large number of amphibian species are especially valuable even if they are located in urban areas. The automation of the classification process allows for a faster evaluation of the presence of amphibian species in the water bodies. Three machine-learning methods (artificial neural networks, decision trees and the k-nearest neighbours algorithm) have been used to classify water bodies in Chorzów – one of 19 cities in the Upper Silesia Agglomeration. In this case, classification is a supervised data mining method consisting of several stages such as building the model, the testing phase and the prediction. Seven natural and anthropogenic features of water bodies (e.g. the type of water body, aquatic plants, the purpose of the water body (destination), position of the water body in relation to any possible buildings, condition of the water body, the degree of littering, the shore type and fishing activities) have been taken into account in the classification. The data set used in this study involved information about 71 different water bodies and 9 amphibian species living in them. The results showed that the best average classification accuracy was obtained with the multilayer perceptron neural network.

Machine-learning methods in the classification of water bodies

Department of Hydrogeology and Engineering Geology, Faculty of Earth Sciences, University of Silesia, Będzińska Str. 60, Sosnowiec, Poland

Department of Industrial Informatics, Silesian University of Technology, 40-019 Katowice, Krasińskiego Str. 8, Poland

Environmental & Socio-economic Studies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Machine-learning methods in the classification of water bodies"}

Amphibian species have been considered as useful ecological indicators. They are used as indicators of environmental contamination, ecosystem health and...

Machine-learning methods in the classification of water bodies

Published Online: Jun 24, 2016

Page range: 34 - 42

Received: Mar 09, 2016

Accepted: May 27, 2016

DOI: https://doi.org/10.1515/environ-2016-0010

Keywords
water body, k-nearest neighbour algorithm, artificial neural network, decision tree, amphibians

© 2016 Marek Sołtysiak et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Machine-learning methods in the classification of water bodies

Published Online: Jun 24, 2016

Page range: 34 - 42

Received: Mar 09, 2016

Accepted: May 27, 2016

DOI: https://doi.org/10.1515/environ-2016-0010

Keywordswater body, k-nearest neighbour algorithm, artificial neural network, decision tree, amphibians

© 2016 Marek Sołtysiak et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
water body, k-nearest neighbour algorithm, artificial neural network, decision tree, amphibians