Specific features of feed-forward neural networks application in classification and regression problems in algology

Abstract

Abundance and biological diversity of phytoplankton communities, investigated in this work, are often used as a marker for the determination of environmental health and fresh water quality. Presently their routine analysis is very time consuming and expensive. A lot of articles are devoted to the development of a system for an in situ automated analysis of phytoplankton properties. However, the applied problems of biology, ecology and, in particular, algology usually are associated with some difficulties due to shortage and/or fuzziness of the experimental data. Hence, while using neural network modelling a set of specific problems can occur. In this article on the base of experimental data several such problems are presented and possible solutions are suggested. In particular, the illogical behavior of classifying neural network is revealed, while studying the biological diversity of cyanobacteria, and the original technique for results validation is presented. This problem is investigated on a set of spectroscopic data, recorded by means of confocal laser scanning microscopy. The generalization quality of the trained model is studied as the main learning parameter. Another problem of shortage dataset is examined in the frames of regression model for bioplankton abundance. This problem is solved by means of feed-forward back-propagation neural networks with two hidden layers. The modelling was carried out on a small experimental selection (only 39 observations were available), despite this, the relatively high determination coefficient was obtained for the training and test samples, while using dropout layout.