Abstract
This paper introduces a new approach for the detection of harmful algae in water monitoring with the application of a fuzzy based machine learning methodic by fuzzily describing the data after feature extraction from the water spectra. The main challenge of this task was to describe the whole transmission and fluorescence spectra to extract the features and build a multidimensional fuzzy pattern classifier that integrates the measurement uncertainties associated with the measurement. The utility and application of such a methodic is to detect with light sensors harmful algae in water monitoring before, for example, algae can pollute the water or toxic algal blooms can cause harm. Using different solutions of a reference substance for alga within chlorophyll a data basis for the learning phase of a classification was generated with the feature extraction. The result is a highly flexible classifier which provides information about unknown spectral data and indicates a possible need for action.
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Penzel, S., Rudolph, M., Borsdorf, H., Kanoun, O. (2023). Application of a Fuzzy Based Machine Learning Approach to the Detection of Harmful Algae in Water Monitoring. In: Kahraman, C., Sari, I.U., Oztaysi, B., Cebi, S., Cevik Onar, S., Tolga, A.Ç. (eds) Intelligent and Fuzzy Systems. INFUS 2023. Lecture Notes in Networks and Systems, vol 759. Springer, Cham. https://doi.org/10.1007/978-3-031-39777-6_22
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