Predicting the Influence of Ammonium Toxicity Levels in Water Using Fuzzy Logic and ANN Models

Yuliia Trach (), Roman Trach, Pavlo Kuznietsov, Alla Pryshchepa, Olha Biedunkova, Agnieszka Kiersnowska and Ihor Statnyk
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Yuliia Trach: Institute Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine
Roman Trach: Faculty of Civil and Environmental Engineering, Institute of Civil Engineering, Warsaw University of Life Sciences, 02-787 Warsaw, Poland
Pavlo Kuznietsov: Institute Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine
Alla Pryshchepa: Institute Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine
Olha Biedunkova: Institute Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine
Agnieszka Kiersnowska: Faculty of Civil and Environmental Engineering, Institute of Civil Engineering, Warsaw University of Life Sciences, 02-787 Warsaw, Poland
Ihor Statnyk: Institute Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine

Sustainability, 2024, vol. 16, issue 14, 1-21

Abstract: The study aimed to address the complex and critical issue of surface water quality monitoring by proposing a diversified approach that incorporates a range of chemical indicators. (1) Background: the purpose of the study was to address the problem of surface water quality monitoring in relation to the toxic effects of ammonium on aquatic ecosystems by developing predictive models using fuzzy logic and artificial neural networks. (2) Water samples from the Styr River, influenced by the Rivne Nuclear Power Plant, were analyzed using certified standard methods and measured parameters, while fuzzy logic and artificial neural network models, including Mamdani’s algorithm and various configurations of activation functions and optimization algorithms, were employed to assess water quality and predict ammonium toxicity. (3) A fuzzy logic system was developed to classify water quality based on ammonia content and other parameters, and six Artificial Neural Network (ANN) models were tested, with the ANN#2 model (using ReLU activation and ADAM optimizer) showing the best performance. (4) This study emphasizes the critical need for precise monitoring and modeling of total ammonium in surface water, considering its variable toxicity and interactions with environmental factors, to effectively protect aquatic ecosystems, namely ichthyofauna.

Keywords: non-ionized ammonia; surface water; machine learning; fish (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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