WATER RESOURCE QUALITY IN THE SYSTEM OF SUSTAINABLE DEVELOPMENT INDICATORS: MODELING AND FORECASTING METHODS

H. B. Humeniuk, B. B. Sokil, R. M. Dukh

Abstract


Ensuring high water quality is a crucial aspect of sustainable development and effective environmental management. Population growth, rapid urbanization, and climate change significantly increase pressure on water resources, particularly surface waters, making their monitoring and management a global challenge. This study examines modern approaches to assessing and managing water quality, with a particular focus on integrating sensor technologies and machine learning methods.
Traditional water monitoring methods often demonstrate limited effectiveness due to their labor-intensive nature, high operational costs, and lack of real-time data. To address these limitations, the use of real-time sensors, automated data collection systems, and advanced machine learning algorithms is proposed. Specifically, the application of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), decision trees, k-nearest neighbors algorithms, and deep learning techniques-including long short-term memory (LSTM), bidirectional LSTM, and gated recurrent units (GRU)-is considered.
Hybrid models that combine artificial intelligence methods with nature-inspired optimization algorithms show enhanced predictive accuracy and efficiency in water quality management. Special attention is given to modeling the dynamics of surface water systems and developing integrated intelligent decision-support systems. These systems allow for assessing the impact of climate change, anthropogenic factors, and extreme weather events on water quality, while also optimizing water treatment processes, planning, and crisis response strategies.
Achieving Sustainable Development Goal 6 (SDG 6)-ensuring the availability and sustainable management of water and sanitation for all-is a critical element of global water security. The development of adaptive models and artificial intelligence-based systems significantly contributes to improving the management of surface waters and preserving water resources.

Keywords


water quality; artificial intelligence; machine learning; modeling; hybrid models; intelligent decision support system; Sustainable Development Goals; water resources management

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DOI: https://doi.org/10.25128/2078-2357.25.4.11

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