Modern global trends in water quality forecasting
Abstract
Purpose. Generalization of modern scientific approaches to water quality forecasting, identification of main development trends, assessment of advantages and disadvantages of the most common groups of methods, as well as determination of possibilities of their effective application in the conditions of modern Ukraine.
Methods. An adapted systematic review approach according to the PRISMA methodology (Preferred Reporting Items for Systematic Reviews and Meta-Analysis), visual and comparative analyses were applied.
Results. Based on the analysis of modern scientific publications on water quality forecasting for 2020-2025, forecasting approaches were classified by conditional groups of methods in order to identify trends in scientific research. Using a spreadsheet editor, the frequency of use of keywords in the titles of publications was calculated, and based on the calculation results, a chronological graph was constructed that reflects the dynamics of the annual frequency of mention of keywords that correspond to different groups of forecasting methods. From the analysis of this graph, the most common groups of methods were identified, with a clear positive trend in their use in scientific publications. The advantages, limitations and prospects for the implementation of these most common groups of methods in domestic practice were also assessed, in particular taking into account the current conditions of full-scale war. The analysis allowed us to identify the most relevant and realistically applied approaches, and also pointed out potential difficulties. The use of XAI methods to overcome the “black box” problem in water quality forecasting is being actively studied in the world. In Ukraine, XAI was used mainly in the agricultural sector, while the author did not find any scientific studies using XAI to forecast water quality.
Conclusions. The most common group of water quality forecasting methods are methods related to the use of artificial intelligence and machine learning. In Ukraine, there are studies using artificial intelligence and machine learning, mainly in the form of hybrid methods, most often combining remote sensing and machine learning. These approaches demonstrate high efficiency in conditions of full-scale war, when physical access to a water body is limited or impossible
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