4.6 Article

Possibilities of River Water Temperature Reconstruction Using Statistical Models in the Context of Long-Term Thermal Regime Changes Assessment

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APPLIED SCIENCES-BASEL
卷 12, 期 15, 页码 -

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MDPI
DOI: 10.3390/app12157503

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artificial neural network; multiple linear regression; random forest regression; multilayer perceptron network; Mann-Kendall test; Sen test

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Water temperature is a crucial factor in determining the various biotic and abiotic processes in river ecosystems. This study addresses the issue of insufficient measurement data by reconstructing water temperature for selected rivers in Poland. The results indicate that the multilayer perceptron network model, using water temperatures in lakes and meteorological data, provides the best reconstruction results. The average errors and root mean square errors for the Polish rivers are relatively small, indicating accurate reconstructions. Additionally, the study finds a significant increase in average yearly water temperature for Polish rivers from 1987 to 2020, with the largest increases occurring in June, August, September, November, and December.
Water temperature in rivers is the key property determining the biotic and abiotic processes occurring in these ecosystems. In many regions of the world, the significant lack of measurement data is a serious problem. This paper presents reconstruction of water temperature for selected Polish rivers with monitoring discontinued in the period 2015-2020. Information regarding air temperature and water temperature in lakes provided the basis for the comparison of three models: multiple linear regression, random forest regression, and multilayer perceptron network. The results show that the best reconstruction results were obtained with a multilayer perceptron network model based on water temperatures in the lake and air temperatures from three meteorological stations. The average values of mean error, root mean square error and standard error were for the rivers in Poland: 1.52 degrees C, 5.03%, and 0.47 degrees C. The course of mean yearly water temperature in the years 1987-2020 showed a statistically significant increase from 0.18 to 0.49 degrees C per decade. The results show that the largest increases occurred in June, August, September, November, and December.

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