Temat: water classification - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Monthly changes in physicochemical parameters of the groundwater in Nida valley, Poland (case study)
Autorzy:: Phan, Cong Ngoc
Strużyński, Andrzej
Kowalik, Tomasz
Powiązania:: https://bibliotekanauki.pl/articles/2203556.pdf
Data publikacji:: 2023
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: groundwater
Nida valley
physicochemical water property
statistical method
water quality classification
Opis:: The groundwater of the Nida valley was investigated to assess the quality of water source and monthly variations of the physicochemical parameters. A total of 70 water samples were collected from 7 sampling sites during a 10 months period from June 2021 to March 2022. Sampling frequency was once per month. The parameters such as temperature (T), electrical conductivity (EC), dissolved oxygen (DO), pH, total dissolved solids (TDS) were measured in-situ by using handheld device. Meanwhile, total nitrogen (TN), total phosphorus (TP), chloride (Cl – ), sulphate (SO42– ), manganese (Mn), iron (Fe), zinc (Zn), cadmium (Cd), lead (Pb), copper (Cu), chemical oxygen demand (COD) were analysed in the laboratory. According to the classification of Ministry of Marine Economy and Inland Navigation in Poland (2019), some investigated parameters are classified as unsatisfactory quality waters (class 4) and poor-quality waters (class 5) for a few specific months. Such as, TP concentrations obtained in June and January are classified as class 4, SO42– concentrations corresponded to classes 4 and 5 in June, July and August, and Mn concentrations (except in January) are settled in class 5. The high values of Fe in November are arranged in class 5 and in June, July to September and March are classified in class 4. Statistical methods were used as: Shapiro-Wilk test (α = 0.05), ANOVA test and post-hoc Tukey test (α = 0.05), Kruskal-Wallis test and Wilcoxon (Mann-Whitney) rank sum test (α = 0.05) estimated the significant differences in sampling months. Pearson correlation analysis (α = 0.01 and 0.05), principal component analysis (PCA) and cluster analysis showed correlation between the parameters and sampling months.
Źródło:: Journal of Water and Land Development; 2023, 56; 220--234
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Artificial intelligence for supervised classification purposes: Case of the surface water quality in the Moulouya River, Morocco
Autorzy:: Manssouri, Imad
Talhaoui, Abdelghani
El Hmaidi, Abdellah
Boudad, Brahim
Boudebbouz, Bouchra
Sahbi, Hassane
Powiązania:: https://bibliotekanauki.pl/articles/1841945.pdf
Data publikacji:: 2021
Wydawca:: Instytut Technologiczno-Przyrodniczy
Tematy:: artificial intelligence
environment
supervised classification
the Moulouya River
water quality
Opis:: From a management perspective, water quality is determined by the desired end use. Water intended for leisure, drinking water, and the habitat of aquatic organisms requires higher levels of purity. In contrast, the quality standards of water used for hydraulic energy production are much less important. The main objective of this work is focused on the development of an evaluation system dealing with supervised classification of the physicochemical quality of the water surface in the Moulouya River through the use of artificial intelligence. A graphical interface under Matlab 2015 is presented. The latter makes it possible to create a classification model based on artificial neural networks of the multilayer perceptron type (ANN-MLP). Several configurations were tested during this study. The configuration [9 8 3] retained gives a coefficient of determination close to the unit with a minimum error value during the test phase. This study highlights the capacity of the classification model based on artificial neural networks of the multilayer perceptron type (ANN-MLP) proposed for the supervised classification of the different water quality classes, determined by the calculation of the system for assessing the quality of surface water (SEQ-water) at the level of the Moulouya River catchment area, with an overall classification rate equal to 98.5% and a classification rate during the test phase equal to 100%.
Źródło:: Journal of Water and Land Development; 2021, 50; 240-247
1429-7426
2083-4535
Pojawia się w:: Journal of Water and Land Development
Dostawca treści:: Biblioteka Nauki

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