Assessing the spatiotemporal variability of lake water quality using A novel multidimensional shape – Position similarity cloud model

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Cloud model theory provides a reliable method to effectively solve the problem of uncertainty associated with lake water quality assessments. To accurately match water quality parameters obtained from water samples and water quality standards, water quality parameters from water samples and water quality class levels were used as inputs to a reverse cloud generator algorithm to derive corresponding sample and level clouds. A multidimensional shape-position similarity cloud model (MSPSCM) was then developed to accurately evaluate lake water quality by considering shape and position similarities between the sample and level clouds. Using monthly water quality monitoring data from 2017 to 2019, spatiotemporal variability of water quality parameters of Nansi Lake in Shandong Province was analyzed, and the MSPSCM was used to further study the spatiotemporal variability of water pollution in Nansi Lake. Results showed that total nitrogen and total phosphorus were the main sources of pollution in Nansi Lake. Except for the severe pollution of the upper lake and its inflow waters (Class V water quality standard) in November 2017 and September 2019, Nansi Lake waters meet Class III water quality standard, and are suitable for drinking after being treated by a sewage treatment plant. Concentration of residential areas and industries around the upper lake is relatively high; large quantities of pollutants are discharged into the upper lake, resulting in considerably severe pollution of the upper lake (Class IV). Difference between water quality of Nansi Lake and that of its inflow water indicates that the purification ability of Nansi Lake should not be underestimated. In addition, compared with the existing cloud model used to evaluate lake water quality, the MSPSCM can more accurately reflect lake water quality, and provides a more flexible and effective method for lake water quality evaluation.