Correlation and Prediction Analysis of Cyanobacteria with Water Environment Data

Abstract

Objectives A study was conducted to implement a predictive for cyanobacteria occurrence, a direct indicator of algae presence. Water quality, river environment, and meteorological data were collected and analyzed at six locations along the Nakdong River in Gyeongsangbuk-do and Daegu City. The primary objective was to establish correlations between various environmental factors and validate the utility of predicting algae(cyanobacteria) occurrences. Methods Cyanobacteria was designated as the dependent variable for correlation analysis. Water quality parameters associated with algal growth, including chlorophyll-a, water temperature, pH, dissolved oxygen(DO), total nitrogen(TN), and total phosphorus(TP), were utilized as key indicators. Additionally, meteorological data such as air temperature, humidity, cloud cover, precipitation, and solar radiation, as well as flow rate and flow quantity, which are indicators of water body stability, were utilized. The collected data were verified and corrected for accuracy before analyzing correlations between cyanobacteria occurrence and the independent variables. Furthermore, cyanobacteria occurrence was predicted using a random forest algorithm, with a linear regression model serving as a baseline for comparison. Results and Discussion The data presented a normal distribution. Correlation analysis indicated that previous cyanobacteria occurrences had a significant influence on current occurrences. Water temperature showed a positive correlation with cyanobacteria, while DO exhibited a negative correlation. Flow quantity and flow rate were inversely correlated with cyanobacteria cell density. In terms of meteorological data, air pressure negatively correlated with cyanobacteria occurrence, while air temperature showed a positive relationship. Substantial precipitation significantly reduced cyanobacteria concentrations; however, this effect was less pronounced during periods of low rainfall. The linear regression model, with an R2 value of 0.734, demonstrated considerable explanatory power, indicating its effectiveness in accounting for the variability of the dependent variable. Conclusion The random forest, built on the linear regression model, successfully captured the overall trend of cyanobacteria occurrence when compared with field measurements. The model consistently maintained predictive performance by effectively recognizing complex data patterns, suggesting its potential for reliable prediction of cyanobacteria occurrence trends in aquatic ecosystems.

Keywords

• algae
• cyanobacteria
• scatter plots
• linear regression analysis
• random forest