Data Analytics and Prediction for Water Quality Monitoring

Activity: SupervisionMaster Dissertation Supervision


With population growth and urban industrialization, water pollution has become more and more serious.
Therefore, water quality has become a growing concern in various countries. At the same time, climate
change, eutrophication and pollution have become major factors, especially cyanobacteria outbreaks. There
is always a period of time every year when cyanobacterial outbreaks occur on a large scale, which makes
the monitoring and prediction of water quality particularly important.Therefore, this paper addresses the
factors affecting cyanobacterial outbreaks and growth prediction trends, and is divided into three main modules:
data pre-processing, data analysis, and drawing conclusions.
(1) In the data pre-processing part, the k-means clustering algorithm is used to remove abnormal data.
(2) In the data analysis section, BP neural network model was used to make predictions, and the prediction
research steps and prediction feasibility were proposed. Apriori association rules are also used to identify
the main causes of cyanobacterial outbreaks based on the support and confidence levels.
(3) The last section summarizes the first two modules and proposes methods that can control the cyanobacterial
Period27 Apr 2022