Water pollution monitoring method after flood disaster based on big data technology

Abstract

In order to solve the problem of poor pollution load reduction performance of traditional monitoring models, with the support of big data analysis technology, a water pollution monitoring model after flood disasters was constructed. Using big data analysis methods to obtain water pollution data after flood disasters, calculate the “best point,” “worst point,” and “expected point” data corresponding to each pollution index based on the collected data, and obtain the corresponding data of each pollution index material element analysis reference value, calculate the comprehensive correlation function value of the monitoring points, realize the optimization of monitoring points, and construct the monitoring model according to the basic equation of the model and the generalized result to realize the monitoring of water pollution information and water body conditions. The experimental results show that the load reduction rate of the designed model for different types of pollution is higher than 0.10%, which is significantly higher than that of the traditional model, indicating that the pollution load reduction performance of the designed model is better than that of the traditional model, and it is more suitable for water pollution monitoring after flood disaster.