In water quality monitoring, the complexity and abstraction of water environment data make it difficult for staff to monitor the data efficiently and intuitively. Visualization of water quality data is an important part of the monitoring and analysis of water quality. Because water quality data have geographic features, their visualization can be realized using maps, which not only provide intuitive visualization, but also reflect the relationship between water quality and geographical position. For this study, the heat map provided by Google Maps was used for water quality data visualization. However, as the amount of data increases, the computational efficiency of traditional development models cannot meet the computing task needs quickly. Effective storage, extraction and analysis of large water data sets becomes a problem that needs urgent solution. Hadoop is an open source software framework running on computer clusters that can store and process large data sets efficiently, and it was used in this study to store and process water quality data. Through reasonable analysis and experiment, an efficient and convenient information platform can be provided for water quality monitoring.
With the accelerated pace of urbanization, the construction of sponge cities is particularly essential for maintaining the ecological environment. Given the traditional multiparameter detection of heavily polluted water quality in cities, there are many problems, such as long completion time, low accuracy, high cost. A multi-parameter detection method of water quality based on the random forest classification algorithm is proposed. Through the analysis of urban heavily contaminated water quality, the probability synthesis matrix of heavily polluted water quality index is obtained by combining the water quality fluctuation probability of a single parameter and the dynamic correlation coefficient of multi-parameters. The membership function of the fluctuation of the treated water quality index is obtained, and the weak correlation among the water quality indexes is removed by the obtained membership function, and the strong relationship characteristic quantity of the water quality index is obtained, which is input into the random forest classifier to complete the multi-parameter detection of river water quality.
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