With the increasing pollution of the ecological water environment, the treatment of the ecological water environment has become the focus of everyone's attention. At present, there are many research results on water environment governance, but the effect is not ideal. In order to effectively control the ecological water environment and promote sustainable economic growth, this research combines artificial intelligence algorithms and applies them to the governance process to explore its application effects and its impact on economic growth. First, the environmental sensor of the corresponding module is designed according to the water environment factor, and the data of dissolved oxygen content, water temperature, turbidity, temperature and humidity, and smoke concentration in the water environment are collected. Then the dynamic time-varying exponential smoothing prediction method is used to predict water quality, and a water quality prediction model is established. Then use support vector machine (SVM) to train the collected data samples, use the decision tree-based SVM classification method to classify the data samples, establish a water quality evaluation model, and use particle swarm optimization algorithm to optimize the evaluation model. Put the sensors and predictive evaluation models established in this research design into the governance of a certain river reach, and collect relevant data from 7 : 00 to 18 : 00 on October 11, 2019. And predict and evaluate its water quality. The experimental results show that the average absolute error of predicting dissolved oxygen content is 0.97%, and the average absolute error of predicting phosphorus content is 2.27%. This shows that the application of artificial intelligence algorithms in the process of ecological water environmental governance can effectively help collect effective information and make more accurate predictions and evaluations of water quality, thereby improving governance efficiency and promoting sustainable economic growth.
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