Abstract
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.
Highlights
With the rapid development of industry, a large amount of industrial sewage and domestic wastewater has caused serious pollution to the water environment, and the originally scarce water resources are facing severe tests [1]. e effects of some current water environment treatment projects and sewage treatment facilities are not satisfactory, which are unfavorable to the sustainable development of the environment and economy and bring serious hidden dangers to the lives and health of residents [2]. erefore, the use of artificial intelligence algorithms to monitor ecological water environment-related data and propose scientific processing methods is of great significance for improving the efficiency of water environment governance
Sensor Data Acquisition Results. e sensor designed in this study is used to collect the data of dissolved oxygen, water temperature, turbidity, temperature and humidity, and smoke concentration in a river section. e collection period is from 7 : 00 to 18 : 00 on October 11, 2019, and the data are recorded every 1 h
With the change of the ambient temperature, the water temperature will change . e ambient temperature reaches the highest value of 17.2°C at 13 : 00, while the water temperature reaches the maximum of 15.7°C at 18 : 00. e content of dissolved oxygen showed a U-shaped trend, and the content was the lowest at noon
Summary
Erefore, the use of artificial intelligence algorithms to monitor ecological water environment-related data and propose scientific processing methods is of great significance for improving the efficiency of water environment governance. Chen et al proposed a feasible method of using SO2 (sulfite) to catalyze the oxidation of wastewater to improve the removal rate of pollutants in the water environment [5]. Their research analyzed the impact of plants on water pollution, they did not propose direct treatment methods. In order to improve the efficiency of ecological water environment governance, improve the status quo of ecological water environment, and promote green growth of the economy, this paper conducts in-depth research. (2) e water quality prediction model is established by using the dynamic timevarying index smoothing prediction method, which can effectively predict the water quality and put forward the corresponding treatment scheme as soon as possible. In order to improve the efficiency of ecological water environment governance, improve the status quo of ecological water environment, and promote green growth of the economy, this paper conducts in-depth research. e innovations are as follows: (1) According to the selected water environment factors, a series of sensors with perfect functions are designed to collect the timely data of dissolved oxygen content, water temperature, turbidity, temperature and humidity, and smoke concentration in the water environment. (2) e water quality prediction model is established by using the dynamic timevarying index smoothing prediction method, which can effectively predict the water quality and put forward the corresponding treatment scheme as soon as possible. (3) e support vector machine (SVM) and SVM classification method based on decision tree are used to train and classify the data samples. e water quality evaluation model is established after the optimization of particle swarm optimization algorithm, which can accurately classify the water quality
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