不同预测模型在水污染问题中的应用综述
西南林业大学, 机械与交通学院, 云南昆明 650224
| 摘 要： | 本文从目前的水污染问题出发，阐述了当前水污染问题的严重性以及危害性，并说明了构建与污染问题匹配的水质预测模型的重要性。而后分析了在构建预测模型时数据误差的主要来源。由于不同流域的水污染问题存在不同程度的差异，所以本文提出了三种目前研究较多的预测模型，分别是BP神经网络预测模型、LSTM（长短记忆人工神经网络）预测模型、随机森林算法预测模型，并分别阐述了每一种模型的优点以及发展现状，并相互进行对比，且对每一种模型所适用的水域范围进行了阐明。表明水质预测模型在监测水质中的重要意义，但不同的水污染流域相匹配的水质预测模型是不一样的，多种算法相互结合所得出的预测模型能够有更好的预测效果，最后对水质预测模型在水污染问题的应用中提出了展望。 |
| 关 键 词： | 水污染; 神经网络; 水质预测模型; 算法 |
| DOI： | 10.57237/j.res.2024.03.001 |
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收稿日期: 2024-06-12
接受日期: 2024-07-10
在线出版日期: 2024-07-18
A Review of Different Predictive Models for Water Pollution Problems
School of Machinery and Transportation, Southwest Forestry University, Kunming 650224, China
| Abstract: | Starting from the current water pollution problem, this paper describes the seriousness of the current water pollution problem as well as its harm, and explains the importance of constructing a water quality prediction model that matches the pollution problem. And then analyzed the main sources of data errors in the construction of prediction models. Since the water pollution problems in different basins have different degrees of differences, this paper proposes three prediction models that have been researched more than others, namely, BP neural network prediction model, LSTM (Long Short Memory Artificial Neural Network) prediction model, Random Forest Algorithm prediction model, and elaborates on the advantages and the current development status of each model and compares them with each other and explains the scope of water quality prediction model applicable to each model. The scope of each model is clarified. It shows that water quality prediction model has an important role in monitoring water quality, different water pollution basins matching water quality prediction model is not the same, a combination of multiple algorithms with each other to derive the prediction model may have a better prediction effect, and finally puts forward the prospect of water quality prediction modeling in the application of water pollution problems. |
| Keywords: | Water Pollution; Neural Networks; Water Quality Prediction Models; Algorithms |
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