基于神经网络的水质等级预测研究与实现

Yang Gong, P. Zhang
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引用次数: 0

摘要

现代社会对水质的要求越来越高,为了快速判断水质等级。提出了一种基于神经网络的水质等级预测模型。首先,采用爬虫技术获取水质监测历史数据;然后,对收集到的数据进行简单分析;然后,利用数据训练构建的神经网络结构对权重和偏置参数进行连续调整;最后,利用训练好的模型对水质等级进行预测。经过大量的训练和测试,该模型在训练集中的准确率可以达到97.30%;测试集的准确率可以达到96.66%,在训练集和测试集上都取得了很好的效果。该方法具有较好的泛化能力,可用于预测水质水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research and Implementation of Water Quality Grade Prediction based on Neural Network
The demand for water quality in modern society is higher and higher, in order to quickly judge the water quality grade. This paper presents a water quality grade prediction model based on neural network. Firstly, the crawler technology is used to obtain the historical data of water quality monitoring; Then, the collected data are simply analyzed; Then, the neural network structure constructed by data training is used to continuously adjust the weight and bias parameters; Finally, the trained model is used to predict the water quality grade. After a lot of training and testing, the accuracy of the model in the training set can reach 97.30%; The accuracy rate in the test set can reach 96.66%, and good results have been achieved in both the training set and the test set. It has good generalization ability and can help predict the water quality level.
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