Research and Implementation of Water Quality Grade Prediction based on Neural Network

Yang Gong, P. Zhang
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Abstract

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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基于神经网络的水质等级预测研究与实现
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。
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