{"title":"基于神经网络的水质等级预测研究与实现","authors":"Yang Gong, P. Zhang","doi":"10.1109/INSAI54028.2021.00063","DOIUrl":null,"url":null,"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.","PeriodicalId":232335,"journal":{"name":"2021 International Conference on Networking Systems of AI (INSAI)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research and Implementation of Water Quality Grade Prediction based on Neural Network\",\"authors\":\"Yang Gong, P. Zhang\",\"doi\":\"10.1109/INSAI54028.2021.00063\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":232335,\"journal\":{\"name\":\"2021 International Conference on Networking Systems of AI (INSAI)\",\"volume\":\"93 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Networking Systems of AI (INSAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INSAI54028.2021.00063\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Networking Systems of AI (INSAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INSAI54028.2021.00063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.