{"title":"RBF算法及其在多河段水质模拟中的应用","authors":"Chang-jun Zhu, Xiujuan Zhao","doi":"10.1109/CINC.2009.139","DOIUrl":null,"url":null,"abstract":"Based on the theory and method of neural network, and analysis of multi-reach water quality model, a model of RBF water quality model was presented, the model was trained and examined with the data of water quality of Fuyang river in Handan city. The results indicate that the model is more accurate than traditional model and is feasible for water quality simulation.","PeriodicalId":173506,"journal":{"name":"2009 International Conference on Computational Intelligence and Natural Computing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"RBF Algorithm and its Application in Multi-Reach Water Quality Simulation\",\"authors\":\"Chang-jun Zhu, Xiujuan Zhao\",\"doi\":\"10.1109/CINC.2009.139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the theory and method of neural network, and analysis of multi-reach water quality model, a model of RBF water quality model was presented, the model was trained and examined with the data of water quality of Fuyang river in Handan city. The results indicate that the model is more accurate than traditional model and is feasible for water quality simulation.\",\"PeriodicalId\":173506,\"journal\":{\"name\":\"2009 International Conference on Computational Intelligence and Natural Computing\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Computational Intelligence and Natural Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CINC.2009.139\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Computational Intelligence and Natural Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINC.2009.139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
RBF Algorithm and its Application in Multi-Reach Water Quality Simulation
Based on the theory and method of neural network, and analysis of multi-reach water quality model, a model of RBF water quality model was presented, the model was trained and examined with the data of water quality of Fuyang river in Handan city. The results indicate that the model is more accurate than traditional model and is feasible for water quality simulation.
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