{"title":"末端深度计算的人工神经网络模型","authors":"A. Mohammed","doi":"10.4172/2165-784X.1000316","DOIUrl":null,"url":null,"abstract":"In this paper a feed-forward back-propagation type of neural network as well as the multi nonlinear regression model using statistical programming were used to determine the critical depth and discharge passing over the enddepth model, free overfall. This was achieved by training and validating (215) experimental data. The results of the trained verified and tested for neural network model are compared to the experimental measurements. There were well agreements with the measured values.","PeriodicalId":52256,"journal":{"name":"Tumu yu Huanjing Gongcheng Xuebao/Journal of Civil and Environmental Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Artificial Neural Network (ANN) Model for End Depth Computations\",\"authors\":\"A. Mohammed\",\"doi\":\"10.4172/2165-784X.1000316\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper a feed-forward back-propagation type of neural network as well as the multi nonlinear regression model using statistical programming were used to determine the critical depth and discharge passing over the enddepth model, free overfall. This was achieved by training and validating (215) experimental data. The results of the trained verified and tested for neural network model are compared to the experimental measurements. There were well agreements with the measured values.\",\"PeriodicalId\":52256,\"journal\":{\"name\":\"Tumu yu Huanjing Gongcheng Xuebao/Journal of Civil and Environmental Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tumu yu Huanjing Gongcheng Xuebao/Journal of Civil and Environmental Engineering\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.4172/2165-784X.1000316\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tumu yu Huanjing Gongcheng Xuebao/Journal of Civil and Environmental Engineering","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.4172/2165-784X.1000316","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
Artificial Neural Network (ANN) Model for End Depth Computations
In this paper a feed-forward back-propagation type of neural network as well as the multi nonlinear regression model using statistical programming were used to determine the critical depth and discharge passing over the enddepth model, free overfall. This was achieved by training and validating (215) experimental data. The results of the trained verified and tested for neural network model are compared to the experimental measurements. There were well agreements with the measured values.