{"title":"利用人工神经网络估算降水和流量测量网络的月径流量","authors":"Said Rachidi, J. Alami, E. E. Mazoudi","doi":"10.1109/WINCOM50532.2020.9272514","DOIUrl":null,"url":null,"abstract":"The Ourika river originated in the High Atlas generate the average water resources of 157 Mm3/year, Since there is no dam in this river, the water supplies are regulated by traditional channels to irrigate 19 855 ha, hence the importance of developing runoff-rainfall model for water estimation in this context. In the presented study Artificial Neural Network is applied to forecast the monthly runoff in outlet of basin. This study uses runoff from two stations and monthly precipitation data recorded at measurement network composed of 5 stations located in Ourika basin during 15 years from 2000 to 2015. For evaluate the performance of model in the phases training and validation the appropriate statistical methods were used.","PeriodicalId":283907,"journal":{"name":"2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimation of the monthly Runoff from precipitation and flow measurement networks using Artificial Neural Network\",\"authors\":\"Said Rachidi, J. Alami, E. E. Mazoudi\",\"doi\":\"10.1109/WINCOM50532.2020.9272514\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Ourika river originated in the High Atlas generate the average water resources of 157 Mm3/year, Since there is no dam in this river, the water supplies are regulated by traditional channels to irrigate 19 855 ha, hence the importance of developing runoff-rainfall model for water estimation in this context. In the presented study Artificial Neural Network is applied to forecast the monthly runoff in outlet of basin. This study uses runoff from two stations and monthly precipitation data recorded at measurement network composed of 5 stations located in Ourika basin during 15 years from 2000 to 2015. For evaluate the performance of model in the phases training and validation the appropriate statistical methods were used.\",\"PeriodicalId\":283907,\"journal\":{\"name\":\"2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WINCOM50532.2020.9272514\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WINCOM50532.2020.9272514","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimation of the monthly Runoff from precipitation and flow measurement networks using Artificial Neural Network
The Ourika river originated in the High Atlas generate the average water resources of 157 Mm3/year, Since there is no dam in this river, the water supplies are regulated by traditional channels to irrigate 19 855 ha, hence the importance of developing runoff-rainfall model for water estimation in this context. In the presented study Artificial Neural Network is applied to forecast the monthly runoff in outlet of basin. This study uses runoff from two stations and monthly precipitation data recorded at measurement network composed of 5 stations located in Ourika basin during 15 years from 2000 to 2015. For evaluate the performance of model in the phases training and validation the appropriate statistical methods were used.