{"title":"Machine Learning in Python for Weather Forecast based on Freely Available Weather Data","authors":"E. B. Abrahamsen, O. M. Brastein, B. Lie","doi":"10.3384/ECP18153169","DOIUrl":null,"url":null,"abstract":"Forecasting weather conditions is important for, e.g., operation of hydro power plants and for flood management. Mechanistic models are known to be computationally demanding. Hence, it is of interest to develop models that can predict weather conditions faster than traditional meteorological models. The field of machine learning has received much interest from the scientific community. Due to its applicability in a variety of fields, it is of interest to study whether an artificial neural network can be a good candidate for prediction of weather conditions in combination with large data sets. The availability of meteorological data from multiple online sources is an advantage. In order to simplify the retrieval of data, a Python API to read meteorological data has been developed, and ANN models have been developed using TensorFlow.","PeriodicalId":350464,"journal":{"name":"Proceedings of The 59th Conference on imulation and Modelling (SIMS 59), 26-28 September 2018, Oslo Metropolitan University, Norway","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of The 59th Conference on imulation and Modelling (SIMS 59), 26-28 September 2018, Oslo Metropolitan University, Norway","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3384/ECP18153169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 28
Abstract
Forecasting weather conditions is important for, e.g., operation of hydro power plants and for flood management. Mechanistic models are known to be computationally demanding. Hence, it is of interest to develop models that can predict weather conditions faster than traditional meteorological models. The field of machine learning has received much interest from the scientific community. Due to its applicability in a variety of fields, it is of interest to study whether an artificial neural network can be a good candidate for prediction of weather conditions in combination with large data sets. The availability of meteorological data from multiple online sources is an advantage. In order to simplify the retrieval of data, a Python API to read meteorological data has been developed, and ANN models have been developed using TensorFlow.