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dc.contributor.authorChoosri, Pranomkornen_US
dc.contributor.authorประนมกร ชูศรีen_US
dc.contributor.authorNabnean, Sarawuten_US
dc.contributor.authorสราวุฒิ แนบเนียรen_US
dc.contributor.authorKanchanachat, Worapanen_US
dc.contributor.authorวรพันธุ์ การชนะชาติen_US
dc.contributor.authorTeansri, Sanittaen_US
dc.contributor.authorสนิธตา เทียนสีen_US
dc.date.accessioned2022-04-23T09:13:31Z
dc.date.available2022-04-23T09:13:31Z
dc.date.issued2022-04-23
dc.identifier.issn2651-1096
dc.identifier.urihttp://repository.rmutp.ac.th/handle/123456789/3797
dc.descriptionวารสารวิชาการและวิจัย มทร.พระนคร, ปีที่ 15, ฉบับที่ 2 (ก.ค.-ธ.ค. 2564), หน้า 75-87en_US
dc.description.abstractIn this work, solar erythemal ultraviolet radiation (EUV) which affects human skin and skin cancer was investigated at Songkhla province (7.2°N, 100.6°E). EUV was converted to UV index, then the data was used for model development and forecasting of hourly UV index using Artificial Neural Network (ANN). The ANN model has one input layer, two hidden layers and one output layer. This input layer consists of extraterrestrial erythemal ultraviolet radiation, solar zenith angle, aerosol optical depth and cloud index which affect ultraviolet radiation, and the output layer is hourly UV index. The results show that hourly UV index obtained from ANN and that from the measurement are in reasonable agreement, with root mean square difference of 12.8% and mean bias difference of -2.4%. For forecasting of the hourly UV index, the data for 7 days earlier was used for forecasting the next UV index for one day or nine-hour (08:00 am.-16:00 pm.). Multi-layer perceptron and back propagation algorithm were used in the model forecasting. The results show that the UV index from the model forecasting is reasonable agrees with UV index from measurement with root mean square difference of 17.0% and mean bias difference of 0.3%.en_US
dc.description.sponsorshipRajamangala University of Technology Phra Nakhonen_US
dc.language.isothen_US
dc.subjectForecastingen_US
dc.subjectพยากรณ์en_US
dc.subjectModels and modelmakingen_US
dc.subjectแบบจำลองen_US
dc.subjectUltraviolet radiationen_US
dc.subjectรังสีเหนือม่วงen_US
dc.titleDevelopment of a model and forecasting of hourly uv index using artificial neural network (ANN) at Songkhlaen_US
dc.title.alternativeการพัฒนาแบบจำลองและการพยากรณ์ดัชนีรังสีอัลตราไวโอเลตรายชั่วโมง โดยใช้โครงข่ายประสาทเทียมในจังหวัดสงขลาen_US
dc.typeJournal Articlesen_US
dc.contributor.emailauthorpranomkorn.aum@gmail.comen_US
dc.contributor.emailauthorarit@rmutp.ac.then_US


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