Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees

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dc.contributor.authorAltınbilek, Hakkı Fırat
dc.contributor.authorAksu, Sefa
dc.contributor.authorKızıl, Ünal
dc.contributor.authorNar, Hakan
dc.date.accessioned2025-01-27T19:28:51Z
dc.date.available2025-01-27T19:28:51Z
dc.date.issued2022
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractMeteorology stations sold in the market have various difficulties in terms of their use, also these systems are costly to obtain. With state of the art sensor technologies, the development of mini weather stations has become easier. In this study it was aimed to develop a prototype low-cost weather station using temperature, relative humidity, ultraviolet (UV), light dependent resistor (LDR), rain and soil moisture sensors to collect major meteorological data. The collected data transmitted to the remote station for logging via a GSM module and the information was sent to the database in the internet environment. In addition, the data from the sensors are organized by correlation. The classification was made according to the data obtained from the rain sensor and the relationship between the other 5 sensors used in the device to the rain classification was examined. Sensor data were scaled between 0-1 with min-max normalization before being subjected to deep learning and machine learning training. In the Decision Tree (DT) a model score of 0.96 was obtained by choosing the maximum depth of 2. The artificial neural network (ANN) deep learning yielded a classification score of 0.92 using 4 hidden layers and 100 epochs in the artificial neural network model.
dc.identifier.doi10.28979/jarnas.984312
dc.identifier.endpage321
dc.identifier.issn2757-5195
dc.identifier.issue2
dc.identifier.startpage309
dc.identifier.trdizinid1120894
dc.identifier.urihttps://doi.org/10.28979/jarnas.984312
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1120894
dc.identifier.urihttps://hdl.handle.net/20.500.12428/15914
dc.identifier.volume8
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofJournal of advanced research in natural and applied sciences (Online)
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TRD_20250125
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectMeteoroloji ve Atmosferik Bilimler
dc.titleSensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees
dc.typeArticle

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