Eksik sıcaklık verilerinin Yapay Sinir Ağları (YSA) ile tahmin edilmesi

Abstract

Ensuring more reliable and quality meteorological and climatological studies by providing data continuity and widening the data range. For this reason, missing values in meteorological data such as temperature, precipitation, evaporation must be completed. In this study, an artificial neural network (ANN) model was used to complete missing temperature data in the Horasan meteorology station. To establish the ANN model, monthly average temperature values of neighboring stations having similar climatic characteristics and altitude with Horasan were used as input. The monthly average temperature values of the Horasan station were used as output. Approximately 70% of the data was used for training, about 15% for testing, and about 15% for verification in the ANN model. Various statistical parameters were compared to determine the best network architecture and best model. As a result, the model's high determination coefficient (R2 = 0.99) and low mean absolute error (MAE = 0.61) showed that the ANN model can be used effectively in estimating missing temperature data.

An Assessment on Bank Openness, Open Banking and Open Data in Turkey

“…Yapay sinir ağları ile ilgili olarak WOS ve DergiPark dizinlerinde güncel yer alan araştırma makalelerine örnek olarak Bansal vd., 2023;Hou vd., 2023;İlhan ve Öz, 2020;Katipoğlu ve Acar, 2021;Kılıç, 2019;Schmitt vd., 2023;Yabar ve Aydın, 2020;Yüksek, vd., 2018 çalışmaları verilebilir. Burada incelemeye alınan çalışmalar yüksek teknik içerikli olup genel olarak belirli bir problemin çözümü için uygulamanın nasıl yapılması gerektiğine odaklanmaktadır.…”