Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees
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Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Meteorology 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.
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yazılım Mühendisliği, Meteoroloji ve Atmosferik Bilimler
Kaynak
Journal of advanced research in natural and applied sciences (Online)
WoS Q Değeri
Scopus Q Değeri
Cilt
8
Sayı
2
