AIR TEMPERATURE FORECAST OF KARS CITY USING DEEP LEARNING TECHNIQUE

Year 2022, , 1174 - 1181, 30.12.2022

Muhammet Ali Karabulut , Emre Topçu

https://doi.org/10.21923/jesd.1067700

Abstract

Accurate estimation of air temperature plays an important role in water resource management, land-atmosphere interaction, and agriculture. However, it is difficult to accurately predict air temperature due to its non-linear and chaotic nature. Deep learning techniques have been proposed to predict air temperature in recent years. This study presents a comprehensive review of long short-term memory (LSTM), one of the artificial neural networks (ANN)-based approaches used to predict air temperature. Data including weather data, average wind speed, precipitation, snowfall, snow depth, average temperature, maximum temperature and minimum temperature have been input to this algorithm. As the output, it is determined as the average temperature for the next day. The focal point is the meteorological measurements of the Central district of Kars province in the period 2010-2021. The review shows that neural network models can be used as promising tools to predict air temperature. Although ANN-based approaches are widely used for estimating air temperature due to their fast computational speed and ability to deal with complex problems, there is still no consensus on the best available method. The monthly and daily calculated model, its high estimation accuracy, showed that this model can be successfully applied in temperature estimation studies.

Keywords

Forecasting, Data Analysis, Artificial Neural Networks, LSTM

DERİN ÖĞRENME TEKNİĞİ KULLANILARAK KARS İLİNİN HAVA SICAKLIK TAHMİNİ

Abstract

Hava sıcaklığının doğru tahmini, su kaynakları yönetiminde, kara-atmosfer etkileşiminde ve tarımda önemli bir rol oynar. Ancak, doğrusal olmayan ve kaotik doğası nedeniyle hava sıcaklığını doğru bir şekilde tahmin etmek zordur. Son yıllarda hava sıcaklığını tahmin etmek için derin öğrenme teknikleri önerilmiştir. Bu çalışma, hava sıcaklığını tahmin etmek için kullanılan yapay sinir ağı (YSA) tabanlı yaklaşımlarından uzun kısa süreli bellek (LSTM) kapsamlı bir incelemesini sunmaktadır. Hava durumu verileri, ortalama rüzgâr hızı, yağış, kar yağışı, kar derinliği, ortalama sıcaklık, maksimum sıcaklık ve minimum sıcaklığı içeren veriler bu algoritmaya girdi olmuşturlar. Çıktı olarak ise, bir sonraki gün için ortalama sıcaklık olarak belirlenmiştir. Odak noktası Kars ilinin Merkez ilçesinin 2010-2021 dönemindeki meteorolojik ölçümlerdir. İnceleme, sinir ağı modellerinin hava sıcaklığını tahmin etmek için umut verici araçlar olarak kullanılabileceğini göstermektedir. YSA tabanlı yaklaşımlar, hızlı işlem kabiliyeti ve karmaşık problemlerle başa çıkma yetenekleri nedeniyle hava sıcaklığını tahmin etmek için yaygın olarak kullanılmasına rağmen, mevcut en iyi yöntem üzerinde henüz bir fikir birliği yoktur. Aylık ve günlük olarak hesaplanan modelin tahmin doğruluğunun yüksek olması, sıcaklık tahmini çalışmalarında bu modelin başarılı bir şekilde uygulanabileceğini göstermiştir.

Keywords

Sıcaklık Tahmini, Veri Analizi, LSTM, Yapay Sinir Ağları

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