Hybrid ConvGRU Model for Prediction of Climate Variables of Touristic Cities in India

Year 2024, Volume: 6 Issue: 2, 165 - 176, 29.10.2024

Anıl Utku

https://doi.org/10.46387/bjesr.1480346

Abstract

Weather prediction is very important in terms of ensuring effectiveness and efficiency in areas such as agriculture, health, transportation, tourism, air quality and industrial production. Traditional weather forecast models are inadequate for long-term predictions. Artificial intelligence methods can produce successful predictions for the future by learning complex relationships between weather data such as temperature, humidity, wind speed and air pressure. In this study, it was aimed to predict long-term climate variables such as temperature, humidity and dew point of Agra, Jaipur, Jodhpur, New Delhi and Rishikesh, which are important tourism cities of India. For this purpose, the developed ConvGRU hybrid model was comprehensively compared with RF, SVM, CNN, LSTM and GRU models. A real-time and up-to-date dataset between 2010 and 2024 was used. Experimental results show that ConvGRU outperforms benchmarked models with R2 values above 0.9 for all cities and climate variables.

Keywords

Climate Variables, Convolutional Neural Network, Gated Recurrent Unit, Deep Learning, Machine Learning

Hindistan'daki Turistik Şehirlerin İklim Değişkenlerinin Tahminine Yönelik Hibrit ConvGRU Modeli

Abstract

Hava durumu tahmini tarım, sağlık, ulaşım, turizm, hava kalitesi ve endüstriyel üretim gibi alanlarda etkinliğin ve verimliliğin sağlanabilmesi açısından oldukça önemlidir. Geleneksel hava durumu tahmin modelleri uzun vadeli tahminlerde yetersiz kalmaktadır. Yapay zekâ yöntemleri, sıcaklık, nem, rüzgâr hızı ve hava basıncını gibi hava durumu verileri arasındaki karmaşık ilişkileri öğrenerek geleceğe dönük başarılı tahminler üretebilmektedir. Bu çalışmada, Hindistan'ın önemli turizm şehirlerinden olan Agra, Jaipur, Jodhpur, New Delhi ve Rishikesh'in sıcaklık, nem ve çiğ noktası gibi uzun vadeli iklim değişkenlerinin tahmin edilmesi amaçlanmıştır. Bu amaçla, geliştirilen ConvGRU hibrit modeli, RF, SVM, CNN, LSTM ve GRU modelleriyle kapsamlı bir şekilde karşılaştırılmıştır. 2010-2024 yılları arasına ait gerçek zamanlı ve güncel bir veriseti kullanılmıştır. Deneysel sonuçlar, ConvGRU’nun tüm şehirler ve iklim değişkenleri için 0,9’un üzerinde R2 değeriyle karşılaştırılan modellerden daha başarılı olduğunu göstermiştir.

Keywords

İklim Değişkenleri, Convolutional Neural Network, Gated Recurrent Unit, Derin Öğrenme, Makine Öğrenmesi

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