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

Öz

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.

Anahtar Kelimeler

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

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

1. K.U. Jaseena, and B.C. Kovoor, “Deterministic weather forecasting models based on intelligent predictors: A survey”, Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 6, pp. 3393-3412, 2022.
2. C.H. Huang, H.H. Tsai, and H.C. Chen, “Influence of weather factors on thermal comfort in subtropical urban environments”, Sustainability, vol. 12, no. 5, 2020.
3. M. Bulté, T. Duren, O. Bouhon, E. Petitclerc, M. Agniel, and A. Dassargues, “Numerical modeling of the interference of thermally unbalanced Aquifer Thermal Energy Storage systems in Brussels (Belgium)”, Energies, vol. 14, no. 19, 2021.
4. J.S. Nanditha, B. Rajagopalan, and V. Mishra, “Combined signatures of atmospheric drivers, soil moisture, and moisture source on floods in Narmada River basin, India”, Climate Dynamics, vol. 59, no. 9, pp. 2831-2851, 2022.
5. X. Yang, L.L. Peng, Y. Chen, L. Yao, and Q. Wang, “Air humidity characteristics of local climate zones: A three-year observational study in Nanjing”, Building and Environment, no. 171, 2020.
6. L. Gimeno, J. Eiras-Barca, A.M. Durán-Quesada, F. Dominguez, R. van der Ent, H. Sodemann, and J.W. Kirchner, ”The residence time of water vapour in the atmosphere”, Nature Reviews Earth & Environment, vol. 2, no. 8, pp. 558-569, 2021.
7. J. Lin, K. Thu, S. Karthik, M.W. Shahzad, R. Wang, and K.J. Chua, ”Understanding the transient behavior of the dew point evaporative cooler from the first and second law of thermodynamics”, Energy Conversion and Management, no. 244, 2021.
8. M.W. Shahzad, J. Lin, B.B. Xu, L. Dala, Q. Chen, M. Burhan, and K.C. Ng, ”A spatiotemporal indirect evaporative cooler enabled by transiently interceding water mist”, Energy, no. 217, 2021.

9. X. Zuo, “Impact of air pollution: tourists’ decision making behaviour during rural tourism”, Journal of Environmental Engineering and Science, vol. 40, pp. 1-6, 2024.
10. M.V. Sivakumar, “Climate extremes and impacts on agriculture”, Agroclimatology: Linking Agriculture to Climate, vol. 60, pp. 621-647, 2020.
11. K.U. Jaseena, and B.C. Kovoor, “Deterministic weather forecasting models based on intelligent predictors: A survey”, Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 6, pp. 3393-3412, 2022.
12. W. Fang, Q. Xue, L. Shen, and V.S. Sheng, “Survey on the application of deep learning in extreme weather prediction. Atmosphere, vol. 12, no. 6, 2021.
13. S. Srivastava, “Economic potential of tourism: A case study of agra”, Tourismos, vol. 6, no. 2, pp. 139-158, 2011.
14. B.K. Sharma, S. Kulshreshtha, and A.R. Rahmani, “Faunal Heritage of Rajasthan”, Indian Journal, 2011.
15. G. Kaur, A. Ahuja, S.N. Thakur, M. Pandit, R. Duraiswami, A. Singh, and S. Garg, “Jodhpur sandstone: an architectonic heritage stone from India”, Geoheritage, vol. 12, pp. 1-17, 2020.
16. S.S. Shukla, D.K. Goswami, “Indian tourism industry overview of Indian tourism”, International Journal of Technology Management & Humanities (IJTMH), vol. 1, no. 1, 2015.
17. P. Kanungo, “Construction and Transformation of a Sacred Urban Complex of Hardwar-Rishikesh, North India”, Archiv für Religionsgeschichte, vol. 25, no. 1, pp. 211-226, 2023.
18. A. Bekkar, B. Hssina, S. Douzi, and K. Douzi, “Air-pollution prediction in smart city, deep learning approach”, Journal of big Data, vol. 8, pp. 1-21, 2021.
19. S. Abirami, and P. Chitra, “Regional air quality forecasting using spatiotemporal deep learning”, Journal of cleaner production, vol. 283, 2021.
20. K.R. Patil, and M. Iiyama, “Deep learning models to predict sea surface temperature in Tohoku region”, IEEE Access, vol. 10, pp. 40410-40418, 2022.
21. G. Ravindiran, G. Hayder, K. Kanagarathinam, A. Alagumalai, and C. Sonne, “Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam”, Chemosphere, vol. 338, 2023.
22. I. Ayus, N. Natarajan, and D. Gupta, “Comparison of machine learning and deep learning techniques for the prediction of air pollution: a case study from China”, Asian Journal of Atmospheric Environment, vol.17, no. 1, 2023.
23. K. Kumar, and B.P. Pande, “Air pollution prediction with machine learning: a case study of Indian cities”, International Journal of Environmental Science and Technology, vol. 20, no. 5, pp. 5333-5348, 2023.
24. F. Naz, C. McCann, M. Fahim, T.V. Cao, R. Hunter, N. T. Viet, and T. Q. Duong, “Comparative analysis of deep learning and statistical models for air pollutants prediction in urban areas”, IEEE Access, vol. 11, 2023.
25. A. Barthwal, and A.K. Goel, “Advancing air quality prediction models in urban India: a deep learning approach integrating DCNN and LSTM architectures for AQI time-series classification”, Modeling Earth Systems and Environment, pp. 1-21, 2024.
26. F. Mohammadi, H. Teiri, Y. Hajizadeh, A. Abdolahnejad, and A. Ebrahimi, “Prediction of atmospheric PM2. 5 level by machine learning techniques in Isfahan, Iran”, Scientific Reports, vol. 14, no. 1, 2024.
27. D. Krivoguz, A. Ioshpa, S. Chernyi, A. Zhilenkov, A. Kustov, A. Zinchenko, P. Tsareva, “Enhancing Long-Term Air Temperature Forecasting with Deep Learning Architectures”, Journal of Robotics and Control (JRC), vol. 5, no. 3, pp. 706-716, 2024.
28. A. Mishra, and Y. Gupta, “Comparative analysis of Air Quality Index prediction using deep learning algorithms”, Spatial Information Research, vol. 32, no. 1, pp. 63-72, 2024.
29. Kaggle, “Indian Cities Weather 2010-2024: Dive In!”, [Çevrimiçi]. Erişim: https://www.kaggle.com/datasets/mukeshdevrath007/indian-5000-cities-weather-data/data
30. A. Utku, “Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries” Expert Systems with Applications, vol. 231, 2023.
31. M.M. Ghiasi, and S. Zendehboudi, “Application of decision tree-based ensemble learning in the classification of breast cancer”, Computers in biology and medicine, vol. 128, 2021.
32. V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo, and M.J.O.G.R. Chica-Rivas, “Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines”, Ore Geology Reviews, vol. 71, pp. 804-818, 2015.
33. A.M. Prasad, L.R. Iverson, and A. Liaw, “Newer classification and regression tree techniques: bagging and random forests for ecological prediction”, Ecosystems, vol. 9, pp. 181-199, 2006.
34. J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends”, Neurocomputing, vol. 408, pp. 189-215, 2020.
35. M.A. Chandra, S.S. Bedi, “Survey on SVM and their application in image classification. International Journal of Information Technology, vol. 13, no. 5, pp. 1-11, 2021.
36. Y. Liu, H. Pu, and D.W. Sun, “Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices”, Trends in Food Science & Technology, vol. 113, pp. 193-204, 2021.
37. C. Chen, C. Meng, Y. Ma, M. Zhu, X. Wang, X. Xie, and C. Chen, “MGFFCNN: Two‐dimensional matrix spectroscopy combined with multi‐channel gradient feature fusion convolutional neural network means to diagnose glioma and esophageal cancer patients”, Journal of Raman Spectroscopy, vol. 54, no. 4, pp. 385-396, 2023.
38. S.M. Al-Selwi, M.F. Hassan, S.J. Abdulkadir, A. Muneer, “LSTM inefficiency in long-term dependencies regression problems”, Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 30, no. 3, pp. 16-31, 2023.
39. B. Lindemann, T. Müller, H. Vietz, N. Jazdi, and M. Weyrich, “A survey on long short-term memory networks for time series prediction”, Procedia Cirp, vol. 99, pp. 650-655, 2021.
40. F. Landi, L. Baraldi, M. Cornia, and R. Cucchiara, “Working memory connections for LSTM”, Neural Networks, vol. 144, pp. 334-341, 2021.
41. Y. Khalifa, D. Mandic, and E. Sejdić, “A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals”, Information Fusion, vol. 69, pp. 52-72, 2021.
42. T. Wadhera, J. Bedi, and S. Sharma, “Autism spectrum disorder prediction using bidirectional stacked gated recurrent unit with time-distributor wrapper: an EEG study”, Neural Computing and Applications, vol. 35, no. 13, pp. 9803-9818, 2023.
43. Z. Zainuddin, and M.H. Hasan, “Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data”, Bulletin of Electrical Engineering and Informatics, vol. 10, no. 2, pp. 870-878, 2021.
44. L.Y. Chen, Y.T. Chen, Y.H. Chen, and D.S. Lee, “Applicability of energy consumption prediction models in a department store: A case study”, Case Studies in Thermal Engineering, vol. 49, 2023.