Deep Learning Based Temperature and Humidity Prediction
Öz
The temperature and humidity parameters of the weather influence various fields, including living conditions, agriculture, and transportation. Hence, accurately predicting the future values of these parameters is important. In this study, temperature and humidity forecasts were made using deep learning techniques, specifically LSTM algorithms, through a model system created for the Süleymanpaşa district of Tekirdağ province. Temperature and humidity datasets were obtained from the Meteorology Provincial Directorate and integrated with data from multiple sensors to mitigate errors caused by noise in single-sensor data. Temperature and humidity data from the Tekirdağ Meteorology Provincial Directorate between 2015 and 2021 were merged with the 2020 temperature and humidity data obtained from the model system to create a fused dataset. Using this dataset, temperature and humidity data for the year 2022 were predicted using deep learning algorithms. Long Short-Term Memory (LSTM) algorithms were utilized for sequentially ordered data over time. The predicted data were then compared with actual data from the Tekirdağ Meteorology Provincial Directorate for the year 2022. The success metrics for these predictions were calculated as RMSE of 1.895, MSE of 3.547, an R-squared score of 0.952, and an MAE of 1.614. The results suggest that this algorithm can be employed for sequentially ordered data over time. The model system developed is based on PLC and SCADA technology.
Anahtar Kelimeler
Sensor Fusion, LSTM, Temperature and Humidity Prediction, PLC, SCADA
Kaynakça
- I. Gupta, H. Mittal, D. Rikhari, A. K. Singh, Mlrm: A multiple linear regression based model for average temperature prediction of a day, arXiv preprint arXiv:2203.05835. (2022). doi:10.48550/arXiv.2203.05835
- Z. Ouyang, J. Cui, X. Dong, Y. Li, J. Niu, SaccadeFork: A lightweight multi-sensor fusion-based target detector, Information Fusion. 77 (2022), 172-183. doi:10.1016/j.inffus.2021.07.004
- M. Andronie, G. Lăzăroiu, M. Iatagan, I. Hurloiu, R. Ștefănescu, A. Dijmărescu, I. Dijmărescu, Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things, ISPRS International Journal of Geo-Information. 12(2) (2023), 35. doi: 10.3390/ijgi12020035
- X. Chen, T. Zhang, Y. Wang, Y. Wang, H. Zhao, Futr3d: A unified sensor fusion framework for 3d detection, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023), 172-181.
- M. Wang, X. Wang, L. T. Yang, X. Deng, L. Yi, Multi-sensor fusion based intelligent sensor relocation for health and safety monitoring in BSNs. Information Fusion. 54 (2020), 61-71. doi:10.1016/j.inffus.2019.07.002
- H. Orhan, Y. Emrehan, Artificial intelligence-assisted detection model for melanoma diagnosis using deep learning techniques, Mathematical Modelling and Numerical Simulation with Applications. 3(2) (2023), 159-169. doi:10.53391/mmnsa.1311943
- Ö. Arslan, K. Mustafa, Effect of Hilbert-Huang transform on classification of PCG signals using machine learning, Journal of King Saud University-Computer and Information Sciences. 34(10) (2022), 9915-9925. doi:10.1016/j.jksuci.2021.12.019
- S. Yetkin, G. O. Koca, Esnek robotların tasarım, kontrol ve imalat çalışmalar, International Journal of Engineering Research and Development. 13(1) (2021), 74-86. doi:10.29137/umagd.706041
- A. A. SÜZEN, Z. Yildiz, T. Yilmaz, LSTM tabanlı Derin Sinir Ağı ile Ayak Taban Basınç Verilerinden VKİ Durumlarının Sınıflandırılması, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 8(4) (2019), 1392-1398. doi:10.17798/bitlisfen.540273
- W. Zha, Y. Liu, Y. Wan, R. Luo, D. Li, S. Yang, Y. Xu, Forecasting monthly gas field production based on the CNN-LSTM model, Energy. (2022), 124889. doi:10.1016/j.energy.2022.124889