Fog-Enabled Machine Learning Approaches for Weather Prediction in IoT Systems: A Case Study

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書誌詳細
出版年:	Sensors vol. 25, no. 13 (2025), p. 4070-4093
第一著者:	İşler Buket
その他の著者:	Kaya, Şükrü Mustafa, Kılıç, Fahreddin Raşit
出版事項:	MDPI AG
主題:	Turkey Istanbul Turkey Big Data Machine learning Accuracy Precipitation Humidity Deep learning Wavelet transforms Emergency preparedness Decision making Neural networks Sensors Data processing Air pollution Energy efficiency Data analysis Data collection Weather forecasting Batch processing Cloud computing Energy consumption Internet of Things Residential buildings Resource management
オンライン･アクセス:	Citation/Abstract Full Text + Graphics Full Text - PDF
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その他の書誌記述
抄録:	Temperature forecasting is critical for public safety, environmental risk management, and energy conservation. However, reliable forecasting becomes challenging in regions where governmental institutions lack adequate measurement infrastructure. To address this limitation, the present study aims to improve temperature forecasting by collecting temperature, pressure, and humidity data through IoT sensor networks. The study further seeks to identify the most effective method for the real-time processing of large-scale datasets generated by sensor measurements and to ensure data reliability. The collected data were pre-processed using Discrete Wavelet Transform (DWT) to extract essential features and reduce noise. Subsequently, three wavelet-processed deep-learning models were employed: Wavelet-processed Artificial Neural Networks (W-ANN), Wavelet-processed Long Short-Term Memory Networks (W-LSTM), and Wavelet-processed Bidirectional Long Short-Term Memory Networks (W-BiLSTM). Among these, the W-BiLSTM model yielded the highest performance, achieving a test accuracy of 97% and a Mean Absolute Percentage Error (MAPE) of 2%. It significantly outperformed the W-LSTM and W-ANN models in predictive accuracy. Forecasts were validated using data obtained from the Turkish State Meteorological Service (TSMS), yielding a 94% concordance, thereby confirming the robustness of the proposed approach. The findings demonstrate that the W-BiLSTM-based model enables reliable temperature forecasting, even in regions with insufficient governmental measurement infrastructure. Accordingly, this approach holds considerable potential for supporting data-driven decision-making in environmental risk management and energy conservation.
ISSN:	1424-8220
DOI:	10.3390/s25134070
ソース:	Health & Medical Collection