A Regularization-Based Big Data Framework for Winter Precipitation Forecasting on Streaming Data

In the current paper, we propose a machine learning forecasting model for the accurate prediction of qualitative weather information on winter precipitation types, utilized in Apache Spark Streaming distributed framework. The proposed model receives storage and processes data in real-time, in order...

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Bibliographic Details
Published in:Electronics (Basel) 2021-08, Vol.10 (16), p.1872
Main Authors: Kanavos, Andreas, Trigka, Maria, Dritsas, Elias, Vonitsanos, Gerasimos, Mylonas, Phivos
Format: Article
Language:English
Subjects:
Classification
Decision trees
Floods
Freezing
Machine learning
Mathematical models
Meteorological data
Numerical prediction
Numerical weather forecasting
Precipitation
Prediction models
Rain
Regularization
Sensors
Winter
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Summary:In the current paper, we propose a machine learning forecasting model for the accurate prediction of qualitative weather information on winter precipitation types, utilized in Apache Spark Streaming distributed framework. The proposed model receives storage and processes data in real-time, in order to extract useful knowledge from different sensors related to weather data. In following, the numerical weather prediction model aims at forecasting the weather type given three precipitation classes namely rain, freezing rain, and snow as recorded in the Automated Surface Observing System (ASOS) network. For depicting the effectiveness of our proposed schema, a regularization technique for feature selection so as to avoid overfitting is implemented. Several classification models covering three different categorization methods namely the Bayesian, decision trees, and meta/ensemble methods, have been investigated in a real dataset. The experimental analysis illustrates that the utilization of the regularization technique could offer a significant boost in forecasting performance.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10161872