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 accurate prediction of qualitative weather information on winter precipitation types, utilized in Apache Spark Streaming distributed framework. The proposed receives storage and processes data real-time, order to extract useful knowledge from different sensors related data. following, numerical aims at type given three classes namely rain, freezing snow as recorded Automated Surface Observing System (ASOS) network. For depicting effectiveness our schema, regularization technique feature selection so avoid overfitting is implemented. Several classification models covering categorization methods Bayesian, decision trees, meta/ensemble methods, have been investigated real dataset. experimental analysis illustrates that utilization could offer significant boost performance.