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A Study on Distributed Machine Learning Techniques for Large-Scale Weather Forecasting
Abstract
The weather data generated, processed, and collected by the sensor or shared by IoT devices and mobile devices has significantly increased weather data collection in daily life. The data generation speed also accelerated, and a vast amount of data has been collected and stored in distributed databases, improving weather forecasting. Still, the conventional processing method for massive data is distributed and centralized computing, and this chapter looks into how distributed machine learning techniques help as to increase the processing speed. Some distributed frameworks that play a significant role in massive data, like MapReduce, have been trained and tested to resolve various machine learning problems in a distributed environment. The aim of this chapter will provide different information about datasets, issues, platforms, and optimized approaches in a distributed environment. So, researchers can use and deploy new techniques in machine learning algorithms. It helps the researchers develop new strategies in distributed computing environments.
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