A real-time collaborative machine learning based weather forecasting system with multiple predictor locations

Abstract

Weather forecasting is an important application in meteorology and has been one of the most scientifically and technologically challenging problems around the world. As the drastic effects of climate change continue to unfold, localised short term weather prediction with high accuracy has become more important than ever. In this paper, a collaborative machine learning-based real-time weather forecasting system has been proposed whereby data from several locations are used to predict the weather for a specific location. In this work, five machine learning algorithms have been used and tests have been performed in four different locations in Mauritius to predict weather parameters such as Temperature, Wind Speed, Wind Direction, Pressure, Humidity, and Cloudiness. The weather data were collected using the OpenWeather API from a mobile as well as a desktop edge device. The data were stored as a JSON file in both the IBM Cloudant database and a local MySQL database. Analytics were performed on both a local server that captures the incoming data from the edge device and via a servlet deployed on the IBM cloud platform. Five machine learning algorithms namely Multiple Linear Regression (MLP), Multiple Polynomial Regression (MPR), K-Nearest Neighbours (KNN), Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) were tested using both collaborative and non-collaborative methods. The experiments showed that the collaborative regression schemes achieved 5% lower Mean Absolute Percentage Error (MAPE) than non-collaborative ones and the Multiple Polynomial Regression (MLR) algorithm outperformed all the other algorithms with errors ranging from 0.009% to 9% for the different weather parameters. In general, the results showed that collaborative based weather forecasting with multiple predictor locations can potentially increase the accuracy of the predictions in machine learning algorithms.