A visual big data system for the prediction of weather-related variables: Jordan-Spain case study

TL;DR

This paper presents a visual big data system for analyzing and predicting weather variables like temperature and rainfall using large-scale data from weather stations, with promising accuracy and usability.

Contribution

It introduces a novel visual big data system that integrates open weather data into a NoSQL database for predictive analysis and visualization, handling missing data and high dimensionality.

Findings

Normalized mean squared error of 0.00013

Directional symmetry of 0.84

Positive expert usability ratings

Abstract

The Meteorology is a field where huge amounts of data are generated, mainly collected by sensors at weather stations, where different variables can be measured. Those data have some particularities such as high volume and dimensionality, the frequent existence of missing values in some stations, and the high correlation between collected variables. In this regard, it is crucial to make use of Big Data and Data Mining techniques to deal with those data and extract useful knowledge from them that can be used, for instance, to predict weather phenomena. In this paper, we propose a visual big data system that is designed to deal with high amounts of weather-related data and lets the user analyze those data to perform predictive tasks over the considered variables (temperature and rainfall). The proposed system collects open data and loads them onto a local NoSQL database fusing them at different levels of temporal and spatial aggregation in order to perform a predictive analysis using univariate and multivariate approaches as well as forecasting based on training data from neighbor stations in cases with high rates of missing values. The system has been assessed in terms of usability and predictive performance, obtaining an overall normalized mean squared error value of 0.00013, and an overall directional symmetry value of nearly 0.84. Our system has been rated positively by a group of experts in the area (all aspects of the system except graphic desing were rated 3 or above in a 1-5 scale). The promising preliminary results obtained demonstrate the validity of our system and invite us to keep working on this area.