2020/08/24 by A H M Jakaria, Jakaria, A H M, Md Mosharaf Hossain +4
Earth and Planetary Sciences · Engineering · Environmental Science · #Applications (stat.AP) #Computers and Society (cs.CY) #Energy Load and Power Forecasting #FOS: Computer and information sciences #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Meteorological Phenomena and Simulations
paper · pdf · doi:10.48550/arxiv.2008.10789
openalex publication_date 2020/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Traditionally, weather predictions are performed with the help of large complex models of physics, which utilize different atmospheric conditions over a long period of time. These conditions are often unstable because of perturbations of the weather system, causing the models to provide inaccurate forecasts. The models are generally run on hundreds of nodes in a large High Performance Computing (HPC) environment which consumes a large amount of energy. In this paper, we present a weather prediction technique that utilizes historical data from multiple weather stations to train simple machine learning models, which can provide usable forecasts about certain weather conditions for the near future within a very short period of time. The models can be run on much less resource intensive environments. The evaluation results show that the accuracy of the models is good enough to be used alongside the current state-of-the-art techniques. Furthermore, we show that it is beneficial to leverage the weather station data from multiple neighboring areas over the data of only the area for which weather forecasting is being performed.