Constructing an efficient machine learning model for tornado prediction

2018/06/15 by Aleskerov, F., Baiborodov, N., Demin, S. +4
#Atmospheric and Oceanic Physics (physics.ao-ph) #FOS: Mathematics #FOS: Physical sciences #Optimization and Control (math.OC)

paper · doi:10.48550/arxiv.1806.05857

Abstract

Tornado prediction methods and main mechanisms of tornado genesis were analyzed. A model, based on the superposition principle, has been built. For efficiency evaluation, the constructed model has been tested on real-life data obtained from the University of Oklahoma (USA). It is shown that the constructed tornado prediction model is more efficient than all previous models.

Related

• Predicting Tornadoes days ahead with Machine Learning
• A Novel Hybrid Approach for Tornado Prediction in the United States: Kalman-Convolutional BiLSTM with Multi-Head Attention
• A Database Engineered System for Big Data Analytics on Tornado Climatology
• Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning
• Predicting property damage from tornadoes with zero-inflated neural\n networks