Using Deep Learning and Machine Learning Methods to Diagnose Hailstorms in Large-Scale Thermodynamic Environments
Department of Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA
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Sustainability 2020, 12(24), 10499; https://doi.org/10.3390/su122410499
Received: 14 November 2020 / Revised: 8 December 2020 / Accepted: 13 December 2020 / Published: 15 December 2020
(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)
Hailstorms have caused damages in billions of dollars to industrial, electronic, and mechanical properties such as automobiles, buildings, roads, and aircrafts, as well as life threats to crop and cattle populations, due to their hazardous nature. Hence, the relevance of predicting hailstorms in the future has significant scientific, economic, and societal benefits. However, climate models do not have adequate resolutions to explicitly resolve these subscale phenomena. One solution is to estimate the probability of these storms by using large-scale atmospheric thermodynamic environment variables from climate model outputs, but the existing methods only carried out experiments on small datasets limited to a region, country, or location and a large number of input features. Using one year of Tropical Rainfall Measuring Mission (TRMM) observations and European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim (ERA-Interim) reanalysis on a global scale, this paper develops two deep-learning-based models (an autoencoder and convolutional neural network (CNN)) as well as a machine learning approach (random forest) for hailstorm prediction by using only four attributes—convective potential energy, convective inhibition, 1–3 km wind shear, and warm cloud depth. In the experiments, the random forest approach produces the best hailstorm prediction performance compared to the other two methods.
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Keywords:
hailstorms; hail models; hailstorms; hail predictions; deep learning
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MDPI and ACS Style
Pulukool, F.; Li, L.; Liu, C. Using Deep Learning and Machine Learning Methods to Diagnose Hailstorms in Large-Scale Thermodynamic Environments. Sustainability 2020, 12, 10499. https://doi.org/10.3390/su122410499
AMA Style
Pulukool F, Li L, Liu C. Using Deep Learning and Machine Learning Methods to Diagnose Hailstorms in Large-Scale Thermodynamic Environments. Sustainability. 2020; 12(24):10499. https://doi.org/10.3390/su122410499
Chicago/Turabian StylePulukool, Farha, Longzhuang Li, and Chuntao Liu. 2020. "Using Deep Learning and Machine Learning Methods to Diagnose Hailstorms in Large-Scale Thermodynamic Environments" Sustainability 12, no. 24: 10499. https://doi.org/10.3390/su122410499
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