Machine Learning for Extreme Weather Events Forecast

2020 International Joint Conference on Neural Networks (IJCNN) @ World Congress on Computational Intelligence July 19-24 2020, Glasgow, UK ( )

Special Session on
Machine Learning for Extreme Weather Events Forecast
Important Dates
  • Paper submission: January 30th, 2020
  • Notification of acceppance: March 15th, 2020
Aim and Scope

In the next few years, due to global climate changes, extreme weather phenomena are expected to become increasingly frequent even in areas previously characterized by a mild climate as, but not limited to, the Mediterranean area. Extreme weather refers to phenomena potentially casting severe damage, serious social disruption, or loss of human life. These phenomena vary, depending on the latitude, altitude, topography, and atmospheric conditions. Recently, the Mediterranean area is subject to a range of destructive weather events, including middle-latitudes storms, Mediterranean sub-tropical hurricane-like storms ("medicanes"), and small-scale but violent local storms. Machine learning techniques and in particular Deep Learning, have been used to recognize, classify, and trace the trajectories of severe storms in atmospheric model data. Even though predicting large-scale atmosphere disturbances is a common activity in numerical weather prediction, the tasks of recognizing, identifying, and tracing trajectories of such extreme weather events within weather model outputs remains challenging.
The aim of the special session is to host the recent research advances in the fields of Severe Weather Events by using Machine Learning techniques.

Potential topics include (but not limited to):
  • Machine Learning
  • Deep Neural Networks
  • Fuzzy and Neuro-Fuzzy Systems
  • Evolutionary approaches
  • Computational models
  • High Performance Computing based methodologies
applied to:
  • Events risk assessment
  • Recognizing, identifying, and tracing storm trajectories
  • Feature selection from meteorological data
  • Weather Forecasting Computational Models
  • Angelo Ciaramella, University of Naples Parthenope, Italy
  • Raffaele Montella, University of Naples Parthenope, Italy
  • Ian Foster, University of Chicago, Argonne National Laboratory, USA

Raffaele Montella,