Modeling using quantum artificial neural networks applied to natural risk management

Abstract

This paper tries to show how modeling and prediction through quantum neural networks can be of great help in managing natural risks more efficiently. Neural networks, or artificial intelligence, allow extrapolating the neuronal functioning of the human brain to all types of modeling and predictions of reality through training and adaptation to each specific risk (climatic, seismic, geomorphological / geological, ...). Its flexibility and plasticity allow the results to include a large number of elements and values that cannot be found in other modeling modes. This work is carried out from the geographical point of view, and it does not intend to show calculations or mathematical modeling, but it seeks to show a theoretical framework that includes the maximum of elements on which a base for a later modeling can be built.