Climate related natural disasters: A crucial challenge for port resilience. A neural network application

Abstract

Current consumption habits are enabled due to the various commercial ports around the world. Goods are transported and traded only due to the existence of ports since the ancient days. However, any port disruptions jeopardize the ordinary consumption patterns. A well know suspect of port operations is climate change. Climate change shifts weather patterns causing more severe and more frequent weather events very often responsible for disturbance of port operations and marine roots. In this context, we investigate how Deep Learning Neural Networks (DLNN), in contrast to the traditional Numerical Weather Prediction (NWP) processes, could offer more accurate weather predictions in port regions preventing major economic losses. This Thesis presents the relative state-of-the-art literature on deep learning weather prediction and constructs 5 days forecasts for the ten biggest US commercial ports for 2023.