Development of Technology for Identification of Climate Patterns during Floods Using Global Climate Model Data with Convolutional Neural Networks

Abstract

Given the increasing climate variability, it is becoming difficult to predict flooding events. We may be able to manage or even prevent floods if detecting global climate patterns, which affect flood occurrence, and using them to make predictions are possible. In this study, we developed a deep learning-based model to learn climate patterns during floods and determine flood-induced climate patterns using a convolutional neural network. We used sea surface temperature anomaly as the learning data, after classifying them into four cases according to the spatial extent. The flood-induced climate pattern identification model showed an accuracy of ≥89.6% in all cases, indicating its application for the determination of patterns. The obtained results can help predict floods by recognizing climate patterns of flood precursors and be insightful to international cooperation projects based on global climate data.