Response Framework: Tidal analysis and prediction through physics-informed ML

Abstract

Tides pose significant operational and engineering challenges to numerous industries and are critical drivers of many natural processes. Accurate tidal predictions are important for modelling these phenomena. Conventionally, tidal prediction is carried out using harmonic analysis which places severe restrictions on the minimum length of tidal records and cannot separate oceanography from astronomy. While Munk and Cartwright’s response method revolutionized tidal analysis, the difficulty of realistic input function selection has made its automated adaptation challenging. Here, we develop a new framework for tidal analysis and prediction based on embedding universal function approximators within Munk and Cartwright’s response method. The new ML Response Framework overcomes the challenges imposed by the original method and demonstrates superior predictive accuracy over harmonic analysis, typically using 90% less data. We devise a method for obtaining physical insights from the learned model and apply this to quantify and predict the tidal response to meteorological and other non-tidal forcing. By disentangling oceanography from astronomy, the ML Response Framework makes straightforward the study of phenomena which heretofore could not be accounted for. These include storm surges, tidal rivers, and anthropogenic climate change. An open-source Python package (RTide) is provided, and shows promise in terms of application to storm-surge modelling.