A Review of Application of Machine Learning in Storm Surge Problems

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Abstract

The rise of machine learning (ML) has significantly advanced the field of coastal oceanography. This review aims to examine the existing deficiencies in numerical predictions of storm surges and the effort that has been made to improve the predictive accuracy through the application of ML. The readers are guided through the steps required to implement ML algorithms, from the first step of formulating problems to data collection and determination of input features to model selection, development and evaluation. Additionally, the review explores the application of hybrid methods, which combine the bilateral advantages of data-driven methods and physics-based models. Furthermore, the strengths and limitations of ML methods in predicting storm surges are thoroughly discussed, and research gaps are identified. Finally, we outline a vision toward a trustworthy and reliable storm surge forecasting system by introducing novel physics-informed ML techniques. We are meant to provide a primer for beginners and experts in coastal ocean sciences who share a keen interest in ML methodologies in the context of storm surge problems.

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Long Short-Term Memory

Jürgen Schmidhuber, Jürgen Schmidhuber (2002)

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.