[Paper Review] A survey on tidal analysis and forecasting methods for Tsunami detection

Sergio Consoli, Diego Reforgiato Recupero+1 more|arXiv (Cornell University)|Mar 1, 2014

Oceanographic and Atmospheric Processes56 references19 citations

TL;DR

This survey reviews classical and advanced tidal analysis and forecasting methods for tsunami detection, emphasizing harmonic analysis, filtering, neural networks, and wavelet transforms. It evaluates their effectiveness in handling both astronomical and non-astronomical influences, offering a comprehensive comparison of techniques for accurate, timely tsunami warnings with varying data availability.

ABSTRACT

Accurate analysis and forecasting of tidal level are very important tasks for human activities in oceanic and coastal areas. They can be crucial in catastrophic situations like occurrences of Tsunamis in order to provide a rapid alerting to the human population involved and to save lives. Conventional tidal forecasting methods are based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters and long-term measured data are required for precise tidal level predictions with harmonic analysis. Furthermore, traditional harmonic methods rely on models based on the analysis of astronomical components and they can be inadequate when the contribution of non-astronomical components, such as the weather, is significant. Other alternative approaches have been developed in the literature in order to deal with these situations and provide predictions with the desired accuracy, with respect also to the length of the available tidal record. These methods include standard high or band pass filtering techniques, although the relatively deterministic character and large amplitude of tidal signals make special techniques, like artificial neural networks and wavelets transform analysis methods, more effective. This paper is intended to provide the communities of both researchers and practitioners with a broadly applicable, up to date coverage of tidal analysis and forecasting methodologies that have proven to be successful in a variety of circumstances, and that hold particular promise for success in the future. Classical and novel methods are reviewed in a systematic and consistent way, outlining their main concepts and components, similarities and differences, advantages and disadvantages.

Motivation & Objective

• To provide a systematic review of tidal forecasting methods applicable to tsunami detection.
• To evaluate the strengths and limitations of conventional harmonic analysis under data-scarce or non-astronomical conditions.
• To assess alternative methods such as neural networks and wavelet transforms for improved accuracy in short or noisy tidal records.
• To compare classical and novel techniques in terms of computational complexity, data requirements, and predictive performance.
• To guide researchers and practitioners in selecting optimal methods for real-time tsunami warning systems.

Proposed method

• Harmonic analysis using least squares to estimate tidal constituent amplitudes and phases from long-term sea level data.
• Application of high- and band-pass filtering to isolate tidal components from noise and non-tidal signals.
• Employment of artificial neural networks to model complex, nonlinear tidal patterns without explicit physical assumptions.
• Use of wavelet transform analysis to decompose tidal signals into time-frequency components for localized feature extraction.
• Integration of meteorological and oceanic forcing effects into tidal models to improve accuracy during extreme events.
• Systematic comparison of methods based on data length, prediction accuracy, and computational efficiency.

Experimental results

Research questions

• RQ1 How do traditional harmonic methods perform when non-astronomical factors such as weather significantly influence tides?
• RQ2 What are the advantages and limitations of neural networks and wavelet transforms in tidal forecasting with limited data?
• RQ3 How do filtering techniques compare to physical modeling in isolating tidal components from noisy signals?
• RQ4 Which methods maintain high accuracy with short or incomplete tidal records?
• RQ5 What criteria should be used to select an optimal tidal forecasting method for real-time tsunami warning systems?

Key findings

• Harmonic analysis remains effective but requires long-term data and struggles with non-astronomical influences such as storm surges.
• Neural networks and wavelet transforms outperform traditional methods in short-data scenarios and complex, nonlinear tidal dynamics.
• Filtering techniques are useful for noise reduction but lack predictive power for long-term or extreme event forecasting.
• Wavelet-based methods provide superior time-frequency localization, enabling detection of transient tidal anomalies linked to tsunamis.
• Hybrid approaches combining physical models with data-driven techniques (e.g., neural networks) show promise for robust, adaptive tsunami warning systems.
• The choice of method depends critically on data availability, required accuracy, and real-time processing constraints.