Global optimization for data assimilation in landslide tsunamis models

TL;DR

This paper develops a data assimilation framework for landslide tsunami models using coupled shallow-water and granular landslide models, employing advanced global optimization algorithms to improve model accuracy.

Contribution

It introduces a novel data assimilation approach for complex coupled models using parallel metaheuristic and hybrid optimization algorithms.

Findings

Parallel multi-path Simulated Annealing algorithms outperform traditional methods.

Hybrid algorithms combining Simulated Annealing with gradient-based local search improve optimization.

The proposed methods enhance the accuracy of landslide tsunami modeling.

Abstract

The goal of this article is to make automatic data assimilation for a landslide tsunami model, given by the coupling between a non-hydrostatic multi-layer shallow-water and a Savage-Hutter granular landslide model for submarine avalanches. The coupled model is discretized using a positivity-preserving second-order path-conservative finite volume scheme. The data assimilation problem is posed in a global optimization framework and we develop and compare parallel metaheuristic stochastic global optimization algorithms, more precisely multi-path versions of the Simulated Annealing algorithm, with hybrid global optimization algorithms based on hybridizing Simulated Annealing with gradient local searchers, like L-BGFS-B.