Abstract
When colossal gravity-driven mass flows enter a body of water, they may generate waves which can have destructive consequences on coastal areas. A number of empirical equations in the form of power functions of several dimensionless groups have been developed to predict wave characteristics. However, in some complex cases (for instance, when the mass striking the water is made up of varied slide materials), fitting an empirical equation with a fixed form to the experimental data may be problematic. In contrast to previous empirical equations that specified the mathematical operators in advance, we developed a purely data-driven approach which relies on datasets and does not need any assumptions about functional form or physical constraints. Experiments were carried out using Carbopol Ultrez 10 (a viscoplastic polymeric gel) and polymer–water balls. We selected an artificial neural network model as an example of a data-driven approach to predicting wave characteristics. We first validated the model by comparing it with best-fit empirical equations. Then, we applied the proposed model to two scenarios which run into difficulty when modeled using those empirical equations: (i) predicting wave features from subaerial landslide parameters at their initial stage (with the mass beginning to move down the slope) rather than from the parameters at impact; and (ii) predicting waves generated by different slide materials, specifically, viscoplastic slides, granular slides, and viscoplastic–granular mixtures. The method proposed here can easily be updated when new parameters or constraints are introduced into the model.
Highlights
When colossal gravity-driven mass flows enter a body of water, such as a sea, a lake, or a reservoir, they sometimes generate large waves
Predicting the characteristics of waves induced by subaerial landslides is of great importance for risk management in coastal areas [3]
To overcome the limitations of empirical equations, the present study presents a data-driven method, known as an artificial neural network (ANN) method, which has been successfully employed in other fields to cope with complicated parameters in experimental data processing and to develop highly accurate predictive models [28,29,30,31,32,33]
Summary
When colossal gravity-driven mass flows enter a body of water, such as a sea, a lake, or a reservoir, they sometimes generate large waves These events are relevant in coastal areas and mountainous countries. Researchers have conducted experiments using physical models that try to reproduce the physical processes of impulse waves generated by subaerial landslides They have simplified water geometry by using 2D flumes or 3D basins and idealized the sliding masses as rigid blocks [4,5,6,7,8], granular solids [9,10,11,12,13,14], or viscoplastic fluids [15,16]. Most equations to date have expressed wave characteristics as power functions of several slide parameters on impact, and some have occasionally involved an additive term [22]
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