Probabilistic tsunami forecasting for early warning

Abstract

Tsunami warning centres face the challenging task of rapidly forecasting tsunami threat immediately after an earthquake, when there is high uncertainty due to data deficiency. Here we introduce Probabilistic Tsunami Forecasting (PTF) for tsunami early warning. PTF explicitly treats data- and forecast-uncertainties, enabling alert level definitions according to any predefined level of conservatism, which is connected to the average balance of missed-vs-false-alarms. Impact forecasts and resulting recommendations become progressively less uncertain as new data become available. Here we report an implementation for near-source early warning and test it systematically by hindcasting the great 2010 M8.8 Maule (Chile) and the well-studied 2003 M6.8 Zemmouri-Boumerdes (Algeria) tsunamis, as well as all the Mediterranean earthquakes that triggered alert messages at the Italian Tsunami Warning Centre since its inception in 2015, demonstrating forecasting accuracy over a wide range of magnitudes and earthquake types.

KOERI’s Tsunami Warning System in the Eastern Mediterranean and Its Connected Seas: A Decade of Achievements and Challenges

“…Hence, efforts should focus on capacity-building programs targeting civil protection authorities or government agencies with national responsibility, such as NTWCs or TWFPs. While recent efforts have focused on the use of probabilistic methods [43] to overcome the uncertainties associated with the deterministic approaches, whether through a decision matrix, pre-calculated tsunami scenario databases or near-real-time tsunami modeling, the real problem is on the side of the downstream component [44], namely the civil protection authorities, who have the responsibility of initiating tsunami evacuation orders. The uncertainty in the upstream component of the tsunami warning system cannot be avoided, unless all TSPs are synchronized in such a way that a unique warning message could be produced, which would correspond to an ill-formulization of the interoperability concept.…”

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

“…It includes two models: Logistic model (logistic regression model) and Probit model (multiple probability ratio), and some scholars think that they are a kind of model. Literature [ 8 ] constructs the early warning index system and constructs the early warning model with the method of conditional probability model. Literature [ 9 ] introduces the neural network model into the field of intelligent emergency risk prediction for sudden financial disasters.…”

Tsunami Alert Efficiency

“…Thus, at present tsunami warning is associated with large uncertainties [33], and false alarms are unavoidable. To cope with these shortcomings, warning centers adopt a probabilistic tsunami forecasting (PTF) approach in order to quantify and reduce those uncertainties in real-time [1,34]. Without explaining this to the general public, warning messages can be perceived as "cry wolf" [35], lead to warning fatigue [36], and TSPs or NTWCs may lose trust and credibility.…”