Towards Tsunami Informatics: Applying Machine Learning to Data Extracted from Twitter | Ian Lumb's Blog

Even in 2018, our ability to provide accurate tsunami advisories and warnings is exceedingly challenged. In best-case scenarios, advisories and warnings afford inhabitants of low-lying coastal areas minutes or (hopefully) longer to react. In best-case scenarios, advisories and warnings are based upon in situ measurements via tsunameters – as ocean-bottom changes in seawater pressure serve as reliable precursors for impending tsunami arrival. (By way of analogy, tsunameters ‘see’ tsunamis as do radars ‘see’ precipitation. Based on ‘sight’ then, both offer a reasonable ability to ‘nowcast’.) In typical scenarios, however, advisories and warnings can communicate mixed messages. In the case of the recent Sulawesi earthquake and tsunami for example, a nearby alert (for the Makassar Strait) was retracted after some 30 minutes, even though Palu, Indonesia experienced a ‘localized’ tsunami that resulted in significant losses – with current estimates placing the number of fatalities at more tha

Even in 2018, our ability to provide accurate tsunami advisories and warnings is exceedingly challenged. In best-case scenarios, advisories and warnings afford inhabitants of low-lying coastal areas minutes or (hopefully) longer to react. In best-case scenarios, advisories and warnings are based upon in situ measurements via tsunameters – as ocean-bottom changes in seawater pressure serve as reliable precursors for impending tsunami arrival. (By way of analogy, tsunameters ‘see’ tsunamis as do radars ‘see’ precipitation. Based on ‘sight’ then, both offer a reasonable ability to ‘nowcast’.) In