Abstract
An artificial intelligence technology with the two-dimensional principal component analysis (2DPCA) method is introduced into the early identification of rainstorm risks. The characteristic subspace for historical rainstorm events can be constructed through the 2DPCA method. When identifying an ongoing rainfall process, the most similar rainstorm event can be found through comparing the features of the ongoing rainfall to the events in the characteristic subspace. Taking the most similar historical rainstorm event found as a reference, the possible duration and intensity of the ongoing rainfall process can be estimated, thus achieving early identification of rainstorm risks. Two groups of validation experiments are performed based on a database including 116 rainstorm events observed in Shenzhen metropolis, China. The validation shows that although the ratio of historical events to validation events impacts the performance of identification, the identified historical events are generally similar to the ongoing rainfall processes in terms of rainfall duration, range of influence, magnitude, and maximum single-station rainfall.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
