GitHub / javedali99 / si2023-compound-flooding
Contributions of Storm Drivers to Compound Flooding in New York City: Insights from Coupled Modeling and Machine Learning Approaches
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/javedali99%2Fsi2023-compound-flooding
PURL: pkg:github/javedali99/si2023-compound-flooding
Stars: 1
Forks: 0
Open issues: 0
License: mit
Language: HTML
Size: 158 MB
Dependencies parsed at: Pending
Created at: about 2 years ago
Updated at: over 1 year ago
Pushed at: over 1 year ago
Last synced at: about 1 year ago
Commit Stats
Commits: 125
Authors: 2
Mean commits per author: 62.5
Development Distribution Score: 0.112
More commit stats: https://commits.ecosyste.ms/hosts/GitHub/repositories/javedali99/si2023-compound-flooding
Topics: compound-flooding, coupled-model, hydrodynamic-modeling, hydrological-modelling, machine-learning, python, sea-level-rise, storm-surge