Read and Download Free eBooks
/
Categories
Browse by category
1,965 books to get lost inYours to read. Yours to keep.
No books found.
Try another title or author.
Try another title or author.
This report develops random forest regression models for estimating low-streamflow statistics at locations in New York where direct streamgage records are unavailable. It focuses on the mainland part of the state and explains how observations from monitored watersheds can inform estimates elsewhere.
The study describes its streamflow and watershed-characteristic inputs, the preparation of model datasets, and the evaluation of predictive performance. It examines the relationship between basin characteristics and low-flow behaviour while documenting the limitations of transferring model estimates to ungaged locations.
Readers will find an applied example of machine learning within hydrology, supported by maps, tables, methods, and references. The publication distinguishes modelled estimates from measured records and explains the scope within which its results are intended to be used. Its associated data links provide a route to the supporting material without replacing the complete explanatory report.
Timothy J. Stagnitta; Joshua C. Woda; Alexander P. Graziano. Random Forest Regression Models for Estimating Low-Streamflow Statistics at Ungaged Locations in New York, Excluding Long Island. U.S. Geological Survey, 2025. https://doi.org/10.3133/sir20255060. Public domain U.S. Government publication. Original agency, author and photograph credits retained.