Exploring Random Forest Machine Learning and Remote Sensing Data for Streamflow Prediction: An Alternative Approach to a Process-Based Hydrologic Modeling in a Snowmelt-Driven Watershed

Exploring Random Forest Machine Learning and Remote Sensing Data for Streamflow Prediction: An Alternative Approach to a Process-Based Hydrologic Modeling in a Snowmelt-Driven Watershed

Physically based hydrologic models require significant effort and extensive information for development, calibration, and validation. The study explored the use of the random forest regression (RFR), a supervised machine learning (ML) model, as an alternative to the physically based Soil and Water A...

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Bibliographic Details
Main Authors: Khandaker Iftekharul Islam, Emile Elias, Kenneth C. Carroll, Christopher Brown
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Remote Sensing
Subjects:
streamflow prediction
random forest machine learning
hydrologic modeling
water resource management
remote sensing data
climate change
Online Access:https://www.mdpi.com/2072-4292/15/16/3999

Internet

https://www.mdpi.com/2072-4292/15/16/3999

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