leelew/RFGranger
Nonlinear Granger causality test based on random forest (source code of "A Causal Inference Model based on Random Forest to identify soil moisture-precipitation feedback", Journal of Hydrometeorology)
This project helps hydrometeorologists and climate scientists understand the cause-and-effect relationships between soil moisture and precipitation. It takes in historical climate data, such as daily or monthly soil moisture and precipitation measurements, and outputs a detailed analysis of their causal links. Researchers can use this to better model climate feedback loops.
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Use this if you need to determine the nonlinear causal relationship between soil moisture and precipitation, accounting for seasonal and interannual variability.
Not ideal if you are looking for a general-purpose causal inference tool for unrelated datasets, or if your data does not involve hydrometeorological variables.
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Language
MATLAB
License
MIT
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Last pushed
Oct 03, 2022
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