An Augmentative Approach to Study Climate Change Using Random Forest
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Dong Katherine
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Understanding the best indicators of climate change is essential to predicting the magnitude of climate change in the future. Machine learning models can use features that indicate climate change to determine its impacts. To forecast mean temperature rise, the random forest algorithm is used on a collective dataset containing different indicators of climate change. The indicators include sea levels, temperature anomalies, CO2 levels, land minus ocean means, and arctic sea ice volumes. The original dataset began with one feature, mean temperature, and several other datasets were augmented to create a larger, more informative dataset. Projecting climate change is modeled as a classification problem with the mean temperature rise as a dependent variable using the random forest model. The features Land Minus Ocean Mean Rise Indicator, Arctic Sea Ice extent, and Mean Temperature Anomalies are the most important variables for predicting temperature change. Running the model yields an R-squared value of 0.58 with mean squared error (MSE) of 0.10, indicating a reasonably effective predicting power of climate change using the identified indicators. This research serves as a guide [NY1] [KD2] for effectively curating and augmenting climate change data and the forecasting of climate change and other similar environmental changes using similar temporal and statistics data.
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Authors
Dong Katherine
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