arctic

Predicting Arctic Sea Ice with Machine Learning

UCL source code and report (December 2020)

https://github.com/UCL-BENV0091-Antarctic/arctic

Python environment

We use Jupyter Lab as our development and analysis platform.

Main libraries

import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns # contains barplot
from sklearn.metrics import r2_score, explained_variance_score, mean_squared_error, mean_absolute_error
from sklearn.linear_model import LassoCV # lasso linear model with cross-validation
from sklearn.ensemble import RandomForestRegressor # random forest
from sklearn.ensemble import GradientBoostingRegressor # gradient boosting
from sklearn.neural_network import MLPRegressor # multiplayer perceptron

Data Credits

• (new) Arctic ice volumn: PIOMAS Ice Volume Data (daily; 1979-present (the 90th day of 2022); downloaded on 24th Apr 2022)
• Arctic sea ice: National Snow & Ice Data Center
• CO2: National Oceanic and Atmospheric Administration
• Ozone: NASA Goddard Space Flight Center
• Temperature: National Oceanic and Atmospheric Administration
• Rainfall and daylight: Weather US
• Population:Our World in Data
• GDP in current USD: World Bank & IMF (World GDP will fall -4.4% in 2020, +5.2% in 2021, +4.2% in 2022)

Key results: Performance Table

Experiment	Metric	Value
LassoLinearmodel	R2	0.916707
LassoLinearmodel	Explained Variance	0.920295
LassoLinearmodel	MSE	0.006924
LassoLinearmodel	MAE	0.068930
PolynomialLassoLinearmodel	R2	0.941503
PolynomialLassoLinearmodel	Explained Variance	0.941943
PolynomialLassoLinearmodel	MSE	0.004863
PolynomialLassoLinearmodel	MAE	0.059528
RandomForestmodel	R2	0.988162
RandomForestmodel	Explained Variance	0.988394
RandomForestmodel	MSE	0.000984
RandomForestmodel	MAE	0.024542
AdvancedNN	R2	0.984525
AdvancedNN	Explained Variance	0.984801
AdvancedNN	MSE	0.001286
AdvancedNN	MAE	0.026479