/Global_Sea_Level_Change

Analysis and Forecast of Global Mean Sea Level Change due to Global Warming

Primary LanguageR

Global Mean Sea Level Change Due to Global Warming

Time Series Analysis and Forecasting

Key Concepts

  • Stationarity of a Time Series Data Set
  • Decomposition of Raw Data Set Using Ratio to Moving Average Method
  • Mathematical Curves
  • ARIMA Modeling
  • Ljung Box Test
  • Forecasting

Model Workflow

  1. Stationarity Testing:
  • Visualizing Raw Data & 1st Differences
  • KPSS Test with p-value 0.01 implying rejection of H0 and non-stationarity in data
  1. Data Decomposition:

  • Used Additive Model

  • Decomposed Components: Trend, Seasonality, Residual Series with Random/White Noise

  1. Individual Component Fitting:
  • Trend: Fitted Cubic Curve (highest adjusted R^2 of 0.991 & least MSE)
  • Seasonality: ACF and PACF Plots
  • Random Noise: ARIMA Modelling, Ljung–Box test for goodness of fit

Forecasting

Results and Inference

  • The best Fit is AR(1) to forecast seasonality due to geometric decay in the ACF plot and sharp cut-off in the PACF Plot.
  • Out of an overall YoY rise of 3.2 ± 0.5 mm, 1.8 ± 0.41 mm is due to Climate Change (~43%).
  • The overall rise of 102.5 mm (approx. 4 inches) seen since 1993; the forecasted increase of approx. 109.6 mm till 2025 (i.e., ~ 6.9% rise in change in 4 years).

Future Scope

  • Studying regional impact analysis with the inclusion of isostatic causes.
  • Extending the study to support the formulation of natural calamity action plans and better the existing understanding of their occurrences

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