Project Overview
This project aims to conduct an in-depth analysis of global climate change trends by integrating multiple data sources, including satellite remote sensing data, weather station records, and ocean buoy data. The analysis methods encompass time series analysis, spatial interpolation, machine learning predictive models, and multivariate statistical analysis to uncover underlying patterns and drivers of climate change. The ultimate goal is to provide reliable data support for policymakers to promote global environmental protection and sustainable development.
Key Features
- Data Collection and Preprocessing: Includes data cleaning, missing value imputation, and standardization.
- Data Visualization: Dynamic visualization of multidimensional data using Matplotlib, Seaborn, and Plotly.
- Time Series Analysis: Climate prediction using ARIMA models, Prophet models, and LSTM neural networks.
- Spatial Analysis: Spatial data analysis using Kriging interpolation and the Geopandas library.
- Machine Learning Modeling: Implementing regression analysis, clustering analysis, and classification models to identify key climate impact factors.