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A collaborative project built to experiment with data analytics techniques contributing to prediction, forecasting and visualization for different use cases of climate change.

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Climate-Change-Analysis

A collaborative project built to experiment with data analytics techniques for prediction, time-series forecasting and visualization of various use cases of climate change.

Description

It's important that we understand how the climate is changing, so that we can prepare for the future. Studying the climate helps us predict how much rain the next winter might bring, or how far sea levels will rise due to warmer sea temperatures. After the horrors that happened this year in Australia, I decided to take a look at the global temperatures and how exactly they are evolving and have been evolving during the years. The analysis needed to underpin climate change decisions is like putting together the pieces of a jigsaw. I needed some observations of weather, climate, water resources, agriculture and other sectors. I also needed to analyze the links between these and human and ecosystem development.

Literature of concept

Land Surface Temperature is an important variable within the Earth’s climate system. It describes processes such as the exchange of energy and water between the land surface and atmosphere, and influences the rate and timing of plant growth. Land surface temperature is how hot the “surface” of the Earth would feel to the touch in a particular location. Scientists monitor land surface temperature because the warmth rising off Earth’s landscapes influences (and is influenced by) our world’s weather and climate patterns. Commercial farmers may also use land surface temperature maps like these to evaluate water requirements for their crops during the summer, when they are prone to heat stress.

Machine Learning Techniques used

  1. Linear Regression for predicting temperature.
  2. Time Series Forecasting for increase in temperature across different countries.
  3. Various different visualizations and plots for drawing insights.

Datasets used

  1. Continents dataset (Country code, alpha values, region).
  2. Global temperatures dataset (By Country, By City, By State).

Observations

Human-induced climate change has contributed to changing patterns of extreme weather across the globe, from longer and hotter heat waves to heavier rains. From a broad perspective, all weather events are now connected to climate change. While natural variability continues to play a key role in extreme weather, climate change has shifted the odds and changed the natural limits, making certain types of extreme weather more frequent and more intense. The analysis shows that the rise in average world land temperature globe is approximately 1.5 degrees C in the past 250 years, and about 0.9 degrees in the past 50 years. While our understanding of how climate change affects extreme weather and rise in land temperature is still developing, evidence suggests that extreme weather may be affected even more than anticipated. Extreme weather is on the rise, and the indications are that it will continue to increase, in both predictable and unpredictable ways.

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A collaborative project built to experiment with data analytics techniques contributing to prediction, forecasting and visualization for different use cases of climate change.

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