Deforestation & Temperature: Mau Forest's Changing Climate

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  Mau Forest

Inspiration

The alarming rise in global temperatures and severe heat waves, driven by human activities such as deforestation, inspired us to take action. We are currently seeing the effects through drought, food insecurity, famine etc. Our Green AI platform uses machine learning for global reforestation, identifying optimal tree planting locations and providing real-time monitoring. Key features include data-driven decision making, predictive analysis for tree growth, and user-friendly web and mobile applications. We chose Kenya's Mau Forest due to its critical ecological role and recent deforestation issues. Our aim is to uncover the impact of deforestation on local temperature changes and contribute to global climate change understanding.

What it does

This project analyzes the relationship between deforestation and temperature changes in the Mau Forest. By examining comprehensive data, it reveals how tree loss fuels temperature spikes and heat waves, providing crucial insights for climate action.

How we built it

We collected data through web scrapping (javascript), and APIs. Then using Python we analyzed temperature and deforestation data from various sources. Using statistical methods and machine learning models like Random Forest Regression, we quantified the impact of tree loss on temperature changes. Visualizations and predictive models were created to illustrate our findings.

Challenges we ran into

1. Obtaining sufficient and reliable temperature and deforestation data was a major challenge. Recording datasets in Kenya, particularly historical data, are often incomplete or inconsistent.
2. Variability in data collection methods and lack of standardized recording practices can affect the accuracy and reliability of the datasets.
3. Access restrictions on certain data platforms posed a significant challenge.

Accomplishments that we're proud of

1. Successfully demonstrating the significant relationship between deforestation and temperature changes in Mau Forest.
2. Highlighted the potential of our Green AI platform, which increases forest cover, enhances carbon sequestration, preserves biodiversity, creates jobs, and improves livelihoods for local communities.
3. Developing accurate predictive models to forecast temperature changes based on deforestation data.
4. Providing actionable insights that can inform environmental policies and conservation strategies.

What we learned

1. The critical impact of local deforestation on temperature variations and broader climate change patterns.
2. The importance of high-quality, accessible data for conducting robust environmental research.
3. Advanced statistical and machine learning techniques to analyze complex environmental data.

What's next for Deforestation & Temperature: Mau Forest's Changing Climate

1. Extended Analysis: Expand the study to cover more years for a comprehensive long-term analysis.
2. Incorporate Additional Variables: Include factors like precipitation and land use changes to enhance model accuracy.

Built With

• api
• excel
• html
• javascript
• machine-learning
• python