Wildfires are some of the most dangerous effects humanity is facing right now due to climate change. Every year, there are hundreds and thousands of wildfires reported across the world, causing huge economical losses. Resulting in an overall economic imbalance in world markets and trading.
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Understanding Wildfires: Analyzing the geographical information of past wildfires to identify high-risk areas vulnerable to these disasters.
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Optimizing Response: Providing recommendations to improve strategies for the location and timely response of firefighting departments.
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Predictive Insights: Predicting the causes and risks of wildfires to plan preventive measures for the future.
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Public Awareness: Increasing public understanding of wildfires to prevent future losses and save billions of dollars.
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Costly Disasters: Wildfires and firefighting efforts incur significant costs, with an average of over 75,000 wildfires in the US each year.
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Economic Impact: In 2018, wildfires in California resulted in a staggering $400 billion in economic losses, with the costliest wildfires alone causing $16.5 billion in damages.
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Human vs. Nature: Most wildfires in the US are caused by human activities, while the most destructive ones are often of natural origin.
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Interactive Data Visualization: Creating interactive maps to visualize historical fire data.
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Statistical Analysis: Employing statistical analysis to reveal patterns, trends, and correlations in wildfire occurrences.
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Machine Learning Models: Developing machine learning models, such as Random Forest and Gaussian Classifier, to predict the causes and risks of wildfires.
The dataset is from USDA Forest Services covering the years 1992-2015.
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Data Collection and Data Cleaning: Using NOAA API, wildfire dataset from US forecast service.
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EDA and Data Visualization: Utilizing SciPy, Scikit-Learn, Matplotlib, Plotly, Folium, Seaborn.
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Machine Learning Models: Implementing Random Forest, creating Gaussian Classifier.