Wildfires pose a grave threat to the global ecosystem. The length of the global wildfire season increased by 19%, and severe wildfires plagued nations worldwide. Every year, forest fires release vast quantities of carbon dioxide into the atmosphere, thereby contributing to climate change. A system that prevents, detects, and extinguishes wildfires is required.
The AI-based Wildfire Prevention, Detection and Suppression System is an innovative AI-based solution that effectively detects hotspots and wildfires and deploys fire retardant-spraying drones to prevent and suppress wildfires. This includes four steps.
- Load real-time satellite and meteorological data from NASA and NOAA pertaining to vegetation, temperature, precipitation,wind, soil moisture, and land cover for the purpose of prevention.
- Load real-time Land Cover, Humidity, Temperature, Vegetation, Burned Area Index, Ozone, and CO2 data for detection.
- The AI model can be trained using a labelled dataset of hotspots/wildfires and non-hotspots/non-wildfires Identification:
- Run real-time data through the model to identify hotspots and wildfires automatically.
- Drone Deployment: The drone can fly to locations of hotspots and wildfires.
This will mitigate the effects of climate change, safeguard ecosystems and biodiversity, prevent enormous economic losses, and save lives.
How to get the Data?
You can access real-time satellite and meteorological data pertaining to vegetation, temperature, precipitation, wind, soil moisture, and land cover from NASA and NOAA through their online portals:
NASA Earthdata: The NASA Earthdata website provides access to a variety of satellite data and products, including those related to vegetation, temperature, precipitation, wind, soil moisture, and land cover. You can search and download data from a variety of sensors and platforms, including MODIS, VIIRS, and Landsat.
NOAA National Centers for Environmental Information (NCEI): The NCEI website provides access to a wide range of meteorological and climatological data, including those related to temperature, precipitation, and wind. You can search and download data from a variety of sources, including the National Weather Service (NWS), National Climatic Data Center (NCDC), and Global Historical Climatology Network (GHCN).
NOAA National Operational Hydrologic Remote Sensing Center (NOHRSC): The NOHRSC website provides access to snow and soil moisture data, including snow cover maps and soil moisture estimates.
Global Soil Moisture Data Portal: This portal provides access to soil moisture data from a variety of sources, including NASA and ESA missions, and national and regional networks.
In addition to these online portals, NASA and NOAA also provide application programming interfaces (APIs) and web services that allow you to access and download data programmatically. You can find more information about these resources on their respective websites.
What are the next steps?
- Identify the data sources: There are various sources available to access real-time data related to Land Cover, Humidity, Temperature, Vegetation, Burned Area Index, Ozone, and CO2. Some of the popular sources include NASA, ESA, NOAA, USGS, and other agencies. Identify the most reliable sources based on your research needs and data quality requirements.
- Access data through APIs: Many of these agencies and organizations offer APIs and web services to access real-time data. You can use programming languages like Python, R, or MATLAB to load and manipulate the data. For example, NASA’s Application for Extracting and Exploring Analysis Ready Samples (AρρEEARS) allows you to extract and download data in various formats.
- Use data visualization tools: Once you have loaded the data, you can use various visualization tools to display the data in an understandable format. Tools like Google Earth Engine, ArcGIS, QGIS, and Matplotlib can be used to visualize the data.
- Clean and preprocess data: Real-time data can be messy and may require cleaning and preprocessing before analysis. You can use tools like Pandas, NumPy, and SciPy to clean and preprocess the data. For example, you can remove null values, interpolate missing values, and normalize the data.
- Analyze data: After cleaning and preprocessing, you can analyze the data using various statistical and machine learning techniques. For example, you can use regression analysis to find the correlation between temperature and CO2 levels, or use clustering to group regions based on their vegetation cover.
- Interpret and report results: Finally, you can interpret the results and report your findings. You can use tools like LaTeX, Word, or Google Docs to write your report and create visualizations to support your findings.
How to make an AI model that can be trained using a labeled dataset of hotspots/wildfires and non-hotspots/non-wildfires identification?
- Gather and label the dataset: Collect a dataset of satellite images that contain both hotspots/wildfires and non-hotspots/non-wildfires. Label each image with the corresponding class (i.e., hotspot or non-hotspot). You can use resources like the NASA fire map or the MODIS active fire detection dataset to collect satellite images.
- Preprocess the images: Preprocess the images by resizing them to a fixed size and normalizing the pixel values. This helps the CNN model learn features in a consistent and uniform way.
- Split the dataset: Split the dataset into training, validation, and testing sets. The training set is used to train the CNN model, while the validation set is used to tune the hyperparameters and prevent overfitting. The testing set is used to evaluate the performance of the trained model.
- Build the CNN model: Build a CNN model with several convolutional and pooling layers to learn the features from the input images. Use activation functions like ReLU and softmax to introduce nonlinearity and to output the class probabilities.
- Train the model: Train the CNN model using the labeled dataset. Use an optimization algorithm like Adam or stochastic gradient descent (SGD) to minimize the loss between the predicted and actual classes.
- Evaluate the model: Evaluate the performance of the trained CNN model on the testing set. Use metrics like accuracy, precision, recall, and F1 score to measure the performance.
- Use the model: Once the CNN model is trained and evaluated, it can be used to classify new satellite images as hotspots/wildfires or non-hotspots/non-wildfires.
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
# Build the CNN model
model = Sequential()
# Add convolutional layer with 32 filters, 3×3 kernel size, and ReLU activation function
model.add(Conv2D(32, (3, 3), activation=‘relu’, input_shape=(64, 64, 3)))
# Add max pooling layer with 2×2 pool size
model.add(MaxPooling2D(pool_size=(2, 2)))
# Add convolutional layer with 64 filters, 3×3 kernel size, and ReLU activation function
model.add(Conv2D(64, (3, 3), activation=‘relu’))
# Add max pooling layer with 2×2 pool size
model.add(MaxPooling2D(pool_size=(2, 2)))
# Add flattening layer to flatten the output of the previous layer
model.add(Flatten())
# Add fully connected layer with 128 nodes and ReLU activation function
model.add(Dense(128, activation=‘relu’))
# Add output layer with sigmoid activation function for binary classification
model.add(Dense(1, activation=‘sigmoid’))
# Compile the model
model.compile(optimizer=‘adam’, loss=‘binary_crossentropy’, metrics=[‘accuracy’])
# Train the model on the labeled dataset
model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_val, y_val))
# Evaluate the model on the testing set
score = model.evaluate(x_test, y_test, verbose=0)
print(“Test loss:”, score[0])
print(“Test accuracy:”, score[1])
