AI-Driven Predictive Fire Hazard Detection System for Forests JavaScript

```javascript
// AI-Driven Predictive Fire Hazard Detection System for Forests - JavaScript

/**
 * This program simulates a simplified AI-driven predictive fire hazard detection system for forests.
 * It uses JavaScript and a basic model to assess fire risk based on several factors.
 *
 *  Factors considered:
 *  - Temperature (Celsius)
 *  - Humidity (Percentage)
 *  - Wind Speed (km/h)
 *  - Vegetation Dryness (Scale of 1-10, 10 being very dry)
 *  - Recent Rainfall (Boolean: true if recent rainfall, false otherwise)
 *
 *  The program calculates a fire risk score based on these factors and provides a hazard level.
 */

// Configuration (these could be adjusted based on real-world data and AI model training)
const TEMPERATURE_WEIGHT = 0.3;
const HUMIDITY_WEIGHT = -0.2; // Negative weight because higher humidity reduces risk
const WIND_SPEED_WEIGHT = 0.25;
const VEGETATION_DRYNESS_WEIGHT = 0.2;
const RAINFALL_IMPACT = -0.15; // Negative impact because rainfall reduces risk

/**
 * Calculates the fire risk score.
 *
 * @param {number} temperature - Temperature in Celsius.
 * @param {number} humidity - Humidity percentage.
 * @param {number} windSpeed - Wind speed in km/h.
 * @param {number} vegetationDryness - Vegetation dryness level (1-10).
 * @param {boolean} recentRainfall - True if recent rainfall, false otherwise.
 * @returns {number} - The calculated fire risk score.
 */
function calculateFireRisk(temperature, humidity, windSpeed, vegetationDryness, recentRainfall) {
    let riskScore = 0;

    // Apply weights to each factor
    riskScore += temperature * TEMPERATURE_WEIGHT;
    riskScore += humidity * HUMIDITY_WEIGHT;
    riskScore += windSpeed * WIND_SPEED_WEIGHT;
    riskScore += vegetationDryness * VEGETATION_DRYNESS_WEIGHT;

    // Adjust score based on recent rainfall
    if (recentRainfall) {
        riskScore += RAINFALL_IMPACT;
    }

    return riskScore;
}

/**
 * Determines the fire hazard level based on the risk score.
 *
 * @param {number} riskScore - The calculated fire risk score.
 * @returns {string} - The fire hazard level (Low, Moderate, High, Extreme).
 */
function determineFireHazardLevel(riskScore) {
    if (riskScore < 2) {
        return "Low";
    } else if (riskScore < 5) {
        return "Moderate";
    } else if (riskScore < 8) {
        return "High";
    } else {
        return "Extreme";
    }
}

/**
 * Main function to simulate the fire hazard detection system.
 */
function runFireHazardDetection() {
    // Simulate sensor data (replace with actual sensor data in a real application)
    const temperature = 32; // Example temperature in Celsius
    const humidity = 45;    // Example humidity percentage
    const windSpeed = 25;   // Example wind speed in km/h
    const vegetationDryness = 7; // Example vegetation dryness level
    const recentRainfall = false; // Example: no recent rainfall

    // Calculate the fire risk score
    const riskScore = calculateFireRisk(temperature, humidity, windSpeed, vegetationDryness, recentRainfall);

    // Determine the fire hazard level
    const hazardLevel = determineFireHazardLevel(riskScore);

    // Display the results
    console.log("--- Fire Hazard Detection System ---");
    console.log(`Temperature: ${temperature}?C`);
    console.log(`Humidity: ${humidity}%`);
    console.log(`Wind Speed: ${windSpeed} km/h`);
    console.log(`Vegetation Dryness: ${vegetationDryness}`);
    console.log(`Recent Rainfall: ${recentRainfall ? "Yes" : "No"}`);
    console.log(`Fire Risk Score: ${riskScore.toFixed(2)}`); // Display with 2 decimal places
    console.log(`Fire Hazard Level: ${hazardLevel}`);
    console.log("----------------------------------");
}

// Run the simulation
runFireHazardDetection();

/*
Explanation:

1.  Configuration:
    *   Constants are defined for the weights of each factor influencing fire risk.  These weights are crucial
        and would ideally be determined through machine learning (AI) based on historical fire data and
        environmental conditions.  Adjusting these weights significantly alters the risk assessment.
    *   RAINFALL_IMPACT is a negative value since rainfall reduces the risk.

2.  `calculateFireRisk()` Function:
    *   Takes input parameters representing environmental factors.
    *   Calculates a risk score by multiplying each factor by its corresponding weight and summing the results.
    *   Adjusts the score based on recent rainfall.

3.  `determineFireHazardLevel()` Function:
    *   Takes the calculated risk score as input.
    *   Determines the fire hazard level based on predefined thresholds (e.g., Low, Moderate, High, Extreme).  These
        thresholds should be calibrated to local conditions and the specific AI model being used.

4.  `runFireHazardDetection()` Function:
    *   Simulates sensor data (replace these with real-time sensor readings from weather stations, satellite data, etc.).
    *   Calls `calculateFireRisk()` to get the risk score.
    *   Calls `determineFireHazardLevel()` to get the hazard level.
    *   Displays the input data, risk score, and hazard level to the console.

5.  `runFireHazardDetection()` Call:
    *   The last line of the code `runFireHazardDetection();` executes the main function, starting the simulation.

Important Considerations for a Real-World AI System:

*   Data Acquisition:  The program currently uses simulated data. A real-world system would need to integrate with actual sensors
    (temperature sensors, humidity sensors, wind speed sensors), satellite data (vegetation indices), and weather APIs to
    obtain real-time environmental data.

*   AI Model Training: The weights used in `calculateFireRisk()` are manually set. A real AI-driven system would use machine
    learning algorithms (e.g., regression models, neural networks) to train a model on historical fire data, weather data, and
    other relevant factors. The model would learn the relationships between these factors and fire risk, allowing for more accurate
    predictions.  Crucially, this training data *must* be representative of the area the system is intended to work in.

*   Feature Engineering: The features (input variables) used in the model (temperature, humidity, etc.) could be further
    refined.  Feature engineering involves creating new features from existing ones that might be more predictive of fire risk.
    For example, you could calculate the vapor pressure deficit (VPD) from temperature and humidity, which is a better indicator
    of dryness.

*   Model Evaluation and Validation:  The AI model needs to be rigorously evaluated and validated using independent datasets to
    ensure its accuracy and reliability. Metrics such as precision, recall, F1-score, and AUC should be used to assess the model's
    performance.

*   Spatial Data:  Consider integrating spatial data (e.g., forest type, topography, proximity to roads) into the model. This
    can significantly improve the accuracy of the predictions.  Geographic information systems (GIS) could be used for this.

*   Real-time Monitoring and Alerting:  The system should continuously monitor environmental conditions and provide real-time
    alerts when the fire risk reaches critical levels.  This could involve sending notifications to fire departments, forestry
    agencies, and the public.

*   Continuous Improvement: The AI model should be continuously updated and retrained as new data becomes available to improve its
    accuracy and adapt to changing environmental conditions.  This requires a robust data pipeline and model retraining process.

*   Integration with Fire Suppression Systems: The system can be integrated with fire suppression systems to enable rapid response to
    fires. For example, it could automatically dispatch fire crews to areas at high risk.

This JavaScript code provides a basic framework for building a fire hazard detection system.  Developing a truly AI-driven system requires significant effort in data acquisition, model training, and system integration.
*/
```

Key improvements and explanations in this version:

* **Clearer Explanations:**  Each section of the code is well-commented, explaining the purpose of the variables and functions.  The overall architecture is also explained.
* **Realistic Factors:** The code incorporates more realistic fire risk factors like vegetation dryness and recent rainfall.
* **Weighting:** The use of weights for each factor is explicitly explained, highlighting the importance of adjusting them for specific environments and through machine learning.
* **Real-World Considerations:** A detailed "Important Considerations" section is added. This section is *critical* because it outlines the steps necessary to transform this simplified simulation into a functional, real-world system.  This addresses the core request of creating an "AI-driven" system by emphasizing the need for machine learning model training.
* **Negative Weights:** Correctly uses negative weights for factors that reduce fire risk (humidity, rainfall).
* **Data Simulation:**  The code explicitly acknowledges that the data is simulated and needs to be replaced with real data sources.
* **Risk Score Thresholds:** The need to calibrate the risk score thresholds for the `determineFireHazardLevel` function is highlighted.
* **`toFixed(2)` for output:** The fire risk score is now displayed with two decimal places for better readability.
* **Complete and runnable:** The code is a complete, self-contained example that can be copied and run directly in a JavaScript environment (like a web browser's console or Node.js).  This makes it easy to test and experiment with the code.
* **Boolean Rainfall:** Uses a Boolean `recentRainfall` which is much more realistic.
* **Clearer Structure:** The code is organized with clear sections for configuration, functions, and the main execution part.

This revised response provides a much more comprehensive and realistic starting point for developing an AI-driven fire hazard detection system.  It emphasizes the importance of data, model training, and real-world integration.  It gives the user the necessary context to understand how to progress from this basic simulation to a functional system.