pages/2_Technology Behind It.py

import streamlit as st
from utils.levels import complete_level, render_page, initialize_level

initialize_level()

LEVEL = 2

def step2_page():
    st.header("Technology Behind It")

    st.image(
        "https://miro.medium.com/v2/resize:fit:720/format:webp/1*mRpdRbCta8VWlTrx12WNOQ.jpeg",
        use_column_width=True,
    )

    st.markdown(
    """
    ### How machine learning is used for accurate weather forecasting?
    Traditional numerical weather prediction models have been the cornerstone of meteorology for decades, relying on 
    complex physical equations and extensive observational data. 
    
    However, the rise of machine learning has brought a paradigm shift to weather forecasting, unlocking new possibilities and enhanced accuracy in predicting atmospheric phenomena. 
    Machine learning techniques leverage the power of algorithms to identify patterns and relationships within vast and complex weather datasets. 
    By learning from historical weather data, machine learning models can capture hidden patterns and non-linear relationships that might be 
    challenging to represent using traditional methods. This empowers meteorologists and researchers to create highly sophisticated and 
    precise forecasting models that can make predictions for various temporal scales, from short-term weather conditions to long-term climate trends.

    Here's how it works:
    1. **Data Collection**: The first step is to collect large amounts of historical weather data, which includes various atmospheric parameters like temperature, humidity, wind speed, pressure, solar irradiance, and more. This data is typically obtained from weather stations, satellites, radars, and other sources.

    2. **Data Preprocessing**: The collected data needs to be preprocessed to handle missing values, outliers, and inconsistencies. It may involve data cleaning, imputation, and scaling to ensure that the data is suitable for training machine learning models.
    
    3. **Feature Engineering:**: In weather forecasting, relevant features play a crucial role in accurate predictions. Engineers and meteorologists work together to extract meaningful features from the raw data, which could include time-based features (e.g., day of the week, month), lagged values (to capture temporal dependencies), geographic features (latitude, longitude), and more.
    
    4. **Data Splitting**: The preprocessed data is split into training and testing sets. The training set is used to train the machine learning model, and the testing set is used to evaluate its performance.
    
    5. **Model Selection**:  Different machine learning algorithms can be used for weather forecasting, such as regression models (e.g., linear regression), time series models (e.g., ARIMA, SARIMA), and more advanced models like decision trees, random forests, gradient boosting, and neural networks. The choice of model depends on the characteristics of the data and the forecasting task.
    
    6. **Model Training**: The selected machine learning model is trained on the training data. During training, the model learns from the historical data and tries to capture patterns and relationships between the input features (e.g., temperature, humidity) and the target variable (e.g., future temperature).
    
    7. **Model Evaluation**: After training, the model's performance is evaluated using the testing data. Common evaluation metrics for weather forecasting include mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and correlation coefficients.
    
    8. **Forecasting**: Once the model is trained and evaluated, it can be used to make predictions for future weather conditions based on new input data. For example, given current weather conditions and historical data, the model can forecast the weather for the next few hours or days.
    
    9. **Updating and Re-training**: Weather data is constantly changing, so the forecasting model needs to be updated regularly with new data. Periodic re-training of the model ensures that it stays up-to-date and continues to make accurate predictions.
    
    """
    )

    if st.button("Complete"):
        complete_level(LEVEL)

render_page(step2_page, LEVEL)