BigData_Weather_Forecast

Weather Forecasting Project for Big Data Course IE212, UIT

This project uses Spark, Hadoop, and Kafka to input weather data into two trained models (Random Forest and Logistic Regression) to predict weather conditions (rain or no rain) and rainfall in real-time. The predictions are then displayed on a web interface.

Setup

- Python 3.10
- Java 11
- kafka_2.12-3.8.0
- Hadoop 3.3.5
- Jupyter Notebook
- MongoDB

During the setup and execution of the project, you might encounter compatibility issues between PySpark, Hadoop, and Java. Versions may need to be adjusted accordingly for them to work together.

NOTE:

• To use hadoop in Windows, you only need winutils.exe and hadoop.dll, no need to install the whole hadoop. I have put it in dependency/hadoop, you can refer to it.
• With Jupyter Notebook, you can install the Jupyter extension in Visual Studio Code and use it directly on VS code.

Usage

The project has been developed and tested on Windows. I have not tested it on other operating systems.

1. Export model

Run training-weather-forcast.ipynb and rainfall-prediction.ipynb in machine_learning/notebooks to export models (I ran them in Kaggle).

Paste the exported models from training-weather-forcast.ipynb to webapp/app/models/weather and from rainfall-prediction.ipynb to webapp/app/models/amount_of_rain for overriding the model here.

Change the paths (weather_model_path and rain_model_path) to the models you want to use in web/app/mykafka.

NOTE: If you do not export new models but use existing models in webapp/app/models, you may receive errors related to these models (checksum, etc).

2. Create a .env file and Prepare Mongodb

Copy the contents from .env.example and paste them into a .env file in the webapp directory (create the file if it doesn't exist).

Edit the values in the .env file to suit your environment.

In MongoDB, create database with name is DB_NAME in .env and two collection data and predict.

3. Run Kafka

In the kafka directory, run the following commands in two separate terminals to start the Zookeeper and Kafka server:

bin\windows\zookeeper-server-start.bat config\zookeeper.properties
bin\windows\kafka-server-start.bat config\server.properties

4. Run Kafka Producer

Install the necessary libraries by running the following command:

pip install -r .\webapp\requirements.txt

Then, in the machine_learning/notebooks directory, run the Weather_producer.ipynb file using Jupyter Notebook (or Visual Studio Code if you have installed the Jupyter extension).

Adjust the file paths as needed.

Note: The topic_name in this file must match the KAFKA_TOPIC_NAME in the .env file.

5. Run the Web App

Execute the following commands to run the web app:

# Navigate to the webapp directory
cd .\webapp

# If the virtual environment is not yet created, run:
python -m venv venv

# Activate the virtual environment
## On Windows:
venv\Scripts\activate

## On macOS/Linux:
venv/bin/activate

# Install the required libraries. If any libraries are missing, manually install them.
pip install -r requirements.txt

# If you've installed additional libraries, save the dependencies to requirements.txt:
pip freeze > requirements.txt

# To run the project, execute:
python app.py

# For future runs, use the following commands:
## Window
cd .\webapp ; venv\Scripts\activate ; python app.py

## Linux
cd .\webapp && venv\Scripts\activate && python app.py

# Note: Kafka and the Producer must be running before the web app is started.

Model

Data

The weather data is sourced from https://www.worldweatheronline.com/

Machine Learning Models and Components

Run the weather-forcast.ipynb and rainfall-prediction.ipynb notebook located in machine_learning/notebooks to train and export the models.

weather-forcast.ipynb will produce two models for weather forecasting: logistic_regression_model and random_forest_model.

rainfall-prediction.ipynb will also produce two models for predicting rainfall: logistic_regression_model and random_forest_model.

Retraining and Updating the Model

Whenever you need to update the model, rerun the notebook and export the new model.

Place the exported files in the appropriate directories (e.g., webapp/app/models), update the model path in consumer.py, and then restart the web app.

Model Training Diagram

System Architecture

Web Interface