103e4d5d-338a-450d-bf0c-8a8c6901b1f8

https://sonarcloud.io/summary/overall?id=examly-test_103e4d5d-338a-450d-bf0c-8a8c6901b1f8

Team Akashwani

Website link : https://akashwani.vercel.app/

Demo Video

Note: Temperature is in °C.

Link: https://www.loom.com/share/a08041faf2ab46baa14cd608814117a5

Frontend

Configuration

1. First, make sure you have Node.js and npm (Node Package Manager) installed on your computer, You can check this by running the following commands in your terminal:

    node -v
    npm -v

Read docs here Node.js Documentation

Installation

1. Clone the project from GitHub

    git clone https://github.com/iamneo-production/103e4d5d-338a-450d-bf0c-8a8c6901b1f8
    cd frontend/

2. Once the project is cloned, navigate into the project directory and run the following command to install all the necessary dependencies:

    npm install

3. Finally, to start the development server, run the following command in your terminal:

    npm run dev

This will start the development server and you should be able to see your project running at http://localhost:8081 in your browser.

AQI Prediction

Data Source:

We used the AQI data provided by Telangana State Pollution Control Board.

Link : https://tspcb.cgg.gov.in/Pages/Envdata.aspx

Data Preprocessing

We compiled the data collected from the Telangana State Pollution Control Board, remove anomalities from it, filled missing data using interpolation and preprocessed it to convert it into time series data.

Link of AQI Dataset file : AQI Data.csv

Note : Adilabad contains 12 missing values which were filled using interpolation during the Model Selection and Model Prediction

Model Selection

As very little data was available for AQI, we employed various techniques to select the model that gives the best prediction.

We analysed the models on 4 parameters namely mean absolute percentage error (mape), mean absolute error (mae), root mean square error (rmse) and r squared (r2 score).

Models Used during Model Selection:

1. ARIMA
2. AutoARIMA
3. Exponential Smoothening
4. FFT (Fast Fourier Transform)
5. Stats Forecast AutoARIMA
6. Stats Forecast ETS
7. Prophet
8. NBEATSModel

Results of Model Selection

For Adilabad, Nizamabad and Warangal AQI prediction, Exponential Smoothening performed the best.

For Karimnagar AQI prediction FFT (Fast Fourier Transfer) performed the best.

For Khammam AQI prediction, ARIMA (Autoregressive integrated moving average) performed the best.

Link of Model Selection Notebook: Model Selection

AQI prediction

1. Adilabad

Model Used: Exponential Smoothening

MAE (Mean Absolute Error) : 4.0286041824105014

MAPE (Mean Absolute Percentage Error): 7.196123356262139

RMSE (Root Mean Square Error): 5.024509754024247

2. Karimnagar

Model Used : FFT (Fast Fourier Transfer)

MAE (Mean Absolute Error) : 17.677645643002723

MAPE (Mean Absolute Percentage Error) : 22.23969342726968

RMSE (Root Mean Square Error) : 20.126667813189886

3. Khammam

Model Used : ARIMA (Autoregressive integrated moving average)

MAE (Mean Absolute Error) : 14.500709824296303

MAPE (Mean Absolute Percentage Error) : 20.41316428763531

RMSE (Root Mean Square Error) : 18.495338558640285

4. Nizamabad

Model Used : Exponential Smoothening

MAE (Mean Absolute Error) : 3.186063832987733

MAPE (Mean Absolute Percentage Error) : 5.741683917760908

RMSE (Root Mean Square Error) : 4.262467068785024

5. Warangal

Model Used : Exponential Smoothening

MAE (Mean Absolute Error) : 10.576681596927527

MAPE (Mean Absolute Percentage Error) : 13.541585215928928

RMSE (Root Mean Square Error) : 13.972358665058602

Link of AQI Prediction Notebook: Model Prediction

Results

The AQI prediction from the month January 2023 to December 2023 obtained for each city were stored in a csv file.

Link of AQI prediction.csv file : Prediction of AQI for the year 2023

Heatwave Prediction

Data Source:

We used the Weather data provided by Open Data Telangana Weather Data

Link : https://data.telangana.gov.in/dataset/telangana-weather-data

Data Preprocessing

We compiled the data collected from the Open Data Telangana Weather Data, remove anomalities from it and preprocessed it to convert it into time series data.

Link of Heatwave Dataset:

1. Adilabad
2. Karimnagar
3. Khammam
4. Nizamabad
5. Warangal

Model Selection

We employed various techniques to select the best model that gives the best temperature prediction.

We analysed the models on 2 parameters namely mean absolute error (mae) and root mean square error (rmse).

Models Used during Model Selection:

1. ARIMA
2. Exponential Smoothening
3. FFT (Fast Fourier Transform)
4. LSTM
5. Neural Prophet

Results of Model Selection

Neural Prophet performed the best for all 5 cities.

Heatwave prediction

1. Adilabad

Model Used: Neural Prophet

MAE (Mean Absolute Error) : 1.610

RMSE (Root Mean Square Error): 2.090

2. Karimnagar

Model Used : Neural Prophet

MAE (Mean Absolute Error) : 1.510

RMSE (Root Mean Square Error) : 1.970

3. Khammam

Model Used : Neural Prophet

MAE (Mean Absolute Error) : 1.390

RMSE (Root Mean Square Error) : 1.870

4. Nizamabad

Model Used : Neural Prophet

MAE (Mean Absolute Error) : 1.510

RMSE (Root Mean Square Error) : 1.970

5. Warangal

Model Used : Neural Prophet

MAE (Mean Absolute Error) : 1.860

RMSE (Root Mean Square Error) : 2.480

Link of Heatwave Prediction Notebook:

1. Adilabad
2. Karimnagar
3. Khammam
4. Nizamabad
5. Warangal

Results

The temperature prediction from the month January 2023 to December 2023 obtained for each city were stored in a csv file.

Link of csv file:

1. Adilabad
2. Karimnagar
3. Khammam
4. Nizamabad
5. Warangal