Ionosphere-TEC-regression-with-pytorch

Predict extended ionosphere total electron content with variable data

Motivation

This is an unofficial implementation of paper

Extending Ionospheric Correction Coverage Area By Using A Neural Network Method

Ionosphere's total electron content make confuse on wireless comunication especially GPS system

so, It's important to predict ionosphere's total electron content

Framework used

Data Processing

Using variable 25 type of data, and they have diffent time scale.

So, U can get two versions of data that one is preprocessed data for same time scale(1), the other is original data(2)

1. Processed data is already interpolated into same time scale. All you have to do is modifying the regression model architecture

2. If you want to practice preprocessing data, select original data. Each data has it's own time scale. So, you have to preprocess the data.

Example code

I built basic regression model, so you can easily modify the architecture

class Regressor(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = nn.Sequential(
            nn.Linear(25, 64, bias=False),
            nn.BatchNorm1d(64, eps=1e-05, momentum=0.1),
            nn.ReLU()
        )
        
        self.layer2 = nn.Sequential(
            nn.Linear(64, 128, bias=False),
            nn.BatchNorm1d(128, eps=1e-05, momentum=0.1),
            nn.ReLU()
        )
        
        self.layer3 = nn.Sequential(
            nn.Linear(128, 256, bias=False),
            nn.BatchNorm1d(256, eps=1e-05, momentum=0.1),
            nn.ReLU()
        )
        
        self.layer4 = nn.Linear(256, 1, bias=False)
        
    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
      
        return x
        

Files description

• Original_data : data before preprocess
• Preprocessed_data : data files already preprocessed
• Ionosphere_regresstion.ipynb : Baseline code to construct regression architecture
• Improved_Ionosphere_regression.ipynb : Modified architecture with my own opininon
• sample_submission.csv : Excel file to save your prediction
• submic.csv : Our final output in original scale

How to use?

1) If you choose preprocessed data

You can do everyting in Ionosphere_regression.ipynb Important thing is changing model architecture Just set data path at start and make your own deep learning model architecture

2) If you choose original data

You have to preprocess the data first, especally you can use interpolation. After this all same with case 1)