I have a dataset with 15 different meteorological stations (providing T, rh, wind direction through time).

How should I implement them in a machine learning model? As independent inputs or can I combine them?

If you could provide me with some references or hints to start this project, that would very helpful !

I have so far cleaned the data and separate each meteorological station. I believe that I should try to perform a single prediction on each station and then combine the prediction of each station together ?

CodePudding user response：

There are different ways to implement multiple meteorological stations in a machine learning model depending on the specific problem you are trying to solve and the characteristics of the data. Here are a few options to consider:

1. Independent models: One option is to train a separate model for each meteorological station, using the data for that station as input. This approach is useful if the stations have different characteristics or if you want to make predictions for each station independently.

2. Combined model: Another option is to combine the data from all stations and train a single model to make predictions for all of them at once. This approach is useful if the stations are similar and the relationship between the input variables and the output variable is the same across all stations.

3. Multi-task learning: You can also consider using multi-task learning, where you train a single model to perform multiple tasks, one for each meteorological station. This approach is useful if the stations are similar but have different characteristics and you want to make predictions for all of them at once.

Regarding how to combine the predictions, it depends on the problem you are trying to solve. If you want to make a prediction for each station independently you don't need to combine the predictions. But if you want to make a prediction for all the stations you can use an ensemble method like a majority vote or a weighted average to combine the predictions.

You can find more information about these approaches and examples of their implementation in papers and tutorials about multi-task learning, multi-output regression and ensemble methods.

Also, it might be helpful to explore the correlation between the meteorological stations. You can use the correlation matrix and heatmap to explore the correlation between the different meteorological stations. If they are highly correlated you can combine them in a single model, otherwise, you can consider them as independent inputs.