# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 4 Experiments and Results
### Discussion  
The metrics we have used are important for evaluating and comparing forecast models, but they are far from indicative of the actual forecast error. One of the major problems is that we use historical meteorological station data as an input to the forecast model, and, at the same time, future reference model data, which can propagate the reference model error. The choice of parameter combination and time frame are very important here. If we choose the air temperature of the reference model as input to the forecast model, and we know that we are forecasting the air temperature, if the reference model has a large error, this will be reflected in our final result. Therefore, we need to choose ICON-D2 parameters that will not affect our final predictor variable directly. However, it is worth noting that this model is only trained to forecast at a given micro-location, whereas ICON-D2 covers data for the whole of Slovenia and its surroundings. Another problem with ICON-D2 weather model is that although this is able to predict the air temperature on hourly basis, it is refreshed every 3 h, whereas we need to update our prediction model every hour. At the same time, we want to keep calibrating the forecast already produced by the ICON-D2 model. This can make it easier to detect a sudden drop or rise in air temperature that may not be detected by the model. Such forecasts come in very handy when forecasting an increasing number of extreme weather events. Ideally, data from satellite images should be retrieved every 15 min and fed into the forecast model. Also, the use of a radar image of rainfall intensity with an interval of 5 min could help to train the model even more optimally, and to compare the forecast of rainfall intensity with the ICON-D2 weather model. The use of a solar irradiance sensor, which is not present at the measuring station, could also make a decisive contribution to a better forecast. Also, a webcam that could predict what the weather is like using DL, and could help to obtain a better overview of the weather conditions. Let us emphasize the time complexity of the proposed prediction method, Maximus. Although we are not focusing just on this component, it can be estimated that each learning phase in searching for the prediction model lasts approximately about 3 h. On the other hand, the proposed meteorological station is a mobile and independent of an external source of energy. This means that it is also flexible because the observed micro-locations  
Figure 9: Air temperature forecast. could be changed easily by moving the location of the station. However, the prediction model needs to be updated properly by the moving. The following seven advances of the proposed prediction method Maximus can be revealed:  
* Use of Mobile Weather Station Data: The model integrates real-time, high-resolution data from a professional mobile weather station, enabling localized and frequent updates every hour. * Development of an LSTM-Based Prediction Model: A novel Long Short-Term Memory (LSTM) neural network was designed and optimized specifically for micro-location temperature prediction, outperforming general models in several cases. * Use of a Lower-Resolution Long-Term Dataset (CERRA): Leveraging the CERRA dataset allows for incorporating long-term historical patterns, enhancing the robustness of predictions. * Comparison with ICON-D2: The proposed model was rigorously tested against the state-of-the-art ICON-D2 reference model, demonstrating competitive or superior results in several forecast windows. * Hyper-parameter Tuning and Generational Testing: Comprehensive experimental design included different generations of models, topologies, and hyper-parameter settings to identify optimal configurations. * Flexibility and Portability of the Weather Station: The weather station setup is mobile and self-sustaining, which allows relocation and re-calibration for other micro-locations without major reconfiguration.