# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 1 Introduction
Therefore, we need to find the best available model publicly, which would be the best approximation for a specific micro-location. Validation from a real weather station improves the prediction of the weather models that are too general because it can consider the micro-climatic conditions for the specific micro-location in the prediction. In the case of predicting air temperature at a micro-location, the challenge is even greater, as the terrain can be very rugged and the air temperature can be completely different just a few meters down from our weather station location. Also, a sudden shower or thunderstorm can cool the atmosphere very much above the measurement site, while, a few kilometers away it is completely dry, and there is no sudden change in air temperature. When monitoring sudden weather events with dynamic and turbulent changes of air masses, it is difficult to predict the air temperature, especially, in the north-eastern part of Slovenia due to the arrival of south-westerly winds in anticipation of a change in the weather situation, mostly before the weather worsens, which warms the atmosphere very quickly. However, the areas with less winds can have temperatures more than 10 degrees Celsius lower than windward areas. The differences can be approximately a few hundred meters, and predicting such situations also represents a challenge. In general, weather forecasting is influenced by a huge number of factors. To add to this, the butterfly effect and a small deviation in the initial forecast conditions can critically change the whole weather situation. Therefore, one of the major challenges is to find such parameters as inputs to the forecast model that are capable of both (1) taking the quality of the measurements from the weather station into account and (2) reducing the error of the forecasting model based on high-resolution datasets [10]. As a matter of fact, we tried to solve the air temperature prediction problem by analyzing different weather situations and observing how well the learned air temperature prediction models reacted to each weather situation. It is assumed that some of these learned prediction models will react better in, e.g., sunny weather, while others will react better in, e.g., a rapid cooling in the middle of the day. This could mean that the classifiers could be used for determining which prediction model could be used for a particular weather situation, but the model selection will be toward the most optimal model that will be the best at identifying general weather patterns for a given situation. Moreover, the metrics with which we evaluate our model must provide some indication of the performance of the learned model in predicting a sequence of air temperature values for the next few hours, but this does not mean that it is capable of predicting all weather situations so well. As a result, we really need to decide very carefully about which learned prediction model to use. Our proposed prediction model, called Maximus, has the ability to remember short- and long-term details. Therefore, this is ideal for solutions using Machine Learning (ML) methods that are capable of dealing with time-series data, in our case, Long Short-Term Memory (LSTM). Although more suitable state-of-the-art combinations of either convolution neural network (CNNs) [11] and LSTM or models with attention mechanisms also exist, the goal or our study was to show that the prediction of air temperature at the micro-location can be improved by using the optimal LSTM model. The authors of the paper are aware that the prediction could also be improved by using an advanced prediction method, but this subject is left for future work. The advantage of using meteorological knowledge is in choosing those features that contribute the most to the prediction without much further calculation of different combinations of hyper-parameter settings controlling the model's architecture. The Maximus prediction method was tested for predicting the air temperature at a given Slovenian micro-location.