# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 2 Material and Methods
### Prediction Methods  
Many different ML and Deep Learning (DL) methods exist for weather prediction. In this study, we focus in particular on a DL method called LSTM [6], which is a type of recurrent neural network (RNN) [21].  
[PERSON] et al. [22] described the application of this method based on data acquired from IoT temperature sensors through the main thoroughfares of New York City, America, where the air temperature sensors were placed at a distance of about 150 m. The main point is a prediction of the hourly air temperature for the next day. The LSTM method learns over the data from all measurements and locations to obtain an accurate prediction.  
[PERSON] et al. [7] presented a combination of several LSTM-based layers that do not support backward data searching in the model learning process. Consequently, a new version of the LSTM model was implemented which supports both backward and forward data searching, and thus increased the accuracy of the model prediction.  
Combining CNN and LSTM gives a better combination and thus better predictions. CNN reduces the dimensionality of the time series data, while LSTM is able to capture the memory of long time periods in the air temperature data. Missing data, data standardization, and interpolation were taken care of during data preparation [8].  
A very interesting combination was presented by [PERSON] and [PERSON] [23], which included the attention mechanism, to capture the links between several meteorological parameters for air temperature prediction successfully. This model has the ability to learn from the temporal and spatial features and interactions between the data. When evaluated using different combinations of models, it received the best Mean Squared Error (MSE) and Mean Absolute Error (MAE) metrics among all the other metrics.  
The work of [PERSON] et al. [2] describes a spatial short-term forecast of unusual weather events or patterns that can be used to warn the population of an impending hazard. Using IoT, a change in anomalies is identified where high-resolution weather models are used. The use of unsupervised learning algorithms is used to find unusual patterns.  
As an alternative, solar irradiance prediction using meteorological data in parts of Bangladesh was also used in study by [PERSON] et al. [13]. Thus, three different NNs were compared: RNN, LSTM, and Gated Recurrent Unit (GRU) [24]. Using meteorological data from the previous day, it was possible to predict solar irradiance for future dates. The best model with respect to the Mean Absolute Percentage Error (MAPE) metric was GRU, with a score of 19.28.  
Although LSTM has much better results than traditional statistical methods in predicting the time-series of large amounts of data, the combinations of the LSTM model with other methods, namely LSTM-SAE, work better, as described by [PERSON] and [PERSON] [20]. If we have to process very non-linear data and long time-series, namely with supervised learning, we do not achieve good enough results. Therefore, the use of an unsupervised learning and LSTM-SAE model combination is proposed.  
Based on the time-series data, the following combined method to improve the prediction accuracy, called ConvLSTM, was also proposed by [PERSON] et al. [25]. The ConvLSTM combines the CNN and the LSTM model. A stand-alone attention mechanism was implemented for improved temporal resolution and pattern detection. A combined Bi-LSTM method [26], which uses traditional coding and temporal attention learning, has also been presented to work with data involving multiple variables. This yields long time dependence and a hidden correlation between the parameters in the data. The results with regard to the RMSE metric are the best compared to other methods. In the study of [PERSON] et al. [27], the attention mechanism was also used to match the most relevant temporal data.