# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 3 Proposed MIA-LSTM Model  
Figure 1 shows the unique methodology which is used in this paper for the prediction of air pollution concentration. The unavailable values from the input data were found and replaced using iterative imputation with an ET regressor as an estimator for calculating the missing values. The output data after imputation contained some anomaly values which were detected and further removed by using LSTM autoencoders by setting the threshold level of MAE. The clean data were then passed through a multivariate LSTM module which predicted the value of PM\({}_{2.5}\) using the previous data of all pollutants and meteorological parameters. Algorithm 1. is the algorithm for proposed MIA-LSTM model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference No** & **Technique** & **Preprocessing Method** & **Strength** & **Limitations** \\ \hline
[28] & CNN-BILSTM-IDW & Linear interpolation for missing values & \
\begin{tabular}{c} Deep \\ learning and geostatistical \\ approach obtained better \\ accuracy. \\ \end{tabular} &
\begin{tabular}{c} Time complexity is not \\ discussed in the hybrid \\ method. \\ \end{tabular} \\
[49] & LSTM & Missing values ignored &
\begin{tabular}{c} Different LSTM \\ configurations were tested. \\ \end{tabular} & Missing values ignored. \\
[50] & VMD-LASSO-SAE- & VMD and LASSO & \
\begin{tabular}{c} Extracted information from \\ high-resolution dataset. \\ \end{tabular} &
\begin{tabular}{c} Time complexity is not \\ mentioned. \\ \end{tabular} \\
[53] & Proximity and & Linear interpolation for missing values & \
\begin{tabular}{c} Anomalies detected from air \\ pollution dataset. \\ LSTM proved slightly better \\ than DAE. \\ \end{tabular} &
\begin{tabular}{c} Not mentioned. \\ method. \\ \end{tabular} \\
[55] & LSTM and DAE & \begin{tabular}{c} Simple imputation of \\ backward fill used for \\ imputation \\ \end{tabular} & \
\begin{tabular}{c} Data plus images used for \\ pollution prediction. \\ \end{tabular} &
\begin{tabular}{c} Requires more \\ computational power. \\ \end{tabular} \\
[56] & \begin{tabular}{c} Univariate LSTM \\ \end{tabular} & \begin{tabular}{c} Negative values \\ present in dataset were \\ removed \\ \end{tabular} & \
\begin{tabular}{c} Model performance checked \\ with different batch size. \\ \end{tabular} &
\begin{tabular}{c} Calibration part is missing \\ for the deployed device. \\ \end{tabular} \\
[58] & \begin{tabular}{c} Simple RNN, LSTM, \\ and GRU \\ \end{tabular} & \begin{tabular}{c} Null values are \\ removed \\ \end{tabular} & \
\begin{tabular}{c} For lower time intervals, \\ LSTM and GRU obtained \\ good accuracy. \\ \end{tabular} &
\begin{tabular}{c} Imputation not performed \\ for missing values. \\ \end{tabular} \\
[59] & PCA-Attention-LSTM & \begin{tabular}{c} Missing values filled \\ with average of \\ adjacent values \\ \end{tabular} & \
\begin{tabular}{c} Analysis of variable \\ importance was performed. \\ \end{tabular} &
\begin{tabular}{c} Time complexity is not \\ mentioned. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Strength and limitations of forecasting techniques. Figure 1: MIA-LSTM model. ```
1. Input feature1, feature2,\(\rightarrow\)featurex. 2. Output values prediction for PM2,5 based on minimum RMSE/MAE values [v1 v2 v3]
3. Perform iterative imputation on raw data 4. Input [ f1 1 f2 1....... fn]
4. Remove the data with missing values
5. Now, split data into two
[f11, f12, f13....f1n]: without missing values
[f21, f22, f23....f2n]: missing values
6. for i = 0, where I = iteration
7. Apply ET regressor on [f11, f12, f13\(\rightarrow\)f1n] by randomly choosing optimal point
8. Impute the data in place of missing values by predicting the values
9. Let
10. Pvj\(\rightarrow\)predicted values at current level Pvi\(\rightarrow\)predicted values at \(\alpha\rightarrow\)minimum threshold at previous value for stopping criteria If Pvj - Pvi <= \(\alpha\), Then Stop
11. Else go to step 7 i++
12. Apply LSTM for Anomaly detection Training set [m1, m2.