# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 2 Related Work
### Modern Methods Used for Forecasting
When essential information is extracted from data, the VMD (variational mode decomposition) and LASSO (least absolute shrinkage and selector operation) feature selection increase the efficiency of the proposed model [50]. BA-SVR (bat algorithm for support vector regression) is a hybrid algorithm developed with an optimization technique that obtains better results for short-, medium-, and long-term forecasting for the closing price of eighteen indices of the mainland in China [51]. A novel hybrid method was proposed, named ICEEMDAN-MOHHO-ELM (improved complete ensemble empirical mode decomposition with adaptive noise multiobjective Harris hawks optimization extreme learning machine), which first deals with high-frequency noise and achieves stabler and higher predictive performance [52]. To identify abnormalities from air quality data in terms of NO\({}_{2}\) concentrations, the anomaly detection method used a hybrid proximity and clustering-based methodology; before that, missing values were replaced by a linear interpolation method [53]. A new method based on intelligent computing was proposed which uses LSTM and optimization, called a smart air quality prediction model (SAQPM), for the prediction of six types of pollutant prediction, namely PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), O\({}_{3}\), CO, and NO\({}_{2}\), but did not mention the imputation, and the missing values were dropped [54]. Two models named LSTM and DAE (deep autoencoders) were proposed for predicting PM\({}_{2.5}\) and PM\({}_{10}\) values and concluded that LSTM performs better than DAE but did not discuss crucial data preprocessing, such as handling missing values and outliers [55]. Images were used for the prediction of air pollution, and the image features were enhanced by using meteorological data, which has boosted the accuracy of classification [56]. While preprocessing the data, a simple imputation technique of backward fill was used for replacing the missing values in the dataset, but the author agreed that more sophisticated methods for imputation can be used. Univariate LSTM with more batch size is effective in predicting CO concentration [57]. Univariate LSTM and ARIMA comparison showed that ARIMA exhibits better prediction in the case of CO concentration. A relative study considering LSTM, simple RNN, and GRU concluded that simple RNN outperforms the other two in stock market prediction, which is the application of time series data. This is because RNNs are susceptible to vanishing gradient problems [58]. A PCA-attention-LSTM model was used to predict PM\({}_{2.5}\) concentration, which obtained better accuracy compared to LSTM and BPNN models [59]. A novel method was proposed that combines deep learning and a geo-statistical approach, known as CNN-BILSTM-IDW, which increased the prediction accuracy using only past values to predict future values, as the data availability was poor [28]. The authors also suggested that using more data and multivariate interpolation technique prediction could improve results, which is performed in the proposed method.