## 2 Literature Review
### Research Gaps and Motivation
* The development of improved deep CNN (I-DCNN) model for estimating the intensity using the infrared satellite imagery dataset. * To perform a damage assessment of hurricanes and categorization of various extreme weather events using VGG 19 CNN with data augmentation and transfer learning. * To explore the mitigation steps in reducing the hurricane risk. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Forecasting Model** & **Severe Weather Type** & \begin{tabular}{c} **Data Source, Data Set, Sample** \\ **Size and Location** \\ \end{tabular} & **Accuracy and Evaluation** & **Purpose of Prediction** & **Limitations** \\ \hline \multirow{4}{*}{[21]} & Convolutional deep \\  & neural network (CDNN) & Flood disaster. & Big data of flood disaster for a \\  & & period of 10 years. & (ANN) and DNN with \\  & & higher accuracy of 91\%. & System (DHPS) map \\ \hline \multirow{4}{*}{[22]} & \multirow{4}{*}{DEEPSTORM} & \multicolumn{4}{c}{Microwave radiosometer} \\  & & ice water path detection & \begin{tabular}{c} redar (CPF) measurements \\ embedded in each radiometer \\ project for the period spanning \\ between 2006 and 2017. Hurricane \\ data between Sep 2016 to Dec 2016 as considered. \\ \end{tabular} & \begin{tabular}{c} **Average root mean square** \\ \(\text{convect}\) is accurate in \\ correlation index\({}^{-0.27}\)/s/m\({}^{2}\) \\ less alarm rate\({}^{-2}\)\%. \\ 2016 \\ \end{tabular} & \begin{tabular}{c} Deep most atmospheric \\ convection is accurate in \\ correlation index\({}^{-0.28}\)/s/m\({}^{2}\) \\ less alarm rate\({}^{-2}\)\%. \\ 2016 \\ \end{tabular} & \begin{tabular}{c} **Pione to over-fit, difficult** \\ to capture non-linear \\ convection is accurate in \\ \end{tabular} \\  & & & & & \\ \cline{1-1}  & \multirow{4}{*}{[23]} & Deep learning neural \\ network (DLNN) & Flood susceptibility & \begin{tabular}{c} The high frequency tropical storm \\ area in Vietnam with attributes \\ such as slope, curvature, stream density, and rainfall are \\ considered. \\ \end{tabular} & \begin{tabular}{c} DDNN outperforms \\ multilayer perceptron \\ neural network and \\ support vector machine. \\ accuracy rate\({}^{-0.25}\)/s \\ Positive predictive value. \\ \end{tabular} & \begin{tabular}{c} Feature selection is carried \\ out using the information \\ gain ratio. Automated \\ feature selection at higher \\ level using hybrid models \\ can enhance the prediction \\ accuracy. \\ \end{tabular} \\ \hline \multirow{4}{*}{[24]} & Recurrent neural \\  & network (RNN) and \\ Genetic algorithm (GA) & Hurricane prediction. & Atlantic hurricane data. & \begin{tabular}{c} Proposed model provides \\ nearly 85\% greater \\ accuracy than the other \\ traditional models. \\ \end{tabular} & \begin{tabular}{c} Dynamic time warping \\ nearly 85\% greater \\ accuracy than the other \\ \end{tabular} & \
\begin{tabular}{c} Dynamic time warping \\ (DVT) that measures the \\ distance between the \\ target hurticanes improves \\ the prediction accuracy. \\ \end{tabular} &
\begin{tabular}{c} RNN is not capturing \\ the long-term \\ dependence in the data \\ and fails to produce good \\ results for the changing \\ and dynamic nature of \\ weather variables. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recent studies on extreme weather events prediction using various deep learning models.