## 2 Literature Review
### Research Gaps and Motivation
\begin{table}
\begin{tabular}{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
[25] & \begin{tabular}{c} Long short term memory \\ (LSTM) \\ \end{tabular} & \begin{tabular}{c} Earthquake impactors \\ building damage using \\ textual descriptions. \\ \end{tabular} & \begin{tabular}{c} California earthquake recorded \\ building damage data with 3423 \\ building (1552 green tagged, 1674 \\ yellow tagged, 197 red tagged). \\ \end{tabular} & \begin{tabular}{c} Accuracy of 80\% is \\ achieved to identify the \\ ATC-20 tags for the test \\ data. \\ \end{tabular} & \
\begin{tabular}{c} Building damage \\ assessment after the \\ occurrence of an \\ earthquake to help the \\ emergency responders and \\ recovery planners. \\ \end{tabular} &
\begin{tabular}{c} Model was trained for a \\ single earthquake with a \\ smaller number of textual \\ information and \\ components. Training the \\ model to classify the \\ multiple events with \\ higher level components. \\ \end{tabular} \\ \hline
[26] & \begin{tabular}{c} Grid-based Recurrent \\ neural network (RNN) \\ \end{tabular} & \begin{tabular}{c} Hurricane prediction, \\ Hurricane Center (NHC). \\ \end{tabular} & \begin{tabular}{c} Hurricanes and tropical storms \\ from 1920 to 2012 from National \\ Hurricane Center (NHC). \\ \end{tabular} & \begin{tabular}{c} Mean squared error \\ DEAN-0.0852, \\ SANDV-0.0800; \\ ISAAC-0.0592. \\ \end{tabular} & \
\begin{tabular}{c} Grid-based RNN \\ outperforms sparse based \\ RNN average by \\ considering data and \\ longitudinal data. \\ \end{tabular} &
\begin{tabular}{c} When the size of the \\ dataset increases, the \\ conversion rate from \\ locations of grid to latitude \\ and \\ inceases. \\ \end{tabular} \\ \hline
[27] & \begin{tabular}{c} CNN-LSTM with \\ 3 DCNN and 2 DCNN \\ \end{tabular} & \begin{tabular}{c} Hybrid Meteorological \\ Organization (WMO) of BestTrack \\ Archive for Climate Stewardship \\ (BITAKOS) typical cyclone \\ \end{tabular} & \begin{tabular}{c} Hybrid CNN-LSTM \\ \(-0.852\), \\ AUC-0.897. \\ \end{tabular} & \
\begin{tabular}{c} Spatio-temporal sequence \\ prediction and analysis of \\ atmospheric variables are \\ done in 3D space. \\ \end{tabular} &
\begin{tabular}{c} High-resolution satellite \\ image data are not \\ prediction. \\ \end{tabular} \\ \hline
[28] & \begin{tabular}{c} 3D Fully connected \\ convolutional neural \\ networks (FCNN) \\ \end{tabular} & \begin{tabular}{c} Post-disaster assessment \\ of hurricane damage. \\ \end{tabular} & \begin{tabular}{c} Point cloud datasets at the south \\ of Texas after Hurricane Harvey. \\ \end{tabular} & \begin{tabular}{c} Salt Lake (Model-64) -97\% \\ of Texas after Hurricane Harvey. \\ \end{tabular} & \
\begin{tabular}{c} To classify the objects of \\ different types such as \\ damages, undamped \\ (Model-100\(\rightarrow\)97.47\%) \\ accuracy. \\ \end{tabular} &
\begin{tabular}{c} Achieved lower precision \\ and recall values for the \\ classes with similar \\ geometric and color \\ features. \\ \end{tabular} \\ \hline
[29] & \begin{tabular}{c} Deep convolutional \\ neural networks \\ (DCNN) \\ \end{tabular} & \begin{tabular}{c} Heavy rain (HR), hall, \\ convective gusts (CG), \\ and thunderstorms. \\ \end{tabular} & \begin{tabular}{c} Surface Contraction Waves (SCW) \\ observations and National \\ Centers for Environmental \\ Prediction (NCP) final (FNL) \\ operational global analysis data \\ during the period of \\ March-October 2010-2014. \\ \end{tabular} & \begin{tabular}{c} Surface Contraction Waves (SCW) \\ observations and National \\ Centers for Environmental \\ \end{tabular} & \begin{tabular}{c} Threat score of \\ thunderstorms—16.1%, \\ hh=33.2%, hall—178\%, \\ CG—55.7\%, \\ \end{tabular} & \
\begin{tabular}{c} Deep CNN extracts \\ nonlinear features of \\ weather variables \\ automatically and \\ considers terrain features. \\ \end{tabular} &
\begin{tabular}{c} Analysis is done for \\ different types of weather \\ events by considering only \\ a few datasets. The hybrid \\ model can enhance the \\ prediction accuracy for a \\ large volume of data. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}