# A Smart Framework for Managing Natural Disasters Based on the IoT and ML
## 3 Methodology  
The proposed framework is based on IoT and machine learning algorithms. Sensors will be distributed in the target region for data collection. The multiple sensor nodes deployed will form a wireless sensor network connected to fog nodes that connect to the cloud. Utilizing fog provides many advantages, such as dividing the cities to cells where each cell will be monitored independently. Moreover, fog will apply the roles and outputs of the proposed ML algorithms on the new data, which means reducing the load on the cloud in addition to fast detecting any threat without delay. The proposed framework and its layers, in addition to the proposed algorithms, will be discussed in detail by the following sub-sections.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research** & **The Objectives** & **Used Model** & **Result** & **Limitation** \\ \hline \multirow{6}{*}{[27]} & To construct a LSTM model used in prediction of precipitation. & \multirow{6}{*}{LSTM model} & The recurrent neural network models, combined with meteorological variables, could predict the precipitation accurately in Jingdezhen City and provide sufficient time to prepare strategies against potential related disasters. & \multirow{6}{*}{The developed model only works with large data thus unsuitable for less data.} \\ \cline{3-4}  & To analyze the nonlinear autoregressive network with exogenous inputs (NARX) model for a time series to evaluate the pattern of precipitation & & & \\ \cline{1-1} \cline{2-4}  & Verification of SVM, KNN, Bayes, and deep neural networks for flood prediction accuracy and error rate. & SVM, KNN, and Naive Bayes & The deep neural network can be used efficiently and accurately for flood prediction based on monsoon parameters only prior to flood occurrence & The dataset used in research considered old data, dating back to 1990 to 2002 \\ \hline \multirow{6}{*}{[35]} & Proving that machine learning techniques can accurately map and predict flood-prone areas and can be used to develop flood mitigation policies and plans & \multirow{6}{*}{ANN and LR} & We can quickly identify areas with high flood susceptibility using machine learning techniques and promote the development of policies and infrastructure that can reduce the potential effects of flooding. & \multirow{6}{*}{The problem of availability of data between locations where flood historical, environmental, and hydrological data are scarce} \\ \cline{1-1} \cline{2-4}  & Creating flood susceptibility maps by using ML models & RT, FDA, GLM, and MDA & Flood susceptibility areas have been identified in the study area early alerts. & The model does not include the Internet of Things (IoT) to send early alerts. \\ \hline \multirow{6}{*}{[37]} & ML models can predict the occurrence of urban pluvial flooding & \multirow{6}{*}{ML models} & Based on machine learning (ML) approaches, use a rainfall threshold to classify flood vs. non-flood events. & The model lacks using some techniques such as the Internet of Things for risk classification and sending early alerts to users and rescue teams \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)