# Cloud Computing Based on Computational Characteristics for Disaster Monitoring
## 3 Cloud Computing Model for Disaster Monitoring
### Application of MapReduce for Disaster-Monitoring Model

Table 1: Definition, algorithms, and features of five typical disaster-monitoring models mentioned in this paper. Figure 1: Flow chart of vegetation drought monitoring model. ### Performance Evaluation

The general model of cloud computing proposed in this paper provides an algorithm model for remote-sensing data processing on cloud computing platforms. These computing models offer excellent programmability. Three typical models were chosen for these comparative experiments. The performance comparative experiments were implemented on a desktop computer with a 4-core CPU Intel Core i5 and a cloud computing platform using 10 Hadoop nodes with Intel Xeon. Our Hadoop system consisted of 10 nodes linked by a Gigabit ethernet network, including 1 NameNode and 10 DataNodes, wherein the master node was the NameNode in addition to being a DataNode. In addition, the configuration of each node was the same, and the details are as follows: CPU characteristics were an Intel(R) Xeon(R) CPU L5640 (24 cores), 2.27 GHz; memory was 32 GB. Real-time data processing is highlighted as one of the key features for disaster tasks processing. Using the MapReduce model not only achieve the efficiency of distributed parallel computing, but also avoid the complex process of distributed deployment. On the one hand, real-time disaster-monitoring has high requirements in the performance of data processing, and on the other hand, it also places \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Typical Disaster-Monitoring Models** & **Model Description** & **Algorithm Involved** & \begin{tabular}{c} **Computational** \\ **Complexity** \\ \end{tabular} \\ \hline \multirow{6}{*}{SAR Flood monitoring model} & \begin{tabular}{c} A neural network \\ method for synthetic \\ aperture radar (SAR) \\ image segmentation and \\ classification has been \\ developed [45] to study \\ flood extent extraction. \\ \end{tabular} & \begin{tabular}{c} Mosaic \\ Image segmentation \\ Geometric correction \\ Calibration correction \\ Neural network \\ classification \\ Colorization \\ Visualization \\ Format conversion \\ \end{tabular} & \begin{tabular}{c} Moderately \\ complex, O(m\({}^{2}\)) \\ \end{tabular} \\ \cline{1-1}  & \begin{tabular}{c} It implemented the \\ collaborative inversion \\ inversion model \\ \end{tabular} & \begin{tabular}{c} Geometric correction \\ Calibration correction \\ Mosaic \\ Colorization \\ Visualization \\ Image segmentation \\ \end{tabular} & \begin{tabular}{c} Complex, O(m\({}^{2}\)), \\ r is the number of \\ iterations \\ AOT \\ \end{tabular} \\ \hline \multirow{6}{*}{Dust storm monitoring model} & \begin{tabular}{c} Extract dust information \\ and the results will be \\ used as mask for dust \\ classify with brightness \\ temperature differences. \\ \end{tabular} & \begin{tabular}{c} Geometric correction \\ Distribution correction \\ Dust extract \\ Bright temperature \\ Dust intensity \\ Dust classify \\ Mosaic \\ Colorization \\ Visualization \\ \end{tabular} & \begin{tabular}{c} Moderately \\ complex, O(m\({}^{2}\)) \\ \end{tabular} \\ \cline{1-1}  & \
\begin{tabular}{c} Numerical calculation \\ \end{tabular} &
\begin{tabular}{c} NDWI, AWI, AvgNDWI, NDVI, Calibration correction, \\ Dust extraction, Brightness temperature, Dust intensity, \\ Colorization, Band data extraction \\ \end{tabular} \\ \hline
2 & Iterative calculation & AOT \\ \hline
3 & Statistical analysis & AvgNDWI, Dust classification, Image segmentation \\ \hline
4 & Neighborhood operation &
\begin{tabular}{c} Mosaic, Colorization, Visualization, Format conversion, \\ Geometric correction, Image segmentation, Neural \\ network classification \\ \end{tabular} \\ \hline
5 & Frequency domain operation & FFT \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._

\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Algorithm Type** & **Algorithm Name** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Numerical calculation} &