## 4 IoT Systems in the Pre-Disaster Stage
### Analysis and Applications of Sensor Data
Regarding data classification, we can classify the water level data into stable level (i.e., \(-20^{\circ}\), \(20^{\circ}\)), slight increase level (i.e., \(20^{\circ}\), \(45^{\circ}\)), high increase level (i.e., \(45^{\circ}\), \(90^{\circ}\)), slight decrease level (i.e., \((-20^{\circ}\), \(-45^{\circ}\)) and high decrease level (i.e., \(-45^{\circ}\), \(-90^{\circ}\)) [45]. Another classification is 0 to 0.25 for Mild level, 0.26 to 0.5 for the Moderate level, 0.51 to 0.75 for Severe level, and 0.76 to <1 for Critical level [87]. [PERSON] et al. [37] assumed that the water level data is time-series data. They modelled the time series data based on chaos theory. They used the false nearest neighbor method to estimate the value of the separation dimension and the embedding dimension. [PERSON] et al. [64] found that the best results for all the sensors were obtained when the separation dimension was one and the embedding dimension was two. When we adopted the distributed approach, MLP could present the peak values in a better way. The peak values are very important in examining flood prediction since they are at the points where the flooding occurs. In addition, [PERSON] et al. [69] used a multilayer perceptron artificial neural network to construct the recursive prediction model and obtained better results when the separation dimension was one and the embedding dimension was four. Furthermore, [PERSON] et al. [86] suggested using a Bidirectional Gated Recurrent Unit (BiGRU) model with attention mechanisms to deal with the time-series flood data. The attention mechanism is used to automatically adjust how well the input features match the output features, while the BiGRU model aims to process the input series from both directions of the time series (chronologically and anti-chronologically) and then merge their representations together. Since we use cameras for disaster detection more commonly, image processing algorithms have become one of the most common data analysis methods [48; 71; 72; 73; 79]. Previous studies adopted image-processing algorithms to detect floods [48; 71; 72; 73] and typhoons [79]. Although some of them are machine learning algorithms, the data they processed was different from the machine learning mentioned above. Image processing algorithms focus on image data, while the machine learning algorithms mentioned above focus on numerical data. After data analysis, scholars tend to present the results to the public through web applications [40; 42; 44; 47; 48; 57; 58; 61; 63; 65; 71; 72; 83; 85; 88; 89]. Some of them are mobile applications such as [PERSON] et al. [61]. In addition, we usually visualize data with maps to present environmental data and disaster positions in web applications [40; 42; 45; 48; 58; 63; 72; 78; 81; 83; 84; 88]. Interestingly, [PERSON] et al. [81] integrated data from the sensors to create a virtual reality environment to help residents perform fire and earthquake escape drills. In addition, email [43; 50; 71], messages [43; 71], and social media [40; 68] are the ways to send out warnings to people in time. They are also the ways to send out disaster warnings.