## 4 IoT Systems in the Pre-Disaster Stage
### Sensors
#### 4.1.3 Floods  
People develop flood detection systems based on more considerations such as rainfall [40; 41; 42; 61; 62; 63; 64], water level [40; 43; 44; 46; 47; 62; 63; 65; 66; 67; 68], water pressure [41; 44; 45; 47; 50; 69], soil moisture [42], solar radiation [42], vapor pressure [42], relative humidity [42], humidity [42], humidity [42], temperature [42; 61; 62; 70], air pressure [42; 62], wind speed [42; 61], wind gust [42], wind direction [42], tilt [42], lighting [42], lighting average distance [42], the flow velocity [42; 62]. Moreover, [PERSON] et al. [50] also used GSM to detect locations in their flood monitoring systems.  
We usually collect rainfall data using rain gauges [40; 41], such as double-tipping buckets [40]. We usually measure water level with water level sensors, such as radar level sensors [40; 65], ultrasonic sensors [42; 47; 65; 68; 70], and force-sensitive resistors [47]. Ultrasonic sensors include MaxBotix MB7066 [70], HC-SR04 [68], and so on. A method to measure water level is to measure water pressure and convert that data into water level [45; 64; 69]. [PERSON] et al. [50] detected water with electrical resistance. To be specific, they used fork-shaped probes made of conductive and corrosion-resistant metal with 10 cm long and 1.5 cm spaced terminals [50]. The resistance would drop when the terminal came into contact with water [50][PERSON] Regarding fault [PERSON], they added a triaxial accelerometer (i.e., ADXL345) to use when the water sensors were damaged [50]. [PERSON] et al. [44] combined the Adafruit SHT31-D temperature and humidity sensor with dual ultrasonic sensors to monitor water levels. Another sensor type aims to detect the presence of water. [PERSON] et al. [44] combined a waterproof temperature sensor with dual water presence sensors to detect water presence. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [42] combined Drifters and river drones to measure parameters such as river flow velocity and water temperature during the flooding events. Drifters were the main measuring tool, while RiverDrone aimed to locate the Drifters. [PERSON], [PERSON], Claudel and [PERSON] [70] suggested measuring temperature through passive infrared sensors (e.g., Melexis MLX90 614).  
Cameras are also effective tools for flood detection [41; 48; 49; 63; 71; 72; 73]. Regarding the use of cameras, [PERSON] et al. [72] suggested using no infrared cameras, while [PERSON] et al. [72] and [PERSON] et al. [48] suggested using cameras with water level mark ers. Regarding water level markers, [PERSON] et al. [72] suggested placing highly visible reflective tapes on surfaces visible to cameras ranging from 0 to 1.5 m. Each tape was spaced out with other tapes, which allowed us to obtain a better approximation of the severity of the water level. This method could improve accuracy because it was not affected by the temperature and humidity of the air or the objects that could absorb wave sounds. In addition, [PERSON] et al. [48] suggested putting visible marks on the image captured by the camera to detect the flood-severity level.  
The types of power supplies are numerous. For example, we can place sensors on electricity poles to absorb power [40; 47]. Solar and/or batteries are the common power supply [41; 44; 50; 70], such as Seeed Studio solar cell batteries with TP4056 charge regulators [50] and solar-powered Lithium Iron Phosphate batteries [70]. The models of microprocessors include the Raspberry Pi 4 Model B [48], Arduino Uno [43], Raspberry Pi 3 Model B+ [43], Analog to Digital Converter [50], Intel PXA271 XScale [71], and ESP32 [67]. The models of single computer boards include Arduino [44; 68], NodeMCU [47], TelosB [74], Raspberry Pi [72], ARM Cortex M4 [70], and Arduino DUE [67]. We can also consider SD cards for local data storage [70].