# Flash Flood Early Warning System in Colima, Mexico
## 3 Results  
### Network Results  
Data Processing  
The prediction process begins with the data of the sensors received on the server. Previously, a topographic model of each monitored section has been generated along with its simulation. This allows the generation of specific parameters to feed the system which will use them, and the corresponding formula, to generate overflow alerts. Figure 17 shows a simulation of a river section; the red color of the heat map indicates the overflow zones with simulated flow and conditions. The flow of the river can be calculated with well-known formulas that correspond to a certain shape of the canal. The correct formula is selected accordingly to the results of the simulated data of the topographic survey. This allows the server to calculate the flow in real-time and compare it with the previously determined levels; those were established in each node accordingly to the simulation and to the topographic measurements made based on each section. By this, the stream velocity and height could be properly determined taking into consideration the terrain's geometry; therefore, a semaphore risk indicator was stablished using different alerts: (green--no risk; yellow--warning;and red--danger); that way, the system will be able to predict overflow in the river and possible flooding. Figure 18 shows a list of formulas used to obtain the flow based on the shape of the section. During 2019, the EWIN network registered several important weather events. A couple of them are described below. The EWIN web platform notifies its users when RiverCore data exceed pre-set water flow limits. On 10 September, an alert was triggered at node 14. This node, located just north of the city, is the northernmost node and is the closest to the Colima Volcano, where most floodwaters are generated. At the time, there was no rain downstream in the city. This event possessed the conditions necessary to cause a flash flood. Fortunately, no flash flood occurred, since the precipitation at the volcano, although intense, was of short duration, as shown in Figure 19.  
Figure 17: Example of a simulation which shows overflow in section. Figure 18: Formulas used to calculate flow accordingly to the canal shape. The most remarkable weather event in the state of Colima during the 2019 rainy season was Hurricane Narda. According to data from CONAGUA, on 19 September 2019, Narda generated heavy rains in several regions of Colima, Guerrero, Jalisco, Michoacan, and Nayarit; wind with gusts exceeding 70 km/h in Colima, Jalisco, and Michoacan; with gusts exceeding 60 km/h in areas of Guerrero and Nayarit, and high waves from 2 to 4 m on its coasts [36]. The reports of maximum punctual rain recorded in 24 h by this institution are 29 September: 342.3 mm and 30 September: 28.5 mm [37]. The Narda study was done as follows: first, the topographic survey of each area was carried out and then, the fixed nodes were installed; at that time, there were only four. One of them is the most important because it is the one found upstream. The water level data of each of the four operational nodes were collected on the server; before this, the information generated from the topographic surveys were fed to the hydraulic model and based on these alert levels were determined at each studied point. The system processed the data with the aforementioned formulas and compared them with the alert levels; at this point, an alert may be triggered accordingly. This process happens as soon as the data from any node arrive; usually every 60 s, each node reports new data to the server. It is important to mention that no overflow risk was registered during the entire duration of the event. Figure 20 shows the behavior of the river level at some of the monitoring points. Fifteen days of data were graphed, including the mentioned storm. To obtain the information as it is presented, first, the existing data from the two nodes were extracted of the database, then a filtering was made to eliminate noise that can lead to false positives or false negatives.