FLOOD DETECTION AND FORECAST BY IOT TECHNOLOGY

J.H. Jang and T.W. Li (Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan)
Introduction
Under the influence of climate change, the scale and impact of flood disasters have become moreand more severe in Taiwan due to the increase in rainfall intensity and urbanization. To strengthen
the technology of flood detection and forecast in urban areas, an IoT (Internet of Things) based floodsensor (named flood box) and a flood prediction model based on ML (machine learning) technology
were developed in this study. The flood boxes have been installed at several low-lying locations inTainan, Taiwan and functioned normally under outdoor rainy weather conditions. The ML model wasused to predict the inundation depth at the locations where flood sensors are absent ormalfunctioned. Satisfactory agreements between prediction and observation are found.

By assuming there is a sensor failed once a time, the data of the rest 12 sensors are used topredict the inundation depths for the failed sensor. The regression function can be express by
the following equation in which f(x) is the inundation depth of sensor i, 𝑥 is the inundation depthsof the remaining 12 sensors, 𝜔 is the vector of weights; b is the bias.

𝑓 𝑥

𝜔·𝑥𝑏Study Subjects

Conclusions

The Tainan City in Taiwan is selected
as the study area. In this area, 13flood sensors out of the sensors
installed by WRA are selected foranalysis as shown in Figure 1. The
historical flood event occurred on Aug.
13 in 2019 is selected for case study,
which has brought 188 mm of rainfall
in 3 hours.

Figure 3 shows the comparisons between the observed and predicted inundation depths when
Station #13 and Station #5 are assumed to be failed.

The predictions are slightly underestimated and overestimated for Sensor #13 and Sensor #5,respectively. Overall, the predictions given by the SVR are satisfactory with RMSE of 5.73 cm.

In the future, the ML model can be improved when more data are recorded by the sensors andthe results can be open to the public for disaster prevention uses.

Methodologies
Flood box

Figure 1. The locations of flood sensors in this studyContains five components, including an ultrasonicsensor, an Arduino circuit board (with LoRa modulefunction), an analog signal display, a power supply
battery, and a circuit board connected to an antenna.

The measured water levels are wirelessly transmitted
to a Raspyberry Pi via LoRa and upload to a cloudserver in a period of 10 mins.

The device is small in size, low in power consumption,and using open source language to reduce cost.

ML ModelFigure 3. Comparison between observed and predicted
inundation depths for Sensor #13 and Sensor #5.

Figure 2. Flood sensor

In this study, the inundation depths measured by the 13 flood sensors during the study event areserved as the database for the ML model using the SVR (Support Vector Regression) algorithm.

Acknowledgments

For each sensor, there are a total of 59 inundation depths recorded every 10 minutes from 3:00
a.m. to 12:40 p.m. in 13 Aug., 2019, which are divided into two groups with a ratio of 6: 4 fortraining and test, respectively.

The authors express sincere gratitude to the funding by Ministry of Science and Technology [MOST
108-2119-M-006-005; MOST 108-2625-M-006-008], and are thankful to the Tainan City Government
for providing the flood data and flood sensor data.