River Flood Early Warning System Based on Internet of Things in Binjai City

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International Journal of Research In Vocational Studies (IJRVOCAS)

Vol. 2 No. 4 (2023): IJRVOCAS – Special Issues – INCOSTIG – PP. 42~47 Print ISSN 2777-0168| Online ISSN 2777-0141| DOI prefix: 10.53893 https://journal.gpp.or.id/index.php/ijrvocas/index

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River Flood Early Warning System Based on Internet of Things in Binjai City

Muhammad Rusdi^*, Meidi Wani Lestari, Yuvina, Fitria Nova Hulu Department of Electrical Engineering, Politeknik Negeri Medan, Indonesia

ABSTRACT

Flood is an event of inundation of land, which is usually dry, by water originating from water sources around the land. Binjai City is an area prone to flash floods. This is because in Binjai City there are 5 (five) upstream rivers namely the Bingei river, Mencirim river, Bangkatan river, Diski river and Rambai river. A flood early warning system is a series of systems that function to notify an impending flood disaster. With the existence of a flood early warning system, it can provide information to the community and can reduce victims or losses due to the community's unpreparedness in dealing with flood disasters. This study aims to create a prototype of a river flood early warning system based on the internet of things (IoT). The method used is to design and create a flood early warning system prototype, then perform system testing.

The system is designed using the Arduino Mega2560 microcontroller as the system control center, the HC-SR04 ultrasonic sensors and the ESP32-Cam camera module as system input, as well as buzzer, LCD and website as system output. The transmission medium used is wireless via a 4G WiFi Modem connected to the internet. System prototype testing will be carried out in the Bangkatan river area in Binjai City. From the results of the discussion, it was found that the river flood warning system using the HC-SR04 ultrasonic sensor and the ESP32-Cam camera module based on the Internet of Things was successfully designed and implemented in prototype form and worked well.

Ultrasonic sensors work well in measuring river water level with an average error percentage of 3.642%. The ESP32-Cam camera module works well in capturing images of river water conditions up to a distance of 200 cm. (9 pt).

Keywords:

Flood Early Warning System HC-SR04 Ultrasonic Sensor ESP32-Cam Module Internet of Things Binjai City

Corresponding Author:

Muhammad Rusdi,

Department of Electrical Engineering, Politeknik Negeri Medan,

Almamater Road No 1, Padang Bulan, Medan, North Sumatera, Indonesia.

Email: mrusdi@polmed.ac.id

1. INTRODUCTION

Flood is an event of inundation of land, which is usually dry, by water originating from water sources around the land. Across Indonesia, there are 5,590 main rivers and 600 of them have the potential to cause flooding. Flood-prone areas covered by these main rivers reach 1.4 million hectares. From various studies that have been conducted, the floods that hit vulnerable areas were basically caused by three things. First, human activities that cause spatial changes and impact on natural changes. Second, natural events such as very high rainfall, rising sea levels, storms, and so on. Third, environmental degradation such as loss of ground cover in catchment areas, silting of rivers due to sedimentation, narrowing of river channels and so on (Bappenas, 2019).

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rivers, namely the Bingei River, the Mencirim River, the Bangkatan River, the Diski River and the Rambai River. The causes of frequent flooding in Binjai City are caused by high rainfall in the upstream area of the river, damaged river bodies, damage to water catchment areas, violations of regional spatial planning, lack of integrated development planning, and low community discipline.

Data from BPBD (Regional Disaster Management Agency) for Binjai City, the most recent flood disaster occurred in 2020 which inundated houses and damaged residential areas and damaged road and ditch facilities. Besides that, there were various losses to the community in the form of obstructions to office activities and teaching and learning processes in schools.

A flood early warning system is a series of systems that function to notify an impending flood disaster.

With early warning of flood disasters, it can provide information to the community and can reduce victims or losses due to the community's unpreparedness in dealing with flood disasters.

Research related to flood early warning systems that have been carried out are: The development of flood early warning technology has grown rapidly. This technology has led to improvements in terms of communication and information technology. In this article a flood monitoring information system prototype based on Google Maps has been designed by integrating an ultrasonic sensor as a height detector, an Arduino Uno as a processor, the U-Blox Neo 6m GPS module and a GSM module as a sender of water level data and coordinates to the flood information system station [1]. The flood protection system works automatically and in real time to determine the water level at home. This water level monitoring system is carried out by implementing an Arduino nano-based ultrasonic sensor and the esp 8266 module for data logging at thingspeak.com, which will know the water level made at certain levels and if the sensor detects danger, the device will cut off the electricity. and sending Twitter statuses so that homeowners know the status of their homes [2]. A flood detection and monitoring system with a website interface is a tool that can provide water level information to see the potential for flooding. The HC-SR04 ultrasonic sensor functions as a tool to measure the water level. The system that is created can also provide warnings about the status of the water level level during standby, alert and danger which will send messages via SMS gateway using SIM800L.

SIM800L will send warning messages to 3 different cell phone numbers [3]. An early warning system for tidal flooding designed based on Arduino uses two ultrasonic sensors to measure sea level. The system is also equipped with a buzzer and uses SMS messages to notify the public of tidal floods. From the results of the discussion it was found that the ultrasonic sensor used was able to measure the height (level) of the water surface ranging from 5 cm to 60 cm. The average percentage of measurement error for the two sensors is 1.32%. The accuracy of the system in measuring the height (level) of the water surface is 98.68% [4]. The controlling system automatically identifies the river water level in the hazard category, and sends notifications online using the ESP32-Cam via the smartphone device media [5].

This study aims to create a prototype of a river flood early warning system based on the Internet of Things (IoT). The inputs from the system are the ultrasonic sensor and the ESP32-CAM camera module.

Ultrasonic sensor functions to measure the river water level. The ESP32-CAM camera module functions to capture images of river water realtime conditions. The transmission medium used is wireless via an LTE WiFi Modem connected to the Cloud to connect the microcontroller system with the user. The output of the system is LCD, Buzzer, Website. The LCD will display the river water level. The buzzer will sound when the river water level is in "DANGER" condition. Information on the results of monitoring river water level levels is displayed through a website that can be accessed in real time. The results of the research will be tested on the Bangkatan river in Binjai City. This research was conducted in collaboration with the Regional Disaster Management Agency (BPBD) of Binjai City.

2. RESEARCH METHOD

The block diagram of the research design for an Internet of Things-based river flood early warning system can be seen in Figure 4.2.

The relationship between the input and output of the system created is:

• The inputs to the system are the HC-SR04 ultrasonic sensor and the ESP32-Cam camera module.

Ultrasonic sensor functions to measure the level of river water level against the position of the ultrasonic sensor. The ESP32-Cam camera module functions to capture real-time images of river water conditions.

• The transmission medium used is wireless via a 4G WiFi Modem connected to the internet via an LTE network to connect the microcontroller system with the user's PC.

• The output from the system is LCD, Buzzer, Website. The LCD will display the distance to the river water level. The buzzer will sound when the water level is "DANGER". The website will display flood early warning status and realtime river water conditions on the user's PC.

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Sensor Ultrasonik

HCSR04

Arduino Mega

Buzzer

LCD

NodeMCU ESP8266

Power Supplay

Cloud IoT

Website Modul

ESP32-CAM

Modem WiFi 4G

Figure 1. Block Diagram of Research Design

The parameters observed in this study were the river water level. The water level is measured using an ultrasonic sensor and then compared with the measurement results using a ruler. The percentage of measurement error is calculated using the formula:

% 𝑒𝑟𝑟𝑜𝑟 = |𝑅𝑢𝑙𝑒𝑟 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡 𝑟𝑒𝑠𝑢𝑙𝑡𝑠 − 𝑆𝑒𝑛𝑠𝑜𝑟 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡 𝑟𝑒𝑠𝑢𝑙𝑡𝑠|

𝑅𝑢𝑙𝑒𝑟 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡 𝑟𝑒𝑠𝑢𝑙𝑡𝑠 𝑥 100% (1) The system testing scheme can be seen in Figure 2. The early warning system prototype is placed at a height of 200 cm from the normal river water level. The river water level is calculated by means of the position of the ultrasonic sensor minus the reading of the ultrasonic sensor. The early warning guidelines used in the system are: If the river water level is ≤ 100 cm then the early warning status is "safe". If the river water level is > 100 cm and < 150 cm then the early warning status is "standby". If the river water level is ≥ 150 cm, the early warning status is "danger".

200 cm

Figure 2. System Testing Scheme

3. RESULTS AND ANALYSIS 3.1. Result

Tests are carried out on the system to find out whether the system has been running according to design. Tests were carried out on ultrasonic sensors, ESP32-Cam camera modules, WiFi modems, LCDs, buzzers, and monitoring on the website. The prototype of the design of an IoT-based river flood early warning system (Internet of Things) that has been made can be seen in Figure 3. The website will display flood early warning status and realtime river water conditions on the user's PC as shown in Figure 4.

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Figure 3. Prototype of Flood Early Warning System

Figure 4. Display of the Early Warning System Website

Testing the ultrasonic sensor aims to determine the ability of the sensor to measure the river water level. The results of ultrasonic sensor testing were carried out by taking 10 samples of data and comparing them with the measurement results with a ruler and the results can be seen in Table 1.

Table 1. Ultrasonic Sensor Test Results

Sample The river water level is the measurement result of the

ultrasonic sensor (cm)

The river water level is the measurement result

of ruler (cm)

Sample 1 20 20

Sample 2 47 45

Sample 3 71 75

Sample 4 84 80

Sample 5 52 50

Sample 6 94 90

Sample 7 105 100

Sample 8 124 120

Sample 9 155 140

Sample 10 164 150

Testing the ESP32-Cam camera module to determine its ability to capture images of river water conditions at a distance of up to 200 cm. Images captured by the ESP32-Cam camera are taken every 30 minutes and stored on Google Drive. The results of the ESP32-Cam capture can be seen in Figure 5.

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Figure 5. ESP32-Cam Capture Results stored on Google Drive

Overall system testing aims to determine whether the river flood early warning system based on IoT (Internet of Things) can work as expected. Tests were carried out on ultrasonic sensors, ESP32-Cam camera modules, 4G WiFi modems, buzzers, monitoring on the website. The results of system testing were carried out by taking data as many as 10 samples and the results can be seen in table 2.

Table 2. Overall System Testing Results

Sample Ultrasonic Sensor Measurement

(cm)

ESP32- Cam Module

4G WiFi Modem

LCD Buzzer Early Warning Status on the

Website

Sample 1 20 Active Active Active Not active Safe

Sample 7 105 Active Active Active Not active Standby

Sample 8 124 Active Active Active Not active Standby

Sample 9 155 Active Active Active Active Danger

Sample 10 164 Active Aktif Aktif Active Danger

3.2. Discussion

From the results of the ultrasonic sensor testing in table 1 above, it can be calculated the percentage error of the ultrasonic sensor in measuring the river water level compared to measurements with a ruler using equation (1) and the results can be seen in the graph of Figure 6.

Figure 6. Percentage of Ultrasonic Sensor Measurement Error

From graph in Figure 6 it can be calculated the percentage of error in measuring the river water level by the ultrasonic sensor, namely:

% 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑒𝑟𝑟𝑜𝑟 = 0 + 4,26 + 5,63 + 4,76 + 3,85 + 4,26 + 4,76 + 3,23 + 3,23 + 2,44

10 = 3,642%

0 4,26

5,63 4,76

3,85 4,26 4,76

3,23 3,23 2,44

0 2 4 6

1 2 3 4 5 6 7 8 9 10

%e rror

Sample

Error Percentage

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ultrasonic sensor works well in measuring the level of river water levels up to a level of 200 cm. The ESP32- Cam camera module works well in capturing images of river water conditions up to a distance of 200 cm. The LCD works well in displaying river water level information. The buzzer works well in issuing an alarm when the distance between the river water level and the sensor is more than 150 cm. The 4G WiFi module works well in sending data to websites. The website works well in monitoring the measurement results of river water levels which can be accessed in real-time.

4. CONCLUSION

From the results of the discussion, it can be concluded that the river flood warning system using the HC-SR04 ultrasonic sensor and the ESP32-Cam camera module based IoT has been successfully designed and implemented in the form of a working prototype. Ultrasonic sensors work well in measuring river water level with an average error percentage of 3.642%. The ESP32-Cam camera module works well in capturing images of river water conditions up to a distance of 200 cm.

ACKNOWLEDGEMENTS

The author would like to thank the Medan State Polytechnic for the funding provided through the Contract: B/283/PL5/PT.01.05/2022 which comes from DIPA POLMED funds in 2022.

REFERENCES

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How to Cite

Rusdi, M., Lestari, M. W., Yuvina, & Hulu, F. N. (2023). River Flood Early Warning System Based on Internet of Things in Binjai City. International Journal of Research in Vocational Studies (IJRVOCAS), 2(4), 42–47.

https://doi.org/10.53893/ijrvocas.v2i4.161