Early Flood Detection Research Articles

Retraction notice: Early flood detection and rescue using bioinformatic devices, internet of things (IOT) and Android application

Retraction notice: Early flood detection and rescue using bioinformatic devices, internet of things (IOT) and Android application

Water Level Classification for Early Flood Detection Using KNN Method

Floods occur when water levels exceed normal limits, causing rivers to overflow and inundate low-lying areas. Early warning systems for flood disasters are crucial to mitigate the damage caused, such as loss of life and property. A flood classification system can be developed by utilizing water level data from the Department of Water Resources to predict the likelihood of flooding using the K-Nearest Neighbors (KNN) algorithm. This study aims to determine the flood status classification based on water levels using the KNN method in the Ciliwung River. The research data were obtained from the DKI Jakarta open data site, consisting of 564 samples. The study evaluated K values ranging from 1 to 10. The average accuracy across all K scenarios was 99%, with the best K value being 1, which provided 100% accuracy, sensitivity, and specificity. These results indicate that the KNN method is effective in classifying flood status based on water level data, making it a reliable tool for early warning systems. This system is expected to help reduce the negative impacts of floods by providing accurate and timely information to the public and authorities. This research makes a significant contribution to the development of disaster mitigation technology, particularly in flood risk management in urban areas.

Early Flood Monitoring System Through AI, IOT and Thing Speak Cloud

One kind of natural disaster that can cause significant property damage and even fatalities is flooding. It's critical to diagnose floods early to reduce their effects. In this study, we describe a novel early flood detection technique utilizing the Thingspeak cloud platform, Artificial Intelligence (AI), and Internet Of Things (IOT) technologies. The system consists of a network of Internet Of Things (IOT) sensors positioned in flood-prone areas to detect water levels in real time. These sensors collect and transmit water level data to the Thingspeak cloud platform. The AI algorithms running on the cloud platform examine the data to look for changes in water levels that could indicate an impending flood. Keywords: Artificial Intelligence, Internet of Things, Thingspeak, esp32 microcontroller, water level sensor, Flood, Detection, alert.

IoT Based Early Flood Detection System with Arduino and Ultrasonic Sensors in Flood-Prone Areas

IoT is one of the focuses of application development carried out by various developers today. The aim is to enable various devices and work independently to meet the various needs of their users. The flood early warning system is one of the much-needed IoT-based applications, enabling users to quickly obtain water level information in an area. This application can help people to be more aware of flood disasters, especially during the rainy season. This research develops a flood early warning system application by utilizing Arduino and ultrasonic sensors installed in flood-prone areas. The sensor is used to measure the water level at a time based on the distance from the water surface to the sensor. When the distance between the water surface and the sensor is less than or equal to the set threshold, the sensor will send data and alerts to the user via email. This research applies the IoT design and development method. In addition, this research also used the C and Python programming language for application prototypes and the MySQL database to store the data. the application in this study was tested using the blackbox method and the results showed that all application functions could run properly.

Flood Monitoring and Alerting System

Abstract: Floods pose significant threats to communities worldwide, necessitating the development of efficient monitoring and alerting systems. This abstract presents an IoT-based Flood Monitoring and Alerting System that leverages the power of LoRaWAN (Long Range Wide Area Network) technology to provide real-time flood detection, monitoring, and timely alerts. The system consists of strategically placed water level sensors that use ultrasonic or pressure-based technology to accurately measure water levels. The sensor data is wirelessly transmitted to a central gateway through the LoRaWAN network, known for its long-range and low-power capabilities. A cloud-based platform processes and analyzes the collected data, identifying abnormal water level patterns and potential flood conditions. In case of a flood, the system triggers alerts via mobile notifications, email, and SMS to relevant authorities and stakeholders. Users can access real-time flood information, historical data, and visualizations through a user-friendly web or mobile interface. The system offers cost-effectiveness, extended range, low power consumption, early flood detection, timely response, data-driven decision-making, and has a brighter potential for future enhancements

Deep learning model on rates of change for multi-step ahead streamflow forecasting

Abstract Water security and urban flooding have become major sustainability issues. This paper presents a novel method to introduce rates of change as the state-of-the-art approach in artificial intelligence model development for sustainability agenda. Multi-layer perceptron (MLP) and deep learning long short-term memory (LSTM) models were considered for flood forecasting. Historical rainfall data from 2008 to 2021 at 11 telemetry stations were obtained to predict flow at the confluence between Klang River and Ampang River. The initial results of MLP yielded poor performance beneath normal expectations, which was R = 0.4465, MAE = 3.7135, NSE = 0.1994 and RMSE = 8.8556. Meanwhile, the LSTM model generated a 45% improvement in its R-value up to 0.9055. Detailed investigations found that the redundancy of data input that yielded multiple target values had distorted the model performance. Qt was introduced into input parameters to solve this issue, while Qt+0.5 was the target value. A significant improvement in the results was detected with R = 0.9359, MAE = 0.7722, NSE = 0.8756 and RMSE = 3.4911. When the rates of change were employed, an impressive improvement was seen for the plot of actual vs. forecasted flow. Findings showed that the rates of change could reduce forecast errors and were helpful as an additional layer of early flood detection.

Early Flood Detection Based on Iot Using Machine Learning

The end outcome of climate change is flooding, a complex process that occurs all across the planet. Although certain gauging stations are used to forecast the likelihood of flooding, they are not very reliable. Unexpected flooding events are damaging not only people's lives but also important infrastructure. Our project's goal is to use deep learning to create a system for real-time and accurate flood monitoring and detection. The dependable, low power, wide-area communication method for flood detection that is proposed in this research uses wireless sensor networking technology. Additionally, we use a convolutional neural network to look for any living things that were impacted by the flood.

IOT Based Early Flood Detection and Preventive Measurements

When the flood came, the floods affected both individuals and communities, and they had social, economic, and environmental consequences. So we're designing that kind of system that gets floods before the flood comes. The purpose of the flood warning is to detect and predict flood events so that the public can be informed in advance. Thanks to flood warnings, the effects of major floods can be reduced and flood protection can be strengthened. The warning system will monitor nearby dams, rivers and collect data i.e. water level in the river and river flow rate in relation to floods and the surrounding environment from different sensors. There are three main components of this program. The first part of the system is to detect the water level using the ultrasonic sensor and the water flow rate obtained using the flow sensor as well as the temperature and humidity using the DHT11 sensor and the rain detection by Rain Sensor. The second part of the system sends data to the Thing speak cloud and the third part of the system receives the data from the cloud and displays it in the valley again when a flood situation occurs and an announcement is made to this. Part 3. The structure of the system can be expanded to include a fully functional system to inform the public of the impending flood disaster.

IOT Based Early Flood Detection and Avoidance

IOT Based Early Flood Detection and Avoidance

Designing an early flood detection system prototype in riverbank settlements

There is still a lot of use of the floodgates in the main hole to drain the residential water into the river is still operated manually by someone in charge of opening and closing the floodgates. It is less efficient and often happens to the operator, so the water overflows and can lead to flooding. In this final task, a prototype of an early flood detection system and the automation of sewerage in a settlement located on the riverbanks. The control of floodgates on the main hole works automatically according to the signal from a sensor that reads the state of the water level. Main hole floodgates will work when the river water enters it at a specific limit that sensors will read and provide information on the level of river water in it to someone via WhatsApp to prevent river water from entering the settlement. When the main hole door is closed automatically, the residential water flow will be directed to a temporary reservoir. When the temporary reservoir is full, the sensor will signal to activate the discharge pump that will be discharged into the river to dispose of the water in the reservoir. The design and testing of flood early detection prototype tools and residential water disposal automation can work well by the design principle.

Utilization of the IoT-based early warning system (EWS) for early detection of floods in Setu District, South Tangerang City

Cisadane River is the largest river whose overflow often causes flooding in several locations in South Tangerang City. One of them is located in Pesona Serpong Housing, Setu District, South Tangerang City. The Cisadane Environmental Echo Community (GEMALA) is a community that cares about the sustainability of the Cisadane River. This community service aims to improve understanding of river maintenance and socialize the IoT-based Early Warning System (EWS) tool to the GEMALA community as an early flood detection tool. The method used is to use an ultrasonic sensor HC-SR04 which will measure the water level of the river and will send a signal via the Telegram messaging application. At the end of this activity, an IoT-based Early Warning System (EWS) tool was produced that could be utilized by the GEMALA community as an early flood detection tool.

Early flood detection and rescue using bioinformatic devices, internet of things (IOT) and Android application

PurposeThe impact of natural disasters on human life, the environment and the flora and fauna can be contained to large extent by intelligent human intervention. This study introduces the human capabilities which can be extended considerably with technology. Internet of things have always provided opportunities for predicting and managing manmade/natural disasters. The extreme reason for causing soil erosions, landslides, cloud bursts, floods, etc., are due to excessive rainfall. However, the flood is one of the most happening natural disasters, following Bihar to be the most affected region due to floods. Lots of lives and properties were lost and damaged.Design/methodology/approachThis implemented researchers to introduce an advanced solution for such calamities. Expectations were developed that it would signalize authority as early as possible so that advanced measures are taken before the effect. The lack of sensing or alarming technology in India pushed researchers to develop a model using the Android app that basically detected the upcoming flood and other calamities.FindingsMost importantly the entire model was programmed with IoT and its techniques so that the response is quicker and more accurate.Originality/valueThis research study is original.

IOT Based Early Flood Detection

IOT Based Early Flood Detection

Designing Early Warning Flood Detection and Monitoring System via IoT

Flooding is one of the biggest natural disasters that occurs frequently around the world. It can occur without warning and the after effect of it leaves great damage to the surrounding environment and exposes life threatening to citizen. Therefore, early flood detection and monitoring system with the implementation of Internet of Things and Global Positioning System is proposed in order to reduce the risks that may cause flooding. The aim of this project is to provide the information of a current water level in a drain. When water level increases to a certain level, the system will send a warning notification to users indicating three categories of water level, which are safe, warning and critical level. This system contains an ultrasonic sensor to detect the current water level and at the same time allows users to observe the period of the water level from their phone so that users are more aware of when flooding ought to happen. Moreover, the system consists of a flooding avoidance method that requires the usage of a solenoid as a shutter valve of the drain and water pump to pump out excessive water flow to a suitable place for water release purposes.

Perancangan Sistem Telemetri Deteksi Bencana Banjir Berbasis Web Server Dan Sms Gateway

AbstractIndonesia is one of the areas prone to disasters, especially floods. The impact of flooding can be reduced if the community is better prepared to face the coming flood. Therefore we need an early flood detection tool that can give a signal to the community through early warning so that the community can be more alert in facing flood disastersThe results of this design make two systems, the main control system and the receiving system. The main control system consists of an ultrasonic sensor, SIM800L module and NodeMCU Wi-Fi module. The receiving system consists of a SIM800L module and a siren. The results showed that the ultrasonic sensor used has a good level of accuracy with an error value <1%. The results of SMS testing have a delay until the SMS to the recipient is around 5-10 seconds. The test results also show that 4G network technology has 100% delivery accuracy and 3-4 seconds delivery delay per data. The final result shows thatthe system is able to work as desired and is able to be well connected to the web server.Keyword:Flood Disasters, SIM800L, Ultrasonic sensor, Web Servers, SMS

DESIGN OF EARLY FLOOD DETECTION (EFDe) SYSTEM BASED ON IoT FOR FLOOD VOCATIONAL AREAS

<p>There is an early flood detection tool that has been successfully designed called the Early Flood Detection (EFDe) a system that uses the Internet of Things system which can be accessed by the users through a browser or an Android smartphone. In general, this tool has an accurate sensor response to the water level reading by the sensor. The time needed for the sensor to send data to the server is 10-15 seconds. The speed of this delivery time is influenced by several factors, including sensor response, server response, and the cellular network used. The EFDe system is equipped with an alarm around the location and has 10-15 second response when the indicator shows FLOOD POTENTIAL and will not stop until the signal changed to SAFE. This EFDe System is equipped with a solar panel and solar control charger and has an independent energy source that comes from the sun and stored in batteries. That is why the EFDe system is able to send information in real-time.</p>

Perancangan Sistem Monitoring Ketinggian Air Sebagai Pendeteksi Banjir Berbasis IoT Menggunakan Sensor Ultrasonik

High rainfall in the rainy season can last about six months, this makes it rain continuously and cause an increase in the volume of water that has the potential for flooding. While the public does not get information or notice directly when it will happen. To anticipate these problems, the design of a water level monitoring system as an early flood detection based on IoT is considered efficient. The system will detect the distance of the sensor to the surface of the water, if the distance of the surface water approaches the sensor, the system will sound the siren as a sign of flooding. The system will display the alert status that occurs and the water surface distance in realtime.. From 100 data of water surface distance with ultrasonic sensors that have been tested, get 6 times the distance reading error. In testing of several water surface distances with sensors, the system can store all distance data into a database.

Early detection of flood in urban catch basins using radio frequency slot line array

This paper presents a low-cost sensor for the early detection of flood in urban catch basins. Blockage and flooding in urban catch basins is caused by accumulation of sediment. This sensor can be installed inside the catch basin for detecting the critical level of sediment. The sensing system is composed of a two-element array of printed circuit board slot lines connected to a data acquisition system via a reading module for real-time monitoring and in-time triggering of the evacuation alarm in a blocked catch basin. The proposed system can reduce the operational costs of water companies by decreasing the incidents of sewer flooding and pollution.

A Wireless Smart Sensor Network for Flood Management Optimization

Recent development in the sensing technologies and wireless sensor networks has encouraged many innovative applications in disaster management and forecasting. Flood is one of the most dangerous natural disasters that occurs frequently in south Asia. Therefore, water level monitoring and flood early prediction, play an important role in lives and properties saving. In this paper, an optimized Flood Warning System (FWS) is presented. The system is based on multi parameters Wireless Smart Sensor Network (WSSN) for early flood warning. WSSN performs pre-processing procedures at sensor level before sending the data to a base data analyzing station. This pre-processing step is to improve the reliability, data quality and transmission quality. For the purpose of validation, the proposed method is applied using two parameters: The water level (L) and streamflow of water (R) in rivers. The proposed system provides early flood detection by continuously measuring R and L in real-time. The collected data is to be used to predict flood time and place. Data is exchanged among sensors in real-time. Pre-analyzing is performed and reports are sent to the base station only if the analysis gave a high risk level. The main purpose is to cut down the data size. Analysis and simulation showed that the data size is improved significantly using this method. Calculations considers real cases on a part of Pahang River (Sungai Pahang).

Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals

Humanitarian organizations have a crucial role in response and relief efforts after floods. The effectiveness of disaster response is contingent on accurate and timely information regarding the location, timing and impacts of the event. Here we show how two near-real-time data sources, satellite observations of water coverage and flood-related social media activity from Twitter, can be used to support rapid disaster response, using case-studies in the Philippines and Pakistan. For these countries we analyze information from disaster response organizations, the Global Flood Detection System (GFDS) satellite flood signal, and flood-related Twitter activity analysis. The results demonstrate that these sources of near-real-time information can be used to gain a quicker understanding of the location, the timing, as well as the causes and impacts of floods. In terms of location, we produce daily impact maps based on both satellite information and social media, which can dynamically and rapidly outline the affected area during a disaster. In terms of timing, the results show that GFDS and/or Twitter signals flagging ongoing or upcoming flooding are regularly available one to several days before the event was reported to humanitarian organizations. In terms of event understanding, we show that both GFDS and social media can be used to detect and understand unexpected or controversial flood events, for example due to the sudden opening of hydropower dams or the breaching of flood protection. The performance of the GFDS and Twitter data for early detection and location mapping is mixed, depending on specific hydrological circumstances (GFDS) and social media penetration (Twitter). Further research is needed to improve the interpretation of the GFDS signal in different situations, and to improve the pre-processing of social media data for operational use.