scholarly journals Early Warning of Impending Flash Flood Based on AIoT

2021 ◽  
Author(s):  
Wen-Tsai Sung ◽  
Ihzany Vilia Devi ◽  
Sung-Jung Hsiao
Keyword(s):  
Natural Disasters ◽  
Real Time ◽  
Early Warning ◽  
Urban Areas ◽  
Flash Flood ◽  
Warning System ◽  
Observation System ◽  
Mountainous Areas ◽  
Hiking Trails ◽  
Flood Detection

Abstract According to data from the Earth's Volcano and Geological Disaster Reduction Center, a country like Indonesia has experienced many natural disasters, one of which is flooding. Floods are an annual natural disaster, especially on mountain slopes. Mountainous areas experience more dangerous than floods than the urban areas because they can cause other natural disasters, such as landslides and damage the hiking trails. The steep and winding roads minimize and limit the number of officers working in the mountains. Therefore, flood detection and monitoring equipment is needed. The proposed system based on AIoT technology provides real-time flood analysis so that the authorities can monitor residents around mountainous areas and provide early warning. This research focuses on the flood observation system as an early warning system to effectively monitor the flood-prone mountain slopes in real time while taking into account the cost, time efficiency, and safety measurement. The proposed system design includes the integration of sensors into the microcontroller, and the communication between the posts using LoRa and SIM900 sends data to the cloud server via the Internet. All sensor readings for each post are displayed on the app, and alerts are sent via SMS and the app.

scholarly journals Flash Flood Early Warning System in Colima, Mexico

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5231
Author(s):  
José Ibarreche ◽  
Raúl Aquino ◽  
R. M. Edwards ◽  
Víctor Rangel ◽  
Ismael Pérez ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System ◽  
Social Consequences ◽  
Long Term Effects ◽  
Government Officials ◽  
Flood Prediction ◽  
Monitoring Stations

This paper presents a system of sensors used in flash flood prediction that offers critical real-time information used to provide early warnings that can provide the minutes needed for persons to evacuate before imminent events. Flooding is one of the most serious natural disasters humans confront in terms of loss of life and results in long-term effects, which often have severely adverse social consequences. However, flash floods are potentially more dangerous to life because there is often little or no forewarning of the impending disaster. The Emergency Water Information Network (EWIN) offers a solution that integrates an early warning system, notifications, and real-time monitoring of flash flood risks. The platform has been implemented in Colima, Mexico covering the Colima and Villa de Alvarez metropolitan area. This platform consists of eight fixed riverside hydrological monitoring stations, eight meteorological stations, nomadic mobile monitoring stations called “drifters” used in the flow, and a sniffer with data muling capability. The results show that this platform effectively compiles and forwards information to decision-makers, government officials, and the general public, potentially providing valuable minutes for people to evacuate dangerous areas.

The innovation of the FloodHub system for a reliable flood early warning and crisis management

2021 ◽  
Author(s):  
Alexia Tsouni ◽  
Haris Kontoes ◽  
Themistocles Herekakis ◽  
Stavroula Sigourou ◽  
Theodora Perrou
Keyword(s):  
Real Time ◽  
Crisis Management ◽  
Early Warning ◽  
Urban Areas ◽  
Critical Infrastructure ◽  
Warning System ◽  
Disaster Resilience ◽  
Statistical Regression ◽  
Societal Benefit ◽  
Monitoring Tools

<p>Flood has become the most frequent and deadliest type of disaster by far, responsible for the 43.5% of deaths in 2019. What is more, the number of flood events has extremely increased during the last decade (2000-2019), compared to the previous one (1980-1999) (CRED 2020). Therefore, policy and decision makers, more than ever, need efficient flood monitoring tools in order to facilitate their work towards increasing disaster resilience, especially in the urban and peri-urban areas, where most of the population and critical infrastructure are located. For this purpose, the FloodHub system has been developed by the Center of Earth Observation and Satellite Remote Sensing BEYOND, at the National Observatory of Athens, in the framework of the EuroGEO Disaster Resilience Action Group, supported by on-going actions (SMURBS / ERA-PLANET and Excelsior H2020 projects and the sponsor Hellenic Petroleum S.A.). The innovation of the system lies in the integration of different data sources, so as to deliver a reliable flood early warning system, and an operational awareness picture of the crisis every 5’ to the relevant authorities, namely on three levels: municipality, region, and national civil protection. FloodHub allows the near-real-time ingestion and assimilation of hydrometeorological measurements from in-situ telemetric stations, Sentinels data, and crowdsourced data, in a multi-source data fusion concept, using sophisticated hydrologic and hydraulic modelling and statistical regression techniques. It offers increased reliability through a continuous validation and optimization of results, automation in assimilating flood modeling in real time, computational efficiency, openness, flexibility, scalability, transferability, and the speed to meet rapid awareness during the crisis. Therefore, FloodHub is a useful tool in the hands of the relevant authorities and key stakeholders, contributing to an effective flood risk and crisis management. This is in line with the requirements for the implementation of the EU Floods Directive 2007/60/EC, the Sendai Framework for Disaster Risk Reduction, the UN SDGs, as well as the GEO’s Societal Benefit Areas.</p>

scholarly journals A Sustainable Early Warning System Using Rolling Forecasts Based on ANN and Golden Ratio Optimization Methods to Accurately Predict Real-Time Water Levels and Flash Flood

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4598
Author(s):  
Feras Alasali ◽  
Rula Tawalbeh ◽  
Zahra Ghanem ◽  
Fatima Mohammad ◽  
Mohammad Alghazzawi
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Flash Flood ◽  
Golden Ratio ◽  
Warning System ◽  
Forecast Model ◽  
Ann Model ◽  
Model Based ◽  
Ratio Optimization

Remote monitoring sensor systems play a significant role in the evaluation and minimization of natural disasters and risk. This article presents a sustainable and real-time early warning system of sensors employed in flash flood prediction by using a rolling forecast model based on Artificial Neural Network (ANN) and Golden Ratio Optimization (GROM) methods. This Early Flood Warning System (EFWS) aims to support decision makers by providing reliable and accurate information and warning about any possible flood events within an efficient lead-time to reduce any damages due to flash floods. In this work, to improve the performance of the EFWS, an ANN forecast model based on a new optimization method, GROM, is developed and compared to the traditional ANN model. Furthermore, due to the lack of literature regarding the optimal ANN structural model for forecasting the flash flood, this paper is one of the first extensive investigations into the impact of using different exogenous variables and parameters on the ANN structure. The effect of using a rolling forecast model compared to fixed model on the accuracy of the forecasts is investigated as well. The results indicate that the rolling ANN forecast model based on GROM successfully improved the model accuracy by 40% compared to the traditional ANN model and by 93.5% compared to the fixed forecast model.

An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

2017 ◽  
Vol 68 (4) ◽  
pp. 858-863
Author(s):  
Mihaela Oprea ◽  
Marius Olteanu ◽  
Radu Teodor Ianache
Keyword(s):  
Air Pollution ◽  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
Urban Air Pollution ◽  
Warning System ◽  
Air Pollutant ◽  
Pollution Level ◽  
Urban Air ◽  
Prediction Approach

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

Safety Monitoring and Warning System for Subway Construction Workers using Wearable Technology

2019 ◽  
Vol 13 ◽  
Author(s):  
Jun-hua Chen ◽  
Da-hu Wang ◽  
Cun-yuan Sun
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Early Warning ◽  
Warning System ◽  
Safety Monitoring ◽  
Wearable Technology ◽  
Construction Workers ◽  
Great Success ◽  
Subway Construction ◽  
Monitoring And Early Warning

Objective: This study focused on the application of wearable technology in the safety monitoring and early warning for subway construction workers. Methods: With the help of real-time video surveillance and RFID positioning which was applied in the construction has realized the real-time monitoring and early warning of on-site construction to a certain extent, but there are still some problems. Real-time video surveillance technology relies on monitoring equipment, while the location of the equipment is fixed, so it is difficult to meet the full coverage of the construction site. However, wearable technologies can solve this problem, they have outstanding performance in collecting workers’ information, especially physiological state data and positioning data. Meanwhile, wearable technology has no impact on work and is not subject to the inference of dynamic environment. Results and conclusion: The first time the system applied to subway construction was a great success. During the construction of the station, the number of occurrences of safety warnings was 43 times, but the number of occurrences of safety accidents was 0, which showed that the safety monitoring and early warning system played a significant role and worked out perfectly.

A Reliability-Based Early Warning System for Real-Time Monitoring on RC Buildings

2012 ◽  
Vol 446-449 ◽  
pp. 3422-3427
Author(s):  
Wang Sheng Liu ◽  
Ming Zhao
Keyword(s):  
Probability Distribution ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Limit State ◽  
Warning System ◽  
State Function ◽  
Real Time Monitoring ◽  
Rc Buildings ◽  
Load Effect

Today there is an urgent need for effective monitoring whether for old buildings or new ones. While conventional early warning system for real-time monitoring is based on safety factor, this paper proposes a new reliability-based framework to monitor the safety of RC buildings probabilistically. The framework includes modeling resistance, predicting probability distribution of load effect, calculating reliability and setting reliability index threshold. The in-situ test data enables to update the resistance model through a Bayesian process. Meanwhile, the observed monitoring data predicts the probability distribution of load effect. FORM is used to calculate the reliability because the limit state function for real-time monitoring is linear and simple. This study shows that the reliability-based early warning system is of more scientific sense in quantifying the safety and may be applied to many engineering fields.

scholarly journals A review of advances in China’s flash flood early-warning system

2018 ◽  
Vol 92 (2) ◽  
pp. 619-634 ◽  
Author(s):  
Changjun Liu ◽  
Liang Guo ◽  
Lei Ye ◽  
Shunfu Zhang ◽  
Yanzeng Zhao ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System

scholarly journals Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1571 ◽  
Author(s):  
Song ◽  
Park ◽  
Lee ◽  
Park ◽  
Song
Keyword(s):  
Decision Making ◽  
Water Level ◽  
Real Time ◽  
Lead Time ◽  
Flash Flood ◽  
Warning System ◽  
Flood Forecasting ◽  
Urban Stream ◽  
Forecasting And Warning ◽  
Flood Warnings

The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the HEC’s River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

Sign in / Sign up

Export Citation Format

Share Document