scholarly journals Methodology for Simulation of IoT-Based Flood Data Collection Infrastructure

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Azimah Abdul Ghapar ◽  
Salman Yussof
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Early Warning ◽  
Real World ◽  
Research Work ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Flood Prediction ◽  
Software Simulation ◽  
Related Data

Internet of Things (IoT) is a potential technology to be used for data collection tasks in real-world environments. However, due to the difficulty of deploying and testing a real IoT implementation, many researchers end up having to use software simulation to evaluate their proposed techniques. This paper focuses on the use of IoT for collecting flood-related data, which would then use by flood-related applications such as flood prediction applications and flood early warning systems. This paper proposed a methodology for simulating the IoT system used for flood data collection. The proposed methodology consists of four main steps which are identifying the flood environment, defining the architecture for flood data collection, simulating the IoT-based flood data collection infrastructure and analyzing the results. The activities for each step are described in detail as to guide other researchers in the same area to adapt the methodology to their research work.

scholarly journals Forecasting Water Level Of Jhelum River Of Kashmir Valley India, Using Prediction And Earlywarning System

2020 ◽  
Vol 13 (2) ◽  
pp. 35-42
Author(s):  
Mirza Imran ◽  
Abdul Khader P. Sheikh
Keyword(s):  
Water Level ◽  
Hybrid System ◽  
Early Warning ◽  
Research Work ◽  
Early Warning Systems ◽  
Base Station ◽  
Supervised Machine Learning ◽  
Warning Systems ◽  
Inference System ◽  
Control Center

The hydrological disasters have the largest share in global disaster list and in 2016 the Asia’s share was 41% of the global occurrence of flood disasters. The Jammu and Kashmir is one of the most flood-prone regions of the Indian Himalayas. In the 2014 floods, approximately 268 people died and 168004 houses were damaged. Pulwama, Srinagar, and Bandipora districts were severely affected with 102, 100 and 148 km 2 respectively submerged in floods. To predict and warn people before the actual event occur, the Early Warning Systems were developed. The Early Warning Systems (EWS) improve the preparedness of community towards the disaster. The EWS does not help to prevent floods but it helps to reduce the loss of life and property largely. A flood monitoring and EWS is proposed in this research work. This system is composed of base stations and a control center. The base station comprises of sensing module and processing module, which makes a localised prediction of water level and transmits predicted results and measured data to the control center. The control center uses a hybrid system of Adaptive Neuro-Fuzzy Inference System (ANFIS) model and the supervised machine learning technique, Linear Multiple Regression (LMR) model for water level prediction. This hybrid system presented the high accuracy of 93.53% for daily predictions and 99.91% for hourly predictions.

Research on Application of Internet of Things on Intelligent Traffic Safety early Warning

2012 ◽  
Vol 263-266 ◽  
pp. 2890-2894
Author(s):  
Ru Xia Hong
Keyword(s):  
Internet Of Things ◽  
Traffic Safety ◽  
Early Warning ◽  
Traffic Accidents ◽  
Simulation Analysis ◽  
Warning System ◽  
Early Warning Systems ◽  
Calculation Model ◽  
Warning Systems ◽  
Research On Application

In order to avoid potential traffic dangers, as well as to prevent the occurrence of traffic accidents, this paper manages to build a traffic safety early warning system by using Technology of Internet of Things. First, this paper has a general review of related researches. Then, it analyzes basic structures of intelligent traffic safety early warning systems based on Technology of Internet of Things, and establishes data collection and calculation model for traffic flow, following with analysis of traffic safety early warning and responding technologies. Finally, this paper conducts a simulation analysis, the results of which indicate that the said method proposed by this paper has a better robustness.

scholarly journals Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2609
Author(s):  
Moritz Gamperl ◽  
John Singer ◽  
Kurosch Thuro
Keyword(s):  
Internet Of Things ◽  
Early Warning ◽  
Informal Settlements ◽  
Cost Effective ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Mems Sensors ◽  
Effective Measure ◽  
Landslide Early Warning ◽  
Geosensor Network

Worldwide, cities with mountainous areas struggle with an increasing landslide risk as a consequence of global warming and population growth, especially in low-income informal settlements. Landslide Early Warning Systems (LEWS) are an effective measure to quickly reduce these risks until long-term risk mitigation measures can be realized. To date however, LEWS have only rarely been implemented in informal settlements due to their high costs and complex operation. Based on modern Internet of Things (IoT) technologies such as micro-electro-mechanical systems (MEMS) sensors and the LoRa (Long Range) communication protocol, the Inform@Risk research project is developing a cost-effective geosensor network specifically designed for use in a LEWS for informal settlements. It is currently being implemented in an informal settlement in the outskirts of Medellin, Colombia for the first time. The system, whose hardware and firmware is open source and can be replicated freely, consists of versatile LoRa sensor nodes which have a set of MEMS sensors (e.g., tilt sensor) on board and can be connected to various different sensors including a newly developed low cost subsurface sensor probe for the detection of ground movements and groundwater level measurements. Complemented with further innovative measurement systems such as the Continuous Shear Monitor (CSM) and a flexible data management and analysis system, the newly developed LEWS offers a good benefit-cost ratio and in the future can hopefully find application in other parts of the world.

scholarly journals Forecasting flood hazards in real-time: A surrogate model for hydrometeorological events in an Andean watershed

2020 ◽  
Author(s):  
María Teresa Contreras ◽  
Jorge Gironás ◽  
Cristián Escauriaza
Keyword(s):  
Early Warning ◽  
Surrogate Model ◽  
Numerical Models ◽  
Early Warning Systems ◽  
High Fidelity ◽  
Flood Hazards ◽  
Warning Systems ◽  
Mountain Regions ◽  
Flood Prediction ◽  
Physically Based

Abstract. Growing urban development, combined with the influence of El Niño and climate change, have increased the threat of large unprecedented floods induced by extreme precipitation in populated areas near mountain regions of South America. High-fidelity numerical models with physically-based formulations can now predict inundations with a substantial level of detail for these regions, incorporating the complex morphology, and copying with insufficient data and the uncertainty posed by the variability of sediment concentrations. These simulations, however, might have large computational costs, especially if many scenarios need to be evaluated to develop early-warning systems and trigger preemptive evacuations. In this investigation we develop a surrogate model or meta-model to provide a rapid response flood prediction to extreme hydrometeorological events. We characterize the storms with a small set of parameters and use a high-fidelity model to create a database of flood propagation under different conditions. We perform an interpolation and regression procedure by using kriging on the space of parameters that characterize the events, approximating efficiently the flow depths in the urban area. This is the first application of its kind in the Andes region, which can be used to improve the prediction of flood hazards in real conditions, employing low computational resources. It also constitutes a new framework to develop early warning systems to help decision makers, managers, and city planners in mountain regions.

The CARE Telematics Network for the Surveillance of Influenza in Europe

1995 ◽  
Vol 34 (05) ◽  
pp. 518-522 ◽  
Author(s):  
M. Bensadon ◽  
A. Strauss ◽  
R. Snacken
Keyword(s):  
Public Health ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Surveillance Networks ◽  
Spanish Flu ◽  
The 1950S ◽  
National Networks ◽  
First Time

Abstract:Since the 1950s, national networks for the surveillance of influenza have been progressively implemented in several countries. New epidemiological arguments have triggered changes in order to increase the sensitivity of existent early warning systems and to strengthen the communications between European networks. The WHO project CARE Telematics, which collects clinical and virological data of nine national networks and sends useful information to public health administrations, is presented. From the results of the 1993-94 season, the benefits of the system are discussed. Though other telematics networks in this field already exist, it is the first time that virological data, absolutely essential for characterizing the type of an outbreak, are timely available by other countries. This argument will be decisive in case of occurrence of a new strain of virus (shift), such as the Spanish flu in 1918. Priorities are now to include other existing European surveillance networks.

Assessment of Food Security Early Warning Systems for East and Southern Africa

10.1596/29269 ◽  
2018 ◽  
Author(s):  
Ademola Braimoh ◽  
Bernard Manyena ◽  
Grace Obuya ◽  
Francis Muraya
Keyword(s):  
Food Security ◽  
Southern Africa ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems

A Residual Chlorine Removal Method to Allow Drinking Water Monitoring by Biological Early Warning Systems

2005 ◽  
Author(s):  
Willian H. VAN DER Schalie ◽  
David E. Trader ◽  
Mark W. Widder ◽  
Tommy R. Shedd ◽  
Linda M. Brennan
Keyword(s):  
Drinking Water ◽  
Early Warning ◽  
Early Warning Systems ◽  
Water Monitoring ◽  
Residual Chlorine ◽  
Warning Systems ◽  
Removal Method ◽  
Chlorine Removal
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