A novel approach for early flood warning using android and IoT

Jayashree, Sreenivasan; Sarika, S.; Solai, A L; Prathibha, Soma

doi:10.1109/iccct2.2017.7972302

Cited by 21 publications

(13 citation statements)

References 4 publications

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“…Their system was evaluated using rules and generated data that was suited for the controlled environment and the prediction did not use a time-series prediction model. Jayashree et al proposed a flood early warning system that is not dependent on mobile towers for alert message broadcasting [18]. Their system does not require Internet as it is based on ZigBee.…”

Section: Related Workmentioning

confidence: 99%

“…For this study, we implement a prototype dissemination system using ThingSpeak [23] to send out early warning messages via Twitter. Other early warning message dissemination techniques that can be used for our system include short message service (SMS) and local IoT WSN [18]. Figure 3 shows the block diagram of two IoT sensing nodes and the edge computing platform in our system.…”

Section: B Iot Frameworkmentioning

confidence: 99%

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Flood Prediction Using IoT and Artificial Neural Networks with Edge Computing

Samikwa

Voigt

Eriksson

2020

2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE

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Flood disasters affect millions of people across the world by causing severe loss of life and colossal damage to property. Internet of Things (IoT) has been applied in areas such as flood prediction, flood monitoring, and flood detection. Although IoT technologies cannot stop the occurrence of flood disasters, they are an exceptionally valuable apparatus for conveyance of catastrophe readiness data. Advances have been made in flood prediction using artificial neural networks (ANN). Despite the various advancements in flood prediction systems, there has been less focus on the utilisation of edge computing for improved efficiency and reliability of such systems. In this paper, we present a system for short-term flood prediction that uses IoT and ANN, where the prediction computation is carried out on a low power edge device. The system monitors real-time rainfall and water level sensor data and uses the temporal correlative information for ahead-of-time prediction of flood water levels using long shortterm memory. The system can be deployed on battery-powered IoT devices. The results of evaluating a prototype of the system indicate that our model is suitable for real-time flood prediction. Furthermore, we obtain a low response time for running the ANN prediction analysis on the low-power edge device. The application of ANN with edge computing will help improve the efficiency of real-time flood early warning systems by bringing the prediction computation close to where data is collected.

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Section: Related Workmentioning

confidence: 99%

Section: B Iot Frameworkmentioning

confidence: 99%

Flood Prediction Using IoT and Artificial Neural Networks with Edge Computing

Samikwa

Voigt

Eriksson

2020

2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE

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“…These studies have mostly relied on a web server to visualise the data coming from the flood monitoring station. Additionally, Jayashree et al [51] proposed an early warning system based on real-time monitoring of dams via flow and water level sensors. The data collected from sensors is accessible and available to the public and can be fetched through an Android app designed for the research.…”

Section: Computer Vision and Iot Sensors For Early Warning Systemsmentioning

confidence: 99%

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Arshad

Ogie

Barthélemy

et al. 2019

Sensors

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Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

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“…Internet of Things (IoT) is a wide domain used for connecting several physical smart devices through internet for gathering, sharing and taking decisions on certain conditions [1]. IoT has a massive variety of applications in the field of embedded systems, Robotics, automations, etc.…”

Section: Introductionmentioning

confidence: 99%

Application of Internet of Things in Mishap Avoidance Due to Swamping: A Novel Approach

Patel

Mangrulkar

2020

Studies in Systems, Decision and Control

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Swamping is one of the most dangerous natural disasters that affect the affected part of the world, causing human deaths, destruction of places, lessening economic growth and affecting health of human beings. According to economics times published dated 14th Oct. 2019, 1900 deaths occurs in overall monsoon in India, including 382 in Mumbai. 46 people were reported as missing and overall 25 lacks people in 22 states got affected due to swamping mishap been reported causing human deaths. The work presented here is to address swamping issue by flood prevention system which reports the symptoms of danger in advance so that the chances of survival will be more in affected areas. The system provides alarm in advance by giving them the knowledge of affected places and helps to point out the location of victim places, sometimes also to protect valuable properties. Usually in urban areas, when the level of water raises above a certain marked level called threshold level, the regulatory authorities issues an order to open the gutters so as to allow flow of excessive water into it. As a result, the level of the water decreases but on the contrary, people are unaware about the opened gutter. When a person inadvertently walks on the gutter, there is a possibility of a person stumbling into it. Therefore, there is a crucial need for building a system that warns the nearby person to prevent him/her from causing an accident. The major goal of this chapter is to give warning signals to the person through proposed system. It also proposes an IoT system based on Wireless Sensor Network (WSN) in which water level sensors are distributed over a region that monitors the level of the water. As soon as the regulatory authority opens the gutters, the sensor detects the change and sends the response to the base station. The base station will send a notification or an alert to the people present nearby that area. This will led the people to avoid the affected route and follow different route or walk-aside the gutter. The system consists of water level

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A novel approach for early flood warning using android and IoT

Cited by 21 publications

References 4 publications

Flood Prediction Using IoT and Artificial Neural Networks with Edge Computing

Flood Prediction Using IoT and Artificial Neural Networks with Edge Computing

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Application of Internet of Things in Mishap Avoidance Due to Swamping: A Novel Approach

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