Internet of Things based Smart Flood forecasting and Early Warning System

Shankar, B. Maruthi; John, Tegil J.; Karthick, S.; Pattanaik, Balachandra; Pattnaik, Manjula; Karthikeyan, S.

doi:10.1109/iccmc51019.2021.9418331

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…The paper proposed by B Maruthi Shankar et. al [3] presents a Flood Detector System using Arduino, employing sensors for flood level detection. It emphasizes the system's role in anticipating and responding to floods, highlighting the rapid monitoring enabled by Arduino UNO.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey on Real-Time Flood Monitoring and Alert System Using Iot

2024

IRJMETS

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Flood detection systems play a crucial role in mitigating the adverse effects of floods by providing timely warnings and facilitating effective response strategies. This literature survey investigates the diverse range of flood detection systems proposed and developed over recent years. By analyzing a variety of research papers, technical reports, and scholarly articles, this study aims to provide a comprehensive overview of the existing flood detection methodologies, their strengths, limitations, and areas for improvement.

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Section: Literature Reviewmentioning

confidence: 99%

A Survey on Real-Time Flood Monitoring and Alert System Using Iot

2024

IRJMETS

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“…The research proposes a new architecture for building decision support systems using heterogeneous wireless sensor networks. Architectures built to monitor and predict floods use water level, water flow, ultrasonic, and magnetic field sensors [26,[38][39][40]. The study suggests that the proposed system could be very efficient in predicting floods early and preventing loss of life and property.…”

Section: Iot In Flood Detectionmentioning

confidence: 99%

“…The parameters in flood early detection are based on factors associated with flood risk, identified through historical data and previous studies in the target area and some related research [26,[38][39][40]. Temperature and humidity were chosen because changes in temperature indicate weather conditions that favor rainfall.…”

Section: Iot-based Flood Detection System Designmentioning

confidence: 99%

Design of Flood Early Detection Based on the Internet of Things and Decision Support System

Warni,

Angriawan

et al. 2024

ISI

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“…Furthermore, for bias correction and downscaling of the dataset, many more methods can be employed to deal with complex topographic features, unpredictable hydrological variations, and biases in climate model datasets. Past research has explored other procedures in terms of the forecasting of hydrological parameters such as Gravity Recovery and Climate Experiment (GRACE) [71], machine learning algorithms [72,73], and Internet of Things (IoT) [74,75]. A comparison of these measures with the CMIP6 model could be performed for further validation of the climate model and betterment in the forecasting study.…”

Section: Uncertainities and Limitationsmentioning

confidence: 99%

Assessing the Implication of Climate Change to Forecast Future Flood Using CMIP6 Climate Projections and HEC-RAS Modeling

2022

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Climate change has caused uncertainty in the hydrological pattern including weather change, precipitation fluctuations, and extreme temperature, thus triggering unforeseen natural tragedies such as hurricanes, flash flooding, heatwave and more. Because of these unanticipated events occurring all around the globe, the study of the influence of climate change on the alteration of flooding patterns has gained a lot of attention. This research study intends to provide an insight into how the future projected streamflow will affect the flooding-inundation extent by comparing the change in floodplain using both historical and future simulated scenarios. For the future projected data, the climate model Atmosphere/Ocean General Circulation Model (AOGCM) developed by Coupled Model Intercomparison Project Phase 6 (CMIP6) is used, which illustrates that the flood is increasing in considering climate models. Furthermore, a comparison of the existing flood inundation map by the Federal Emergency Management Agency (FEMA) study with the map generated by future projected streamflow data presents the entire inundation area in flood maps, implying the expansion area compared to FEMA needs to be considered in making emergency response plans. The effect of flooding in the inundation area from historical to future flow values, presented mathematically by a calculation of inundation extent percentage, infers that the considered watershed of Rock River is a flood-prone area. The goal is to provide insights on the importance of using the forecasted data for flood analysis and to offer the necessary background needed to strategize an emergency response plan for flood management.

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Internet of Things based Smart Flood forecasting and Early Warning System

Cited by 10 publications

References 21 publications

A Survey on Real-Time Flood Monitoring and Alert System Using Iot

A Survey on Real-Time Flood Monitoring and Alert System Using Iot

Design of Flood Early Detection Based on the Internet of Things and Decision Support System

Assessing the Implication of Climate Change to Forecast Future Flood Using CMIP6 Climate Projections and HEC-RAS Modeling

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