Flood Early Warning System Using Cognitive Artificial Intelligence: The Design of AWLR Sensor

Asnaning, Aniessa Rinny; Putra, Septafiansyah Dwi

doi:10.1109/icitsi.2018.8695948

Cited by 9 publications

(13 citation statements)

References 12 publications

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“…[ 29,37,38,48,[51][52][53][54][55] River or flood water, level observed [36,38,56] River or flood water, level forecasted [32,[57][58][59][60][61] Flood inundation extent Establish a spatial representation of floods to understand the impacted area.…”

Section: Intelligence On Flood Hazardsmentioning

confidence: 99%

“…The measuring of river water levels can be classified into three categories based on the method employed: measured water level (by IoT) [29,37,38,[52][53][54][55], observed water level (by the public) [36,38,56], and forecasted water level through simulations [32,[57][58][59][60]. Many modern early warning systems have employed IoT devices such as automated river gauges to continuously measure river water levels in real-time with greater accuracy [29,37,38,[52][53][54][55]. On the other hand, active and passive social media systems and crowdsourcing platforms are also being used to report water levels observed by the community as texts and photographs with time and location data [36,38,56].…”

Section: River Water Levelmentioning

confidence: 99%

“…In order to gain a further lead time for issuing an early warning for an evacuation, predictive models such as hydrological models and rainfall-runoff inundation models [32,[57][58][59][60] are being used to forecast water levels at a given point. The accuracy of these models can be enhanced by providing continuous real-time data gathered through both IoT and crowdsourcing [29,37,38,[52][53][54][55].…”

Section: River Water Levelmentioning

confidence: 99%

“…The role of technology is important in acquiring intelligence in a crisis situation. In the quest for intelligence for FEWRSs, a broad range of technologies such as the Internet of Things (IoT) [29], big data [30], and near-real-time satellite data [31] are used to capture critical information such as rainfall, rising river levels, and floor rates to detect flood threats. Furthermore, integrated information systems [32,33], geographic information systems (GIS) [34], and simulation techniques [35], are being used to process such information and to generate early warnings.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Samansiri

Fernando

Ingirige

2022

Water

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Deaths and property damage from floods have increased drastically in the past two decades due to various reasons such as increased populations, unplanned developments, and climate change. Such losses from floods can be reduced by issuing timely early warnings and through effective response mechanisms based on situational intelligence during emerging flood situations. This paper presents the outcome of a literature review that was conducted to identify the types and sources of the intelligence required for flood warning and response processes as well as the technology solutions that can be used for offering such intelligence. Twenty-seven different types of intelligence are presented together with the technologies that can be used to extract such intelligence. Furthermore, a conceptual architecture that illustrates how relevant technology solutions can be used to extract intelligence at various stages of a flood cycle for decision-making in issuing early warnings and planning responses is presented.

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Section: Intelligence On Flood Hazardsmentioning

confidence: 99%

Section: River Water Levelmentioning

confidence: 99%

Section: River Water Levelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Samansiri

Fernando

Ingirige

2022

Water

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“…Numerous studies were reported in recent literature elaborating on the optimization of various elements of FEWSs using smart sensors. These optimizations include water level recorder, rainfall, river flow, temperature, real-time wind direction, soil moisture data, and external influences like wind speed [23][24][25].…”

Section: Progress In Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Challenges and Technical Advances in Flood Early Warning Systems (FEWSs)

Perera¹,

Seidou²,

Agnihotri³

et al. 2020

Flood Impact Mitigation and Resilience Enhancement

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Flood early warning systems (FEWSs)—one of the most common flood-impact mitigation measures—are currently in operation globally. The UN Office for Disaster Risk Reduction (UNDRR) strongly advocates for an increase in their availability to reach the targets of the Sendai Framework for Disaster Risk Reduction and Sustainable Development Goals (SDGs). Comprehensive FEWS consists of four components, which includes (1) risk knowledge, (2) monitoring and forecasting, (3) warning, dissemination, and communication, and (4) response capabilities. Operational FEWSs have varying levels of complexity, depending on available data, adopted technology, and know-how. There are apparent differences in sophistication between FEWSs in developed countries that have the financial capabilities, technological infrastructure, and human resources and developing countries where FEWSs tend to be less advanced. Fortunately, recent advances in remote sensing, artificial intelligence (AI), information technologies, and social media are leading to significant changes in the mechanisms of FEWSs and provide the opportunity for all FEWSs to gain additional capability. These technologies are an opportunity for developing countries to overcome the technical limitations that FEWSs have faced so far. This chapter aims to discuss the challenges in FEWSs in brief and exposes technological advances and their benefits in flood forecasting and disaster mitigation.

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Machine Learning: The Role of Machines for Resilient Communities

Kammouh¹,

Cimellaro²

2022

Objective Resilience

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Flood Early Warning System Using Cognitive Artificial Intelligence: The Design of AWLR Sensor

Cited by 9 publications

References 12 publications

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Challenges and Technical Advances in Flood Early Warning Systems (FEWSs)

Machine Learning: The Role of Machines for Resilient Communities

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