scholarly journals A Coastal Flood Early-Warning System Based on Offshore Sea State Forecasts and Artificial Neural Networks

2021 ◽  
Vol 9 (11) ◽  
pp. 1272
Author(s):  
Michalis Chondros ◽  
Anastasios Metallinos ◽  
Andreas Papadimitriou ◽  
Constantine Memos ◽  
Vasiliki Tsoukala
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Sea Water ◽  
Numerical Models ◽  
Methodological Approach ◽  
Warning System ◽  
Sea State ◽  
Flood Risks ◽  
Coastal Flood ◽  
Artificial Neural

An integrated methodological approach to the development of a coastal flood early-warning system is presented in this paper to improve societal preparedness for coastal flood events. The approach consists of two frameworks, namely the Hindcast Framework and the Forecast Framework. The aim of the former is to implement a suite of high-credibility numerical models and validate them according to past flooding events, while the latter takes advantage of these validated models and runs a plethora of scenarios representing distinct sea-state events to train an Artificial Neural Network (ANN) that is capable of predicting the impending coastal flood risks. The proposed approach was applied in the flood-prone coastal area of Rethymno in the Island of Crete in Greece. The performance of the developed ANN is good, given the complexity of the problem, accurately predicting the targeted coastal flood risks. It is capable of predicting such risks without requiring time-consuming numerical simulations; the ANN only requires the offshore wave characteristics (height, period and direction) and sea-water-level elevation, which can be obtained from open databases. The generic nature of the proposed methodological approach allows its application in numerous coastal regions.

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

scholarly journals Can an early warning system help minimize the impacts of coastal storms? A case study of the 2012 Halloween storm, Northern Italy

2015 ◽  
Vol 3 (5) ◽  
pp. 3409-3448 ◽  
Author(s):  
M. D. Harley ◽  
A. Valentini ◽  
C. Armaroli ◽  
L. Perini ◽  
L. Calabrese ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Numerical Models ◽  
Warning System ◽  
Northern Italy ◽  
Water Levels ◽  
Storm Event ◽  
Forecast Performance ◽  
High Hazard ◽  
Key Sites

Abstract. The Emilia-Romagna Early Warning System (ER-EWS) is a state-of-the-art coastal forecasting system that comprises a series of numerical models (COSMO, ROMS, SWAN and XBeach) to obtain a daily three-day forecast of coastal storm hazard at eight key sites along the Emilia-Romagna coastline (Northern Italy). On the night of 31 October 2012, a major storm event occurred that resulted in elevated water levels (equivalent to a 1-in-20 to 1-in-50-year event) and widespread erosion and flooding. Since this storm happened just one month prior to the roll-out of the ER-EWS, the forecast performance related to this event is unknown. The aim of this study was to therefore reanalyse the ER-EWS as if it had been operating a day before the event and determine to what extent the forecasts may have helped reduce storm impacts. Three different reanalysis modes were undertaken: (1) a default forecast (DF) mode based on three-day wave and water-level forecasts and default XBeach parameters, (2) a "perfect" offshore (PO) forecast mode using measured offshore values and default XBeach parameters; and (3) a calibrated XBeach (CX) mode using measured offshore values and an optimized parameter set obtained through an extensive calibration process. The results indicate that while a "code red" alert would have been issued for the DF mode, an underprediction of the extreme water levels of this event limited high-hazard forecasts to only two of the eight ER-EWS sites. Forecasts based on measured offshore conditions (the PO mode) more-accurately indicate high hazard conditions for all eight sites. Further considerable improvements are observed using an optimized XBeach parameter set (the CX mode) compared to default parameters. A series of what-if scenarios at one of the sites show that artificial dunes, which are a common management strategy along this coastline, could have hypothetically been constructed as an emergency procedure to potentially reduce storm impacts.

Assessment of the Reliability of the Indian Tsunami Early Warning System

2016 ◽  
Vol 50 (3) ◽  
pp. 92-108 ◽  
Author(s):  
T. Srinivasa Kumar ◽  
R. Venkatesan ◽  
N. Vedachalam ◽  
J. Padmanabham ◽  
R. Sundar
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Information Dissemination ◽  
Sea Water ◽  
Tide Gauge ◽  
Warning System ◽  
International Electrotechnical Commission ◽  
Reliability Modeling ◽  
Tsunami Early Warning ◽  
Failure Data

AbstractThis paper analyses the reliability of the Indian Tsunami Early Warning System (ITEWS), comprising a 24 × 7 manned and automated center capable of monitoring the seismic, open sea water level and coastal tide levels and disseminating tsunami bulletins with the aid of proven prerun scenario models during a tsunamigenic earthquake. Since its inception in 2007, the ITEWS has undergone technological maturity with reliability as the prime objective. The system is expected to be in operation throughout the year and alerting the entire Indian Ocean rim countries in the event of a tsunami. Based on International Electrotechnical Commission (IEC) 61508 standards and field failure data, quantitative reliability modeling is done for the subsystems, and it is found that the seismic network, tsunami buoy network, and distress information dissemination systems conform to Safety Integrity Level SIL4, while tide gauge stations conform to SIL4 with a maintenance interval of 45 days. In case of the tsunami buoy network, the failure of one tsunami buoy degrades the network to SIL3 and needs to be restored within 8 months. The study provides confidence on ITEWS's reliable support to tsunami early warning.

Developing Strategies in the Sharing Economy

2022 ◽  
pp. 1224-1245
Author(s):  
Ramona Diana Leon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Business Models ◽  
Warning System ◽  
Sharing Economy ◽  
Knowledge Based ◽  
Artificial Neural ◽  
The One

The sharing economy is challenging the traditional business models and strategies by encouraging collaboration, non-ownership, temporal access, and redistribution of goods and/or services. Within this framework, the current chapter aims to examine how managers influence, voluntarily or involuntarily, the reliability of a managerial early warning system, based on an artificial neural network. The analysis focuses on seven Romanian sustainable knowledge-based organizations and brings forward that managers tend to influence the results provided by a managerial early warning system based on artificial neural network, voluntarily and involuntarily. On the one hand, they are the ones who consciously decide which departments and persons are involved in establishing the structure of the managerial early warning system. On the other hand, they unconsciously influence the structure of the managerial early warning system through the authority they exercise during the managerial debate.

Assessing Flood Risks in Malwathu Oya River Basin in Northern Sri Lanka for Establishing Effective Early Warning System

2021 ◽  
pp. 765-776
Author(s):  
Nandana Mahakumarage ◽  
Vajira Hettige ◽  
Sunil Jayaweera ◽  
Buddika Hapuarachchi
Keyword(s):  
Sri Lanka ◽  
River Basin ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Flood Risks

scholarly journals AN EARLY WARNING SYSTEM FOR THE ON-LINE PREDICTION OF COASTAL STORM RISK ON THE ITALIAN COASTLINE

2012 ◽  
Vol 1 (33) ◽  
pp. 77 ◽  
Author(s):  
Mitchell D. Harley ◽  
Andrea Valentini ◽  
Clara Armaroli ◽  
Paolo Ciavola ◽  
Luisa Perini ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Numerical Models ◽  
Warning System ◽  
End Users ◽  
Impact Indicator ◽  
On Line ◽  
A Chain ◽  
One Year ◽  
The One

The ability to predict the imminent arrival of coastal storm risks is a valuable tool for civil protection agencies in order to prepare themselves and, if needs be, execute the appropriate hazard-reduction measures. In this study we present a prototype Early Warning System (EWS) for coastal storm risk on the Emilia-Romagna coastline in Northern Italy. This EWS is run by executing a chain of numerical models (SWAN, ROMS and XBeach) daily, with the final output transformed into a format suitable for decision making by end-users. The storm impact indicator selected for this site is the Safe Corridor Width (SCW), which is a measure of how much dry beach width is available for safe passage by beach users. A three-day time-series of the predicted SCW is generated daily by the prototype EWS. If the minimum SCW exceeds a certain threshold, a warning is issued to end-users via an automated email service. All available prediction information is also updated daily on-line. Over the one year that the EWS has been operating (June 2011 until June 2012), 13 “code red” and 16 “code orange” warnings have been issued, with the remaining 305 predictions indicating low hazard in terms of the SCW. The reliability of the predictions from the perspective of the end-user has meant that the EWS is currently being expanded to include the entire Emilia-Romagna coastline.

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