scholarly journals Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1283 ◽  
Author(s):  
Li-Chiu Chang ◽  
Mohd Amin ◽  
Shun-Nien Yang ◽  
Fi-John Chang
Keyword(s):  
Real Time ◽  
Warning System ◽  
Google Earth ◽  
Flood Inundation ◽  
Self Organizing Map ◽  
Storm Events ◽  
Inundation Maps ◽  
Forecast Models ◽  
Som Network ◽  
Precautionary Measures

A regional inundation early warning system is crucial to alleviating flood risks and reducing loss of life and property. This study aims to provide real-time multi-step-ahead forecasting of flood inundation maps during storm events for flood early warnings in inundation-prone regions. For decades, the Kemaman River Basin, located on the east coast of the West Malaysia Peninsular, has suffered from monsoon floods that have caused serious damage. The downstream region with an area of approximately 100 km2 located on the east side of this basin is selected as the study area. We explore and implement a hybrid ANN-based regional flood inundation forecast system in the study area. The system combines two popular artificial neural networks—the self-organizing map (SOM) and the recurrent nonlinear autoregressive with exogenous inputs (RNARX)—to sequentially produce regional flood inundation maps during storm events. The results show that: (1) the 4 × 4 SOM network can effectively cluster regional inundation depths; (2) RNARX networks can accurately forecast the long-term (3–12 h) regional average inundation depths; and (3) the hybrid models can produce adequate real-time regional flood inundation maps. The proposed ANN-based model was shown to very quickly carry out multi-step-ahead forecasting of area-wide inundation depths with sufficient lead time (up to 12 h) and can visualize the forecasted results on Google Earth using user devices to help decision makers and residents take precautionary measures against flooding.

scholarly journals Framework for Offline Flood Inundation Forecasts for Two-Dimensional Hydrodynamic Models

Geosciences ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 346 ◽  
Author(s):  
Punit Bhola ◽  
Jorge Leandro ◽  
Markus Disse
Keyword(s):  
Real Time ◽  
Warning System ◽  
Flood Inundation ◽  
Hydrological Models ◽  
Hydrodynamic Models ◽  
Database Queries ◽  
Inundation Maps ◽  
Online Maps ◽  
The City ◽  
Selection Of

The paper presents a new methodology for hydrodynamic-based flood forecast that focuses on scenario generation and database queries to select appropriate flood inundation maps in real-time. In operational flood forecasting, only discharges are forecasted at specific gauges using hydrological models. Hydrodynamic models, which are required to produce inundation maps, are computationally expensive, hence not feasible for real-time inundation forecasting. In this study, we have used a substantial number of pre-calculated inundation maps that are stored in a database and a methodology to extract the most likely maps in real-time. The method uses real-time discharge forecast at upstream gauge as an input and compares it with the pre-recorded scenarios. The results show satisfactory agreements between offline inundation maps that are retrieved from a pre-recorded database and online maps, which are hindcasted using historical events. Furthermore, this allows an efficient early warning system, thanks to the fast run-time of the proposed offline selection of inundation maps. The framework is validated in the city of Kulmbach in Germany.

AUTOMATED SYSTEM FOR NEAR-REAL TIME PREDICTION OF OIL SPILLS FROM EU SATELLITE-BASED DETECTION SERVICE

2017 ◽  
Vol 2017 (1) ◽  
pp. 1574-1593 ◽  
Author(s):  
Rodrigo Fernandes ◽  
Francisco Campuzano ◽  
David Brito ◽  
Manuela Juliano ◽  
Frank Braunschweig ◽  
...  
Keyword(s):  
Real Time ◽  
Oil Spill ◽  
Hazard Assessment ◽  
Situational Awareness ◽  
Oil Spills ◽  
Oil Pollution ◽  
Warning System ◽  
Pollution Source ◽  
Google Earth ◽  
Automated System

ABSTRACT 2017-244: The state-of-the-art in both operational oceanography, remote sensing, and computational capacity, enables now the possibility of developing near-real time, holistic automated services capable of dramatically improving maritime situational awareness to responding to oil spill emergencies. Based on the European satellite-based oil spill and vessel detection service – CleanSeaNet (EMSA – European Maritime Safety Agency), which distributes oil pollution detection standardized notification packages in less than 30 minutes, a new automated early warning system (EWS) for near-real time modelling and prediction of the detected oil spills was developed. This EWS provides 48-hour oil spill forecasts + 24-hour backward simulations, delivering results 5–10 minutes after the reception of the oil spill detection notifications. These forecasts are then distributed in multiple formats and platforms (e.g. Google Earth, e-mail). The oil spill fate and behaviour model used in this EWS is part of MOHID modelling system, and is coupled offline with metocean forecast solutions, taking advantage of autonomous models previously run in multiple institutions. The system is currently able to integrate various metocean forecasting systems, being agnostic about the data sources and applied locations, as long as their outputs comply with commonly adopted formats, including CF compliant files or CMEMS (Copernicus Marine Environment Monitoring Service). The EWS is currently operational in western Iberia, supporting Portuguese Maritime Authority, and is being expanded to neighbourhood regions (from Spain and Morocco) with high resolution metocean models (MARPOCS project funded by European Union Humanitarian Aid & Civil Protection). Taking advantage of the coupling of MOHID oil spill model and CleanSeaNet, an oil spill hazard assessment is made in the Portuguese continental coast, based on the cumulative analysis of drift model simulations from previously detected spills using metocean model data, for a period between 2011–2016. Although this EWS doesn’t replace on-demand operational oil spill forecasting systems, it supports maritime authorities with a fast first-guess forecast solution, allowing:Anticipation of tactical response (including visual inspection of the spill) and mitigation of the pollution episode;A more effective identification of the pollution source, and in case of suspected illegal spill, earlier actions towards effective prosecution of the polluter;In the other hand, the hazard assessment generated is a valuable instrument for the development of efficient planning and prevention strategies. The EWS can be connected to any satellite-based detection service (inside or outside Europe) as long as the detected oil slicks are automatically distributed in a structured and standardized data format similar to CleanSeaNet.

Building the Flood Early Warning System in Guyana at the National scale, with real-time forecast of inundated areas for selected flood prone communities.

2020 ◽  
Author(s):  
Alessandro Masoero ◽  
Imra Hodzic ◽  
Colis Allen ◽  
Andrea Libertino ◽  
Andrea Giusti ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Flood Forecasting ◽  
Local Scale ◽  
Civil Defense ◽  
Data Availability ◽  
Inundation Maps ◽  
Extreme Flood

<p>Within the framework of the project “Strengthening Disaster Management Capacity of Women in Guyana and Dominica”, the National Flood Early Warning System (NFEWS) for Guyana is currently under development. The technical component of the system aims at implementing an operational flood forecasting modelling chain linking meteorological, hydrological and inundation models to provide timely early warnings and predicted flood scenarios, allowing the decision maker to take prevention actions and reduce the impacts of the forecasted event.</p><p>The objective is to implement, together with the Hydromet Service of Guyana, a technical tool able to provide daily forecasts of extreme flood events 1 to seven 7 days in advance, up to the local scale of inundation maps for selected locations.</p><p>The forecasting chain implemented is composed of five (5) main components: i) the weather forecasts, using the limited area WRF model executed twice a day at Hydromet; ii) observational inputs preparation, in particular rain maps through conditional merging between local ground stations and satellite information; iii) rainfall downscaling in several equiprobable scenarios using RAINFARM stochastic model; iv) the distributed hydrological model CONTINUUM, able to estimate river discharge and soil moisture conditions from the meteorological inputs (observation and forecasts), and v)the hydraulic model HYDRA-2D, that using a simplification of the shallow water equations allows fast and reliable inundation mapping.</p><p>At four (4) selected locations, corresponding to relevant flood-prone communities in Guyana, an innovative coupling between the hydrological and the inundation models allows to trigger an operational execution of several hydraulic simulations, resulting in real-time probabilistic forecast of inundation maps. The outflow volumes, derived from CONTINUUM hydrological routing, for different rainfall scenarios are used as inflow inputs for HYDRA-2D. Scalability between hydrological (1.5km) and hydraulic (12m) scales has been achieved through detailed field data collection, that was also used, together with local knowledge, to calibrate the inundation model.</p><p>Through the complete flood forecasting chain set up for Guyana, probability of exceeding significant water depths can be provided in advance to involved stakeholders, triggering early actions and thus enhancing flood resilience at the local scale.</p><p>The hydrological component of the forecasting chain has been implemented at the national level for the whole country, at a feasible spatial and temporal resolution based on a balance between input data availability and expected response time for civil defense activities.</p><p>Being developed using an open source model, as for all the other elements of the forecasting system, the hydraulic modelling component can be, in future, extended and replicated in other areas of interests.</p>

scholarly journals Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

2016 ◽  
Vol 124 (9) ◽  
pp. 1369-1375 ◽  
Author(s):  
Yuan Shi ◽  
Xu Liu ◽  
Suet-Yheng Kok ◽  
Jayanthi Rajarethinam ◽  
Shaohong Liang ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Policy Decision ◽  
Forecast Models

scholarly journals Dynamic Flood Inundation Forecast for the City of Kulmbach Using Offline Two-Dimensional Hydrodynamic Models

10.29007/c4gq ◽  
2018 ◽  
Author(s):  
Punit Bhola ◽  
Jorge Leandro ◽  
Iris Konnerth ◽  
Kanwal Amin ◽  
Markus Disse
Keyword(s):  
Real Time ◽  
Dynamic Models ◽  
Water Levels ◽  
Flood Inundation ◽  
Hydrological Models ◽  
Hydrodynamic Models ◽  
Database Queries ◽  
Inundation Maps ◽  
Inundation Map ◽  
The City

The paper presents a new methodology for hydrodynamic-based flood forecast focusing on sce- nario generation and database queries to select the appropriate flood inundation map in real-time. In operational flood forecasting, discharges are forecast at specific gauges using hydrological models. The water levels are obtained from a rating curve designed for each respective gauge. Particularly for higher discharges when the flow over-spills the side banks, these curves are highly uncertain. Hy- drodynamic models are then required to produce realistic inundation maps and water levels. Hydro- dynamic models are computationally expensive and therefore not feasible for real-time forecasting. Alternatively, pre-calculated inundation maps can be stored in a database which contains a substantial number of scenarios, and used for extracting the most likely map in real-time. This study investigates the application of offline inundation forecast in the city Kulmbach in Germany.

scholarly journals Infrasound array criteria for automatic detection and front velocity estimation of snow avalanches: towards a real-time early-warning system

2015 ◽  
Vol 15 (11) ◽  
pp. 2545-2555 ◽  
Author(s):  
E. Marchetti ◽  
M. Ripepe ◽  
G. Ulivieri ◽  
A. Kogelnig
Keyword(s):  
Risk Management ◽  
Real Time ◽  
Doppler Radar ◽  
Warning System ◽  
Velocity Estimation ◽  
Apparent Velocity ◽  
Snow Avalanches ◽  
Small Aperture ◽  
Forecast Models ◽  
Back Azimuth

Abstract. Avalanche risk management is strongly related to the ability to identify and timely report the occurrence of snow avalanches. Infrasound has been applied to avalanche research and monitoring for the last 20 years but it never turned into an operational tool to identify clear signals related to avalanches. We present here a method based on the analysis of infrasound signals recorded by a small aperture array in Ischgl (Austria), which provides a significant improvement to overcome this limit. The method is based on array-derived wave parameters, such as back azimuth and apparent velocity. The method defines threshold criteria for automatic avalanche identification by considering avalanches as a moving source of infrasound. We validate the efficiency of the automatic infrasound detection with continuous observations with Doppler radar and we show how the velocity of a snow avalanche in any given path around the array can be efficiently derived. Our results indicate that a proper infrasound array analysis allows a robust, real-time, remote detection of snow avalanches that is able to provide the number and the time of occurrence of snow avalanches occurring all around the array, which represent key information for a proper validation of avalanche forecast models and risk management in a given area.

scholarly journals Development of a real-time regional-inundation forecasting model for the inundation warning system

2013 ◽  
Vol 15 (4) ◽  
pp. 1391-1407 ◽  
Author(s):  
Gwo-Fong Lin ◽  
Hsuan-Yu Lin ◽  
Yang-Ching Chou
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Control Point ◽  
Warning System ◽  
Computational Method ◽  
Support Vector ◽  
Forecasting Model ◽  
Time Data ◽  
Inundation Maps ◽  
Proposed Model

Accurate forecasts of the inundation depth are necessary for inundation warning and mitigation. In this paper, a real-time regional forecasting model is proposed to yield 1- to 3-h lead time inundation maps. First, the K-means based cluster analysis is developed to group the inundation depths and to indentify the control points. Second, the support vector machine is used as the computational method to develop the point forecasting module to yield inundation forecasts for each control point. Third, based on the forecasted depths and the geographic information, the spatial expansion module is developed to expand the point forecasts to the spatial forecasts. An actual application to Siluo Township, Taiwan, is conducted to demonstrate the advantage of the proposed model. The results indicate that the proposed model can provide accurate inundation maps for 1- to 3-h lead times. The accurate long lead time forecasts can extend the lead time to allow sufficient time to take emergency measures. Furthermore, the proposed model is an efficient process that can be trained rapidly with real-time data and is more suitable to be integrated with the decision support system. In conclusion, the proposed modeling technique is expected to be useful to support the inundation warning systems.

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