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

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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A Support Vector Machine Forecasting Model for Typhoon Flood Inundation Mapping and Early Flood Warning Systems

Water ◽

10.3390/w10121734 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1734 ◽

Cited By ~ 12

Author(s):

Ming-Jui Chang ◽

Hsiang-Kuan Chang ◽

Yun-Chun Chen ◽

Gwo-Fong Lin ◽

Peng-An Chen ◽

...

Keyword(s):

Emergency Response ◽

Lead Time ◽

Clustering Algorithm ◽

Support Vector ◽

Forecasting Model ◽

Flood Inundation ◽

Inundation Maps ◽

Flood Warning ◽

Inundation Depth ◽

Proposed Model

Accurate real-time forecasts of inundation depth and extent during typhoon flooding are crucial to disaster emergency response. To manage disaster risk, the development of a flood inundation forecasting model has been recognized as essential. In this paper, a forecasting model by integrating a hydrodynamic model, k-means clustering algorithm and support vector machines (SVM) is proposed. The task of this study is divided into four parts. First, the SOBEK model is used in simulating inundation hydrodynamics. Second, the k-means clustering algorithm classifies flood inundation data and identifies the dominant clusters of flood gauging stations. Third, SVM yields water level forecasts with 1–3 h lead time. Finally, a spatial expansion module produces flood inundation maps, based on forecasted information from flood gauging stations and consideration of flood causative factors. To demonstrate the effectiveness of the proposed forecasting model, we present an application to the Yilan River basin, Taiwan. The forecasting results indicate that the simulated water level forecasts from the point forecasting module are in good agreement with the observed data, and the proposed model yields the accurate flood inundation maps for 1–3 h lead time. These results indicate that the proposed model accurately forecasts not only flood inundation depth but also inundation extent. This flood inundation forecasting model is expected to be useful in providing early flood warning information for disaster emergency response.

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Effective real-time forecasting of inundation maps for early warning systems during typhoons

MATEC Web of Conferences ◽

10.1051/matecconf/201814703014 ◽

2018 ◽

Vol 147 ◽

pp. 03014

Author(s):

Jhih-Huang Wang ◽

Gwo-Fong Lin ◽

Bing-Chen Jhong

Keyword(s):

Early Warning ◽

Lead Time ◽

Early Warning Systems ◽

Reference Points ◽

Support Vector ◽

Spatial Expansion ◽

Warning Systems ◽

Inundation Maps ◽

Inundation Depth ◽

Proposed Model

Accurate forecasts of hourly inundation depths are essential for inundation warning and mitigation during typhoons. In this paper, an effective forecasting model is proposed to yield 1- to 6-h lead-time inundation maps for early warning systems during typhoons. The proposed model based on Support Vector Machine (SVM) is composed of two modules, point forecasting and spatial expansion. In the first module, the rainfall intensity, inundation depth, cumulative rainfall and forecasted inundation depths are considered as model input for point forecasting. In the second module, the geographic information of inundation grids and the inundation forecasts of reference points are used to yield inundation maps for spatial expansion. The results show that the proposed model is able to provide accurate point forecasts at each inundation point. Moreover, the spatial expansion module is capable of producing accurate spatial inundation forecasts. Obviously, the proposed model provides reasonable spatial inundation forecasts, and is able to deal with the nonlinear relationships between inputs and desired output. In conclusion, the proposed model is suitable and useful for inundation forecasting.

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Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Water ◽

10.3390/w11081571 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1571 ◽

Cited By ~ 4

Author(s):

Song ◽

Park ◽

Lee ◽

Park ◽

Song

Keyword(s):

Decision Making ◽

Water Level ◽

Real Time ◽

Lead Time ◽

Flash Flood ◽

Warning System ◽

Flood Forecasting ◽

Urban Stream ◽

Forecasting And Warning ◽

Flood Warnings

The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the HEC’s River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

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A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

Journal of Healthcare Engineering ◽

10.1155/2021/7799793 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Li Liu ◽

Yunfeng Ji ◽

Yun Gao ◽

Zhenyu Ping ◽

Liang Kuang ◽

...

Keyword(s):

Real Time ◽

Early Warning ◽

Traffic Accidents ◽

Recognition Accuracy ◽

Recognition Performance ◽

Warning System ◽

Support Vector ◽

Eyelid Closure ◽

Recognition Result ◽

Fatigue Driving

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

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Review of Tools for the Prediction of Patient Deterioration in the Digital Hospital Setting. (Preprint)

10.2196/preprints.28209 ◽

2021 ◽

Author(s):

Kay Debby Mann ◽

Norm Good ◽

Farhad Fatehi ◽

Sankalp Khanna ◽

Victoria Campbell ◽

...

Keyword(s):

Real Time ◽

Early Warning ◽

Hospital Setting ◽

Warning System ◽

Scoring Systems ◽

Time Data ◽

Time Dynamic ◽

Patient Deterioration ◽

Dynamic Risk ◽

Risk Estimates

BACKGROUND Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data, and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates. OBJECTIVE This review describes published studies on the development, validation and implementation of tools for prediction of patient deterioration in hospital general wards. METHODS An electronic database search of peer-reviewed journal papers 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration - defined by unplanned transfer to intensive care unit (ICU), cardiac arrest, or death. Studies conducted solely in ICUs, emergency departments or on single diagnosis patient groups were excluded. RESULTS Forty-five publications, eligible for inclusion, were heterogeneous in design, setting and outcome measures. Most papers were retrospective studies utilizing cohort data to develop, validate or statistically evaluate prediction tools. Tools consisted of early warning, screening or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time, deal with complexities of longitudinal data and warn of deterioration risk earlier. Only a few studies detailed the results of implementation of the deterioration warning tools. CONCLUSIONS Despite relative progress on the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvement of patient outcomes. Further work is needed to realise the potential of automated predictions and updating dynamic risk estimates as part of an operational early warning system for inpatient deterioration.

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

Geosciences ◽

10.3390/geosciences8090346 ◽

2018 ◽

Vol 8 (9) ◽

pp. 346 ◽

Cited By ~ 12

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.

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Near real-time GPS applications for tsunami early warning systems

Natural Hazards and Earth System Science ◽

10.5194/nhess-10-181-2010 ◽

2010 ◽

Vol 10 (2) ◽

pp. 181-189 ◽

Cited By ~ 30

Author(s):

C. Falck ◽

M. Ramatschi ◽

C. Subarya ◽

M. Bartsch ◽

A. Merx ◽

...

Keyword(s):

Real Time ◽

Early Warning ◽

Early Warning System ◽

Tide Gauge ◽

Warning System ◽

Early Warning Systems ◽

Time Data ◽

Essential Information ◽

Tsunami Early Warning ◽

Land Mass

Abstract. GPS (Global Positioning System) technology is widely used for positioning applications. Many of them have high requirements with respect to precision, reliability or fast product delivery, but usually not all at the same time as it is the case for early warning applications. The tasks for the GPS-based components within the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) are to support the determination of sea levels (measured onshore and offshore) and to detect co-seismic land mass displacements with the lowest possible latency (design goal: first reliable results after 5 min). The completed system was designed to fulfil these tasks in near real-time, rather than for scientific research requirements. The obtained data products (movements of GPS antennas) are supporting the warning process in different ways. The measurements from GPS instruments on buoys allow the earliest possible detection or confirmation of tsunami waves on the ocean. Onshore GPS measurements are made collocated with tide gauges or seismological stations and give information about co-seismic land mass movements as recorded, e.g., during the great Sumatra-Andaman earthquake of 2004 (Subarya et al., 2006). This information is important to separate tsunami-caused sea height movements from apparent sea height changes at tide gauge locations (sensor station movement) and also as additional information about earthquakes' mechanisms, as this is an essential information to predict a tsunami (Sobolev et al., 2007). This article gives an end-to-end overview of the GITEWS GPS-component system, from the GPS sensors (GPS receiver with GPS antenna and auxiliary systems, either onshore or offshore) to the early warning centre displays. We describe how the GPS sensors have been installed, how they are operated and the methods used to collect, transfer and process the GPS data in near real-time. This includes the sensor system design, the communication system layout with real-time data streaming, the data processing strategy and the final products of the GPS-based early warning system components.

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Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Patterns

Sensors ◽

10.3390/s19214806 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4806 ◽

Cited By ~ 2

Author(s):

Wen-Lin Chu ◽

Chih-Jer Lin ◽

Kai-Chun Kao

Keyword(s):

Motor Control ◽

Real Time ◽

Ball Bearing ◽

Warning System ◽

Support Vector ◽

Discrete Wavelet ◽

General Regression Neural Networks ◽

Experimental Process ◽

Time Motor ◽

General Regression

In this study, a set of methods for the inspection of a working motor in real time was proposed. The aim was to determine if ball-bearing operation is normal or abnormal and to conduct an inspection in real time. The system consists of motor control and measurement systems. The motor control system provides a set fixed speed, and the measurement system uses an accelerometer to measure the vibration, and the collected signal data are sent to a PC for analysis. This paper gives the details of the decomposition of vibration signals, using discrete wavelet transform (DWT) and computation of the features. It includes the classification of the features after analysis. Two major methods are used for the diagnosis of malfunction, the support vector machines (SVM) and general regression neural networks (GRNN). For visualization and to input the signals for visualization, they were input into a convolutional neural network (CNN) for further classification, as well as for the comparison of performance and results. Unique experimental processes were established with a particular hardware combination, and a comparison with commonly used methods was made. The results can be used for the design of a real-time motor that bears a diagnostic and malfunction warning system. This research establishes its own experimental process, according to the hardware combination and comparison of commonly used methods in research; a design for a real-time diagnosis of motor malfunction, as well as an early warning system, can be built thereupon.

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Landslide Geohazard Monitoring, Early Warning and Stabilization Control Methods

Studia Geotechnica et Mechanica ◽

10.2478/sgem-2014-0001 ◽

2014 ◽

Vol 36 (1) ◽

pp. 3-13 ◽

Cited By ~ 3

Author(s):

Zbigniew Bednarczyk

Keyword(s):

Real Time ◽

Early Warning ◽

Warning System ◽

Landslide Monitoring ◽

High Plasticity ◽

Monitoring Methods ◽

Time Data ◽

Stabilization Control ◽

Starting Point ◽

On Line

Abstract This paper is a presentation of landslide monitoring, early warning and remediation methods recommended for the Polish Carpathians. Instrumentation included standard and automatic on-line measurements with the real-time transfer of data to an Internet web server. The research was funded through EU Innovative Economy Programme and also by the SOPO Landslide Counteraction Project. The landslides investigated were characterized by relatively low rates of the displacements. These ranged from a few millimetres to several centimetres per year. Colluviums of clayey flysch deposits were of a soil-rock type with a very high plasticity and moisture content. The instrumentation consisted of 23 standard inclinometers set to depths of 5-21 m. The starting point of monitoring measurements was in January 2006. These were performed every 1-2 months over the period of 8 years. The measurements taken detected displacements from several millimetres to 40 cm set at a depth of 1-17 m. The modern, on-line monitoring and early warning system was installed in May 2010. The system is the first of its kind in Poland and only one of several such real-time systems in the world. The installation was working with the Local Road Authority in Gorlice. It contained three automatic field stations for investigation of landslide parameters to depths of 12-16 m and weather station. In-place tilt transducers and innovative 3D continuous inclinometer systems with sensors located every 0.5 m were used. It has the possibility of measuring a much greater range of movements compared to standard systems. The conventional and real-time data obtained provided a better recognition of the triggering parameters and the control of geohazard stabilizations. The monitoring methods chosen supplemented by numerical modelling could lead to more reliable forecasting of such landslides and could thus provide better control and landslide remediation possibilities also to stabilization works which prevent landslides.

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Genetic Algorithm based Predictive model for COVID 19 – Theoretical Model based on an evolutionary approach

10.21203/rs.3.rs-107714/v3 ◽

2021 ◽

Author(s):

Srikanth Rangarajan ◽

Srikanth Poranki ◽

Bahgat Sammakia

Keyword(s):

Genetic Algorithm ◽

Real Time ◽

Theoretical Method ◽

Contact Tracing ◽

Reproductive Number ◽

Time Data ◽

Proposed Model ◽

Fitness Value ◽

Real Time Data ◽

Unique Part

Abstract In this manuscript we propose a novel theoretical method that models the evolution, spread and transmission of COVID 19 pandemic. The proposed model is inspired partly from the evolutionary based state of the art genetic algorithm. The rate of virus evolution, spread and transmission of the COVID 19 and its associated recovery and death rate are modeled using the principle inspired from evolutionary algorithm. Furthermore, the interaction within a community and interaction outside the community is modeled. The constraint with respect to interaction has been implemented by a machine learning type algorithm and becomes the unique part of our study . Using this model, the maximum healthcare threshold is fixed as a constraint. Our evolutionary based model distinguishes between individuals in the population depending on the severity of their symptoms/infection based on the fitness value of the individuals. There is a need to differentiate between virus infected diagnosed (Self isolated) and virus infected non-diagnosed (Highly interacting) sub populations/group. In this study the model results does not compare the number outcomes with any actual real time data based curves. However, the results from the model demonstrates that a strict lockdown, social-distancing measures in conjunction with more number of testing and contact tracing is required to flatten the ongoing COVID-19 pandemic curve. A reproductive number of 2.4 during the initial spread of virus is predicted from the model for the randomly considered population. The proposed model has the potential to be further fine-tuned and matched accurately against real time data.

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