scholarly journals Hydrological Early Warning System Based on a Deep Learning Runoff Model Coupled with a Meteorological Forecast

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1808 ◽  
Author(s):  
Alberto de la Fuente ◽  
Viviana Meruane ◽  
Carolina Meruane
Keyword(s):  
Deep Learning ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Data Driven ◽  
Runoff Forecasting ◽  
Forecast Models ◽  
Forecasting System ◽  
Runoff Forecast ◽  
Near Future

The intensification of the hydrological cycle because of global warming raises concerns about future floods and their impact on large cities where exposure to these events has also increased. The development of adequate adaptation solutions such as early warning systems is crucial. Here, we used deep learning (DL) for weather-runoff forecasting in región Metropolitana of Chile, a large urban area in a valley at the foot of the Andes Mountains, with more than 7 million inhabitants. The final goal of this research is to develop an effective forecasting system to provide timely information and support in real-time decision making. For this purpose, we implemented a coupled model of a near-future global meteorological forecast with a short-range runoff forecasting system. Starting from a traditional hydrological conceptual model, we defined the hydro-meteorological and geomorphological variables that were used in the data-driven weather-runoff forecast models. The meteorological variables were obtained through statistical scaling of the Global Forecast System (GFS), thus enabling near-future prediction, and two data-driven approaches were implemented for predicting the entire hourly flow time-series in the near future (3 days), a simple Artificial Neural Networks (ANN) and a Deep Learning (DL) approach based on Long-Short Term Memory (LSTM) cells. We show that the coupling between meteorological forecasts and data-driven weather-runoff forecast models are able to satisfy two basic requirements that any early warning system should have: The forecast should be given in advance, and it should be accurate and reliable. In this context, DL significantly improves runoff forecast when compared with a traditional data-driven approach such as ANN, being accurate in predicting time-evolution of output variables, with an error of 5% for DL, measured in terms of the root mean square error (RMSE) for predicting the peak flow, compared to 15.5% error for ANN, which is adequate to warn communities at risk and initiate disaster response operations.

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

scholarly journals Integration of Single-Center Data-Driven Vital Sign Parameters into a Modified Pediatric Early Warning System

2017 ◽  
Vol 18 (5) ◽  
pp. 469-476 ◽  
Author(s):  
Catherine E. Ross ◽  
Iliana J. Harrysson ◽  
Veena V. Goel ◽  
Erika J. Strandberg ◽  
Peiyi Kan ◽  
...  
Keyword(s):  
Early Warning ◽  
Vital Sign ◽  
Early Warning System ◽  
Warning System ◽  
Data Driven ◽  
Single Center

Urban Drainage Systems modelling for Early Warning Service Using Data-Driven Modelling

2020 ◽  
Author(s):  
Solomon Seyoum ◽  
Boud Verbeiren ◽  
Patrick Willems
Keyword(s):  
High Resolution ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Data ◽  
Data Driven ◽  
Peak Flows ◽  
Previous Time ◽  
Pluvial Flooding ◽  
Drainage Channels

<p>Urban catchments are characterized by a high degree of imperviousness, as well as a highly modified landscape and interconnectedness. The hydrological response of such catchments is usually complex and fast and sensitive to precipitation variability at small scales. To properly model and understand urban hydrological responses, high-resolution precipitation measurements to capture spatiotemporal variability is crucial input.</p><p>In urban areas floods are among the most recurrent and costly disasters, as these areas are often densely populated and contain vital infrastructure. Runoff from impervious surfaces as a result of extreme rainfall leads to pluvial flooding if the system’s drainage capacity is exceeded. Due to the fast onset and localised nature of pluvial flooding, high-resolution models are needed to produce fast simulations of flood forecasts for early warning system development. Data-driven models for predictive modelling have been gaining popularity, due to the fact they require minimal inputs and have shorter processing time compared to other types of models.</p><p>Data-driven models to forecast peak flows in drainage channels of Brussels, Belgium are being developed at sub-catchment scale, as a proxy for pluvial flooding within the FloodCitiSense project. FloodCitiSense aims to develop an urban pluvial flood early warning service. The effectiveness of these models relies on the input data resolution among others. High-temporal resolution rainfall and runoff data from 13 rainfall and 13 flow gauging stations in Brussels for several years is collected (Open data from Flowbru.be) and the data-driven models for forecasting peak flows in drainage channels are build using the Random Forest classification model.</p><p>Optimal model inputs are determined to increase model performance, including rainfall and runoff information from the current time step, as well as additional information derived from previous time steps.</p><p>The additional inputs are determined by progressively including rainfall data from neighboring stations and runoff from previous time steps equivalent to the lag time equal to the forecasting horizon, in our case two hours. The data-driven model we develop has the form as shown in the following equation.</p><p><strong><em>Q<sub>t</sub> = f(Q<sub>t-lag</sub>, ∑RF<sub>i,j</sub>)  </em></strong><em>for <strong>i</strong> is the number of rainfall stations considered and <strong>j</strong> is the time  from <strong>t-lag</strong> to <strong>t</strong></em></p><p>Where <strong><em>Q<sub>t</sub>  </em></strong>is the flow at a flow station at time <strong><em>t</em></strong>, <strong><em>Q<sub>t-lag </sub></em></strong>is the lagged flow at the station and <strong><em>RF<sub>i,j </sub></em></strong>is the rainfall values for station <strong><em>i</em></strong> and time <strong><em>j</em></strong>.</p><p>For Brussels nine relevant sub-catchments were identified based on historical flood frequency for which we are building data-driven flood forecasting models. For each sub-catchment, RF models are being trained and tested. More than 200,000 data point were available for training and testing the models. For most of the flow stations the data-driven models perform well with R-squared values up to 0.84 for training and 0.6 for testing for a 2-hour forecast horizon. </p><p>To improve the reliability of the data-driven models, as next step, we are including radar rainfall data input, which has the ability to capture temporal and spatial variability of rainfall from localized convective storms to large scale moving storms.</p><p><strong>KEYWORDS</strong></p><p>Data driven models, FloodCitiSense, Flood Early Warning System, Urban pluvial flooding</p>

scholarly journals Innovative Research of Trajectory Prediction Algorithm Based on Deep Learning in Car Network Collision Detection and Early Warning System

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Rongxia Wang ◽  
Malik Bader Alazzam ◽  
Fawaz Alassery ◽  
Ahmed Almulihi ◽  
Marvin White
Keyword(s):  
Deep Learning ◽  
False Alarm ◽  
False Alarm Rate ◽  
Early Warning ◽  
Collision Detection ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Trajectory Prediction ◽  
Activation Area

Predicting the trajectories of neighboring vehicles is essential to evade or mitigate collision with traffic participants. However, due to inadequate previous information and the uncertainty in future driving maneuvers, trajectory prediction is a difficult task. Recently, trajectory prediction models using deep learning have been addressed to solve this problem. In this study, a method of early warning is presented using fuzzy comprehensive evaluation technique, which evaluates the danger degree of the target by comprehensively analyzing the target’s position, horizontal and vertical distance, speed of the vehicle, and the time of the collision. Because of the high false alarm rate in the early warning systems, an early warning activation area is established in the system, and the target state judgment module is triggered only when the target enters the activation area. This strategy improves the accuracy of early warning, reduces the false alarm rate, and also speeds up the operation of the early warning system. The proposed system can issue early warning prompt information to the driver in time and avoid collision accidents with accuracy up to 96%. The experimental results show that the proposed trajectory prediction method can significantly improve the vehicle network collision detection and early warning system.

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
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