Early warning system for detection of urban pluvial flooding hazard levels in an ungauged basin

2018 ◽  
Vol 92 (2) ◽  
pp. 1237-1265 ◽  
Author(s):  
Melisa Acosta-Coll ◽  
Francisco Ballester-Merelo ◽  
Marcos Martínez-Peiró
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Ungauged Basin ◽  
Flooding Hazard ◽  
Pluvial Flooding ◽  
Urban Pluvial Flooding ◽  
Hazard Levels

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 Hybrid Early Warning System for Rock-Fall Risks Reduction

2021 ◽  
Vol 11 (20) ◽  
pp. 9506
Author(s):  
Abdelzahir Abdelmaboud ◽  
Mohammed Abaker ◽  
Magdi Osman ◽  
Mohammed Alghobiri ◽  
Ahmed Abdelmotlab ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Fall Risk ◽  
Confusion Matrix ◽  
Warning System ◽  
Rock Fall ◽  
Economic Costs ◽  
Road Users ◽  
Fall Risks ◽  
Hazard Levels

Rock-fall is a natural threat resulting in many annual economic costs and human casualties. Constructive measures including detection or prediction of rock-fall and warning road users at the appropriate time are required to prevent or reduce the risk. This article presents a hybrid early warning system (HEWS) to reduce the rock-fall risks. In this system, the computer vision model is used to detect and track falling rocks, and the logistic regression model is used to predict the rock-fall occurrence. In addition, the hybrid risk reduction model is used to classify the hazard levels and delivers early warning action. In order to determine the system’s performance, this study adopted parameters, namely overall prediction performance measures, based on a confusion matrix and reliability. The results show that the overall system accuracy was 97.9%, and the reliability was 0.98. In addition, a system can reduce the risk probability from (6.39 × 10−3) to (1.13 × 10−8). The result indicates that this system is accurate, reliable, and robust; this confirms the purpose of the HEWS to reduce rock-fall risk.

Design and Implementation of Intelligent Road Ice Early Warning System

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Wendi Liu ◽  
Zhiqing Zhang ◽  
Dongzhao Li ◽  
Xintong Wu
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Design And Implementation

MODIFIKASI ASESMEN EARLY WARNING SYSTEM UPAYA PENINGKATAN PENERAPAN KESELAMATAN PASIEN

2020 ◽  
Vol 6 (2) ◽  
pp. 112
Author(s):  
Veronika Hutabarat ◽  
Enie Novieastari ◽  
Satinah Satinah
Keyword(s):  
Action Research ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Planning Action

Salah satu faktor dalam meningkatkan penerapan keselamatan pasien adalah ketersediaan dan efektifitas prasarana dalam rumah sakit. Early warning system (EWS) merupakan prasarana dalam mendeteksi perubahan dini  kondisi pasien. Penatalaksanaan EWS masih kurang efektif karena parameter dan nilai rentang scorenya belum sesuai dengan kondisi pasien. Tujuan penulisan untuk mengidentifikasi efektifitas EWS dalam penerapan keselamatan pasien. Metode penulisan action research melalui proses diagnosa, planning action, intervensi, evaluasi dan  refleksi. Responden dalam penelitian ini adalah  perawat yang bertugas di area respirasi dan pasien dengan kasus kompleks respirasi di Rumah Sakit Pusat Rujukan Pernapasan Persahabatan Jakarta. Analisis masalah dilakukan dengan menggunakan diagram fishbone. Masalah yang muncul belum optimalnya implementasi early warning system dalam penerapan keselamatan pasien. Hasilnya 100% perawat mengatakan REWS membantu mendeteksi kondisi pasien, 97,4 % perawat mengatakan lebih efektif dan 92,3 % perawat mengatakan lebih efesien mendeteksi perubahan kondisi pasien. Modifikasi EWS menjadi REWS lebih efektif dan efesien dilakukan karena disesuaikan dengan jenis dan kekhususan Rumah Sakit dan berdampak terhadap kualitas asuhan keperawatan dalam menerapkan keselamatan pasien. Rekomendasi perlu dilakukan monitoring evaluasi terhadap implementasi t.erhadap implementasi REWS dan pengembangan aplikasi berbasis tehnologi

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

KAJIAN KEKUATAN SINYAL RADIO (RSSI) XBEE DALAM RANGKA PEMASANGAN LANDSLIDE EARLY WARNING SYSTEM (LEWS) DI KABUPATEN GARUT, TASIKMALAYA DAN MAJALENGKA

2019 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Riski Fitriani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Received Signal Strength Indicator ◽  
Landslide Early Warning ◽  
Point To Point

Salah satu inovasi untuk menanggulangi longsor adalah dengan melakukan pemasangan Landslide Early Warning System (LEWS). Media transmisi data dari LEWS yang dikembangkan menggunakan sinyal radio Xbee. Sehingga sebelum dilakukan pemasangan LEWS, perlu dilakukan kajian kekuatan sinyal tersebut di lokasi yang akan terpasang yaitu Garut, Tasikmalaya, dan Majalengka. Kajian dilakukan menggunakan 2 jenis Xbee yaitu Xbee Pro S2B 2,4 GHz dan Xbee Pro S5 868 MHz. Setelah dilakukan kajian, Xbee 2,4 GHz tidak dapat digunakan di lokasi pengujian Garut dan Majalengka karena jarak modul induk dan anak cukup jauh serta terlalu banyak obstacle. Topologi yang digunakan yaitu topologi pair/point to point, dengan mengukur nilai RSSI menggunakan software XCTU. Semakin kecil nilai Received Signal Strength Indicator (RSSI) dari nilai receive sensitivity Xbee maka kualitas sinyal semakin baik. Pengukuran dilakukan dengan meninggikan antena Xbee dengan beberapa variasi ketinggian untuk mendapatkan kualitas sinyal yang lebih baik. Hasilnya diperoleh beberapa rekomendasi tinggi minimal antena Xbee yang terpasang di tiap lokasi modul anak pada 3 kabupaten.

Summary of the stakeholders workshop to develop a National Volcano Early Warning System (NVEWS)

2006 ◽  
Author(s):  
Marianne Guffanti ◽  
William E. Scott ◽  
Carolyn L. Driedger ◽  
John W. Ewert
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System
Sign in / Sign up

Export Citation Format

Share Document