A Prelimilary Study of the Rainfall Threshold and Early Warning System for Landslide in Taiwan

2015 ◽  
pp. 1571-1574 ◽  
Author(s):  
Lun-Wei Wei ◽  
Ching-Fang Lee ◽  
Chuen-Ming Huang ◽  
Wei-Kai Huang ◽  
Hsi-Hung Lin ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Threshold

scholarly journals Early warning system for shallow landslides using rainfall threshold and slope stability analysis

2018 ◽  
Vol 9 (6) ◽  
pp. 1871-1882 ◽  
Author(s):  
Shruti Naidu ◽  
K.S. Sajinkumar ◽  
Thomas Oommen ◽  
V.J. Anuja ◽  
Rinu A. Samuel ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Threshold ◽  
Slope Stability Analysis ◽  
Shallow Landslides

A downscaling model system for early warning flooding forecast

2021 ◽  
Author(s):  
Francesco Cioffi ◽  
Federico Rosario Conticello ◽  
Mario Giannini ◽  
Tommaso Lapini ◽  
Sergio Pirozzoli ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Tropical Cyclone ◽  
Early Warning ◽  
Early Warning System ◽  
Large Scale ◽  
Warning System ◽  
Rainfall Threshold ◽  
Range Data ◽  
Tropical Regions ◽  
Global Circulation Models

<p>A     recent report “The Future is Now: Science for Achieving Sustainable Development” Global Sustainable Development Report 2019 - SDG Summit’      as part of the activity of Agenda 2030 of UN, highlights the opportunity to develop Early warning system for drought, floods and other meteorological events, that by providing timely information can be used by vulnerable countries to build resilience, reduce risks and prepare more effective responses. Following the suggestion,      combining outputs from Global Circulation models, remote sensing, hydraulic models and machine learning tools,       a local scale flooding Early Warning System (EWS) is proposed for the St. Lucia island (     Caribbean). The objective of the EWS is to provide forecasts of potentially dangerous flooding phenomena at different time scale: a) 0-2 hours, nowcasting; b) 24-48 hours, short range; c) 3-10 days, middle to long range. Data used to build the model are: Geopotential Height (GPH) fields at 850 hPa and Integrated Vapor Transport (IVT) fields from European Centre for Medium-range Weather Forecasts (ECMWF) - Reanalysis v5 (ERA5); Tropical Cyclone tracks from NOAA-NHC; 18 weather stations homogeneously distributed in the island; rainfall map data from the weather radar in Saint Lucia. GPH and IVT fields were defined between 110°W - 10°W and 45°N - 10°S. The EWS is constituted by an ensemble of flooding risk forecast subsystems which is potentially applicable to Atlantic tropical and extra-tropical regions. Different approaches are used for each subsystem      to link large scale atmospheric features to local rainfall and flooding: a) Non-homogeneous Hidden Markov and Event Synchronization models to translate IVT and GPH at 850 hPa  fields (from ECMWF-Set II- Atmospheric Model Ensemble) in local      daily rainfall amount and probability of  exceedance of  a prefixed heavy rainfall threshold; b) a physical based cyclone/rainfall  model to convert      Tropical cyclone attributes – position and      maximum wind      velocity       (provided from National Hurricane Center)- in rainfall intensity spatial distribution on the island; c) a surrogate model for a  fast and accurate prediction of flooding events that is obtained from a multi-layer perceptron neural network (MLPNN), which is trained on a high-fidelity dataset relying on solution of the full two-dimensional shallow water equations with direct rainfall application.        Results show an excellent ability of the models to identify the climatic configurations that determine the occurrence of extreme events and the exceeding of threshold values ​​that generate floods. In particular, during the late hurricane season September-October-November, when is highest the probability of flood events, the EWS was able to forecast the occurrence of critical climatic configurations 86% of the times they occurred. The EWS was able to predict the exceeding of the rainfall threshold that generated floods 80% of times.</p>

scholarly journals Rainfall–landslide early warning system (RLEWS) using TRMM precipitation estimates

2019 ◽  
Vol 13 (3) ◽  
pp. 1259
Author(s):  
Norsuzila Ya’acob ◽  
Noraisyah Tajudin ◽  
Aziean Mohd Azize
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Threshold ◽  
Residential Areas ◽  
Diagram Method ◽  
Precipitation Estimates ◽  
Accumulated Rainfall ◽  
Landslide Early Warning ◽  
Trmm Precipitation

<span lang="EN-MY">This paper presents Rainfall–Landslide Early Warning System (RLEWS) using Tropical Rainfall Measuring Mission (TRMM) precipitation estimates to notify the warning level for the possibility of landslide occurrences in Ulu Kelang, Selangor. In this study, RLEWS is developed to monitor the possibility of rainfall-induced landslide occurrences by comparing real time TRMM rainfall data with a landslide rainfall threshold. The landslide rainfall threshold is constructed by using the accumulated rainfall-accumulated rainfall (E-E) diagram method. The warning levels of rainfall threshold are classified into three levels; high, moderate and low. The analysis and notification are updating every 24 hours to provide the initial potential landslide information signal. The rainfall threshold analysis was able to detect the early signal of initial potential landslide occurrences. The aims of this study are to develop a low-cost, sustainable early warning system and web base application to send notification and awareness for residential areas in Ulu Kelang, Selangor.</span>

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
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