Natural Disaster Monitoring and Alert System Using Sensors to Save Lives Laércio M. Namikawa – Eymar Lopes Bilateral Research Workshop INPE – ifgi March.

Slides:



Advertisements
Similar presentations
Hydrological information systems Svein Taksdal Head of section, Section for Hydroinformatics Hydrology department Norwegian Water Resources and Energy.
Advertisements

FOSS4G 2009 Building Human Sensor Webs with 52° North SWE Implementations Building Human Sensor Webs with 52° North SWE Implementations Eike Hinderk Jürrens,
© Crown copyright Met Office Hazards – UK perspective 1 st Tech Workshop on Standards for Hazard Monitoring, Databases etc. Graeme Forrester, WMO Geneva,
Use of Cloud Computing Technology for Energy Efficiency Monitoring in Business and Industrial environment dr. Gregor Černe mag. Aleš Černivec Sandi Dolenc.
NCAR GIS Program : Bridging Gaps
Cracow Grid Workshop November 5-6 Support System of Virtual Organization for Flood Forecasting L. Hluchy, J. Astalos, V.D. Tran, M. Dobrucky and G.T. Nguyen.
Sensor & Computing Infrastructure for Environmental Risks SCIER FP IST-5 Stathes Hadjiefthymiades (NKUA) 1st Student Workshop on Wireless Sensor.
Division of Satellites and Environmental Systems Applications of GOES-SA (South America)
Esri International User Conference | San Diego, CA Technical Workshops | Esri Tracking Solutions: Working with real-time data Adam Mollenkopf David Kaiser.
Visualizing large spatial/temporal data sets An example from the European MARS project 15 May 2013, Hendrik Boogaard.
WMO / COST 718 Expert Meeting on Weather, Climate and Farmers November 2004 Geneva, Switzerland.
Dr. Sarawut NINSAWAT GEO Grid Research Group/ITRI/AIST GEO Grid Research Group/ITRI/AIST Development of OGC Framework for Estimating Near Real-time Air.
Securing Legacy Software SoBeNet User group meeting 25/06/2004.
Foundation for Space Science, Technology and Applications Vanildes Ribeiro - System Analyst – FUNCATE -
Common Alerting Protocol (CAP) Implementation Workshop – 2014 ArcGIS Geotrigger for CAP Implementation by Nalaka Kodippili Geo Technical Manager GIS Solutions.
A Global Agriculture Drought Monitoring and Forecasting System (GADMFS) Meixia Deng and Liping Di.
I’ve found the data; it’s free and open access. Now what? Gilberto Câmara National Institute for Space Research (INPE) Brazil.
BY:- RAVI MALKAT HARSH JAIN JATIN ARORA CIVIL -2 ND YEAR.
Geoscience WG update PRAGMA 25 Whey-Fone Tsai Yoshio Tanaka Sarawut Ninsawat Sornthep Vannarat.
Name, Surname, Position Logo(s) Weather monitoring and forecasting over eastern Attica (Greece) in the frame of FLIRE project Vassiliki Kotroni (1), Konstantinos.
DISASTER RISK REDUCTION COORDINATION MECHANISMS AND EARLY WARNING SYSTEMS National Legislation and Coordination Mechanisms The Case of Brazil Lauro T.
Wayne Faas Chief, NOAA National Climatic Data Center Data Operations Division December 3, 2003.
Sensors, SWE and European spatial data initiatives – INSPIRE and GMES Brno, Radim Štampach, Ph.D.
GIS in Weather and Society Olga Wilhelmi Institute for the Study of Society and Environment National Center for Atmospheric Research.
SHOWCASE EUROGRID Is there a need for a European core service component for high-resolution gridded climate data and products? Thomas Klein & Christer.
قسم الجيوماتكس Geomatics Department King AbdulAziz University Faculty of Environmental Design GIS Components GIS Fundamentals GEOM 121 Reda Yaagoubi, Ph.D.
Research Design for Collaborative Computational Approaches and Scientific Workflows Deana Pennington January 8, 2007.
Students: Anurag Anjaria, Charles Hansen, Jin Bai, Mai Kanchanabal Professors: Dr. Edward J. Delp, Dr. Yung-Hsiang Lu CAM 2 Continuous Analysis of Many.
MECHANISM OF PRODUCTION AND DISSEMINATION OF WARNINGS (Case of Meteo - Rwanda) By Marcellin HABIMANA.
Flash Floods in a changing context: Importance of the impacts induced by a changing environment.
Flash Flood Forecasting as an Element of Multi-Hazard Warning Systems Wolfgang E. Grabs Chief, Water Resources Division WMO.
Jonas Eberle 25th March Automatization of information extraction to build up a crowd-sourced reference database for vegetation changes Jonas Eberle,
A Multi-level Data Fusion Approach for Early Fire Detection Odysseas Sekkas Stathes Hadjiefthymiades Evangelos Zervas Pervasive Computing Research Group,
GO-ESSP Workshop, LLNL, Livermore, CA, Jun 19-21, 2006, Center for ATmosphere sciences and Earthquake Researches Construction of e-science Environment.
Strategic Plan for HEPEX John Schaake, Eric Wood and Roberto Buizza AMS Annual Meeting Atlanta February 2, 2006.
Accessing and Using Fire-Related Data with the CAPITA DataFed.net* Services Framework Stefan Falke Rudolf Husar Kari Hoijarvi Washington University in.
International Institute for Geo-Information Science and Earth Observation (ITC) ISL 2004 RiskCity Exercise: Spatial Multi Criteria Evaluation for Vulnerability.
New Fire Weather System Bernard Miville Manager of Operational Forecasting.
Development of a Geographic Framework for an Integrated Flood Modeling System Oscar Robayo Tim Whiteaker August 10, 2004 University of Texas at Austin.
Early Warning System in Thailand by Prawit JAMPANYA Weather Forecast Bureau Thai Meteorological Department Ministry of Information and Communication Technology.
Shinobu Kawahito JAXA / RESTEC Update on Application Prototyping using OGC Servers.
Reunión Gestdropper integration in the municipal structure of Vitoria - Gasteiz IRRIGESTLIFE_LIFE11 ENV/ES/615 GestDropper.
1 86 th Annual American Meteorological Society Meeting Atlanta, Georgia January 29 – February 2, 2006 The Severe Weather Data Inventory (SWDI): A Geospatial.
Students: Aiman Md Uslim, Jin Bai, Sam Yellin, Laolu Peters Professors: Dr. Yung-Hsiang Lu CAM 2 Continuous Analysis of Many CAMeras The Problem Currently.
Asia Flood Network— A USAID Program for Flood Mitigation and Preparedness in Asia Asia Flood Network Program Objective –Identify and fill gaps in end-to-end.
→ MIPRO Conference,Opatija, 31 May -3 June 2005 Grid-based Virtual Organization for Flood Prediction Miroslav Dobrucký Institute of Informatics, SAS Slovakia,
Availability of data for climate change impact indicators 4 EIONET WORKSHOP 1 July 2010, Brussels Maria Khovanskaya Climate Change Topic Area Regional.
The DEWETRA platform An advanced Early Warning System.
Data Assimilation Decision Making Using Sensor Web Enablement M. Goodman, G. Berthiau, H. Conover, X. Li, Y. Lu, M. Maskey, K. Regner, B. Zavodsky, R.
Corn Yield Comparison Between EPIC-View Simulated Yield And Observed Yield Monitor Data by Chad M. Boshart Oklahoma State University.
Collaboration with NCAR Aug. 15, OutlineOutline 1. SMB in brief 2. The responsibility of SMB in Expo The requirements of SMB 4. The potential.
OSSIM Technology Overview Mark Lucas. “Awesome” Open Source Software Image Map (OSSIM)
Latin American and Caribbean Flood and Drought Monitor Colby Fisher, Nathaniel Chaney, Justin Sheffield, Eric F. Wood Princeton University … with support.
16-1 PC-HYSPLIT WORKSHOP Workshop Agenda Introduction to HYSPLIT Introduction.ppt Model Overview Model_Overview.ppt Meteorological Data Meteorological_Data.ppt.
Collaborative intelligence applied to early warning systems of natural disasters in Brazil.
Fire, Smoke & Air Quality: Tools for Data Exploration & Analysis : Data Sharing/Processing Infrastructure This project integrates.
A microcontroller-based system for multi sensor monitoring and messaging via GSM network Bachelor thesis Angelakis Vaios Supervisor:Kazarlis S.
Operational flash flood forecasting based on grid technology Monitoring and Forecasting Thierion Vincent P.-A. Ayral, V. Angelini, S. Sauvagnargues-Lesage,
SmartMet Lea Saukkonen FMI What is SmartMet? A software tool for visualizing and editing meteorological data.
Serving Iowa Mesonet data with U of Minnesota’s MapServer Daryl Herzmann Iowa Environmental Mesonet 31 Jul 2002.
Architectural Description The Wind application is based on the JDDAC platform. The system is comprised of a network of weather stations responsible.
Introducing sferic maps & Mobile
Integrating ArcHydro and HEC Models by David R
Daniel Vila, Luiz A. Toledo Machado
Hydrometeorological Service R. Macedonia
National Early Warning and Monitoring Centre of Natural Disasters - CEMADEN Osvaldo Moraes
Climate Change & Environmental Risks Unit Research Directorate General
Elaine B. Darby GIS – Fall 2005
Red Sky Update “Watching the horizon for emerging health threats”
Reportnet 3.0 Database Feasibility Study – Approach
Presentation transcript:

Natural Disaster Monitoring and Alert System Using Sensors to Save Lives Laércio M. Namikawa – Eymar Lopes Bilateral Research Workshop INPE – ifgi March 2009

SIStema de Monitoramento e Alerta de DEsastres Naturais Version 1.0Version 2.0 Released July/11/2008July/ Natural Disaster Monitoring and Alert System

reference: Adapted from GEO BRASIL Perspectivas do Meio Ambiente no Brasil – Edições Ibama, Natural Disasters in Brazil Forest fires Flooding Droughts Landslides Flooding Landslides Forest fires Flooding High winds Hail storms 12 Oil refineries 4 Petrochemical Complexes

Natural Disasters in Brazil Santa Catarina – Nov. 2009

Natural Disasters in Brazil Source (adpated): Vulnerabilidade Ambiental / Rozely Ferreira dos Santos, organizadora. – Brasilia: MMA, p. : il. color. ; 29 cm. Droughts Floods Epidemic Landslides Extreme temperature High winds

Center for Weather Forecast and Climate Studies

CPTEC Weather ForecastsCPTEC Weather Forecasts

CPTEC Observations Satellite

CPTEC Observations Radar

CPTEC Observations Data Collection Platforms

CPTEC Emergency Actions For Preparation and Mitigation in States and Municipalities National Secretariat for Civil Defense Regional and Municipalities Civil Defenses State Coordination for Civil Defense- SP CPTEC Alerts Civil Defense is notified when forecast indicates that intense or long lasting rain has potential to trigger natural disastres

Technological Support TerraLib: Geographical database and spatial operation by TerraLib:

Spatial Analysis AdditionalInformation Natural Disasters Risks Alerts Hidrology and Meteorology Observations and Forecasts Extreme Event Risk Areas Automatically Generated AlertsAutomatically Generated Alerts

Open Source Computational System based on service oriented architecture Provides technological infrastructure to develop operational systems to manage alerts of environmental risks Services: Data gathering and formatting Analysis by comparison with risk layers or by executing models Risk model edition for alerts Alert handling and management SIStema de Monitoramento e Alerta de DEsastres Naturais Natural Disaster Monitoring and Alert System

Natural Disasters Monitoring and Alert System

Configuration Interface Register data servers and sources Register risk maps and base maps Program analysis Register users and permissions

Configuration Interface Climate data Risk Maps Base Maps Analyses Users Add Server Main Menu

Data from CPTEC CPTEC Server ModelsSatellite/RadarDCP Rain total Fixed time and irregular – alert Point data One file per DCP Grid 4km Total rain 1h Total rain 24h Current (mm/h) Binary file ETA 40, 20, 5 Km Ensemble 40 Km Total rain 72h 72 files ASCII grid file Configuration Interface

Hidrometeorological Servers FTP File ftp:// / c:\data\grids Rain/bingrd Configuration Interface

Hydrometeorological Data Series Grids Hydroestimator Lightning Radar Forecast Models Points DCPs (data collection platforms) Configuration Interface

Hydrometeorological Grid Data Series ASCII-GRID PCD TIFF GrADS Forecasted Total Others Radar.%a%M%d.%h%m.tif Configuration Interface

Hydrometeorological Grid Data Series New Version Extreme event threshold Save storage and analysis Area clipping Storage strategy Delete unnecessary data New formats Cumulative data. Ex: File with rain for every hour during forecast period (72 hours) Configuration Interface

Point Data Series - DCPs DCPs Location Pre calculation of data series DCP Configuration Interface

DCPs Point Data Series Pre calculation of new value to be used in the analysis Configuration Interface Lua Collect Rules by Lua

Included in database through Risk layers Areas (polygons) + attributes describing the risk Base map layers - Vector or grid layers supporting visualization in alert situations Configuration Interface Register risk maps and base maps

Risk Maps Polygons with attributes that specify risk levels Configuration Interface

Programming the Analysis Lua User analysis and risk model programming by Lua

Risk Analyses Analysis Configuration Interface

Analysis Types Using risk maps Executes analyses by areas in risk maps overlaid on hidrometeorological creating alerts in these areas. Model based Ex: SINMAP (Stability INdex MAPping) c – Coesion; β- Slope angle; φ- Soil internal friction; R/T- Reload/transmissivity ratio; a- Watershed area Configuration Interface

Analysis Based on Risk Maps Risk Layers Climate Data Configuration Interface

Analysis Based on Risk Maps LUA programming language LUA operators: arithmetic: + - * / ^ relational: == ~= = logical: and or not mathematical: math.abs math.acos math.asin math.atan …. TerraLib operators: – zonals: maximo minimo media conta_amostras – historical: operador_historico – grid: amostra Configuration Interface

Example - Risk Model Using Hydrometeorological Data in a Grid local ca = maximo(‘total rain') if ca < limiar_ca then return 0 -- Ok elseif ca < limiar_ca * 1.2 then return 2 – Attention level else return 4 – Maximum Alert level end Configuration Interface

Example - Risk Model Using Point Data (DCPs) local rain = media(‘dcp_data’,'pluvio') if rain == nil then rain = media('rain_grid') end if rain < threshold then return 0 -- Ok elseif rain < threshold * 1.2 then return 2 – Attention level else return 4 – Maximum alert level end Configuration Interface

Model Based Analysis Output Grid Climate Data Configuration Interface

Model Based Analysis amostra – value at evaluation location on grid local slope = amostra('slope_grid') local rain = amostra(‘rain_grid') local c= 0.37, B= 34, T= return FS = (c*cos^2.slope(1-(rain*a)/T *sin.slope)r )tan.B)/ sin.slope Configuration Interface

Risk Analyses New Version Use grids in addition to polygons to define risk areas Define influence areas of DCPs Classify analyses as active, inactive, and conditional (activated by another analysis) Configuration Interface

Risk Analyses Stretched Version Functions to validate DCPs data External simulation activated by a risk analysis TerraME program, FORTRAN program, Hidrological modeling Integration with TerraME, TerraHidro TerraHidro – Process Models on generalized flows Configuration Interface

Alert and Base Map Overlay Configuration Interface

Register Users and Analysis Based Permissions

Analysis based Permissions Configuration Interface Register Users for Each Analysis

Presentation Interface Users access alerts on internet through login (password required) Visualize current analysis Visualize risk polygons attributes Visualize a polygon risk history

WEB Main Interface - TerraPHP Presentation Interface

Alert Information Presentation Interface

Alert Events per Region Presentation Interface

Conclusions Processes extremes captured by risk analyses Vulnerabilities Processes values captured by images and DCPs Easy programming (non-experts) of input processing and risk analysis Data availability, censorship

Future Developments Capturing processes relevant changes Validation of DCPs data for a given process Finding and filling gaps in data Using randomly available data Collaborative data: Mobile phone (GPS, images, videos), web cam Use of OGC Sensor Observation Service standard

DCPs Errors

Thank You!