The SISTEMATI project

Paolo Arcaini, Gloria Bordogna, Elisabetta Mangioni, ChrysanthiPolyzoni, Simone Sterlacchini

National Research Council of Italy (CNR)Institute for the Study of the Dynamics of Environmental Processes (IDPA)

GIT 2013 – 17th June, 2013

SISTEMATI

Strumenti Informatici per lo Studio e il Trattamento di EmergenzeAmbientali

A Project of Industrial Research for the experimental development aspart of the strategic sections of ”Regione Lombardia” and ”MIUR”(DECRETO N. 7128 DEL 29 LUGLIO 2011) Critical InfrastructureResilience and Emergency Management

Official starting date of the project: June 2012

Effective starting date of the project: January 2013

Project Duration: 24 months

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Project aims

Risk Forecast and warning news against:

Forest Fire Propagation

Extreme Atmospheric Events (Wind, Rainfall, Temperature)

Pollutant and harmful substance emission over the atmosphere as aresult of a forest fire or intentional acts, both in industrial field andnot.

Tha main target is the defence of the population, the environment andCivil Infrastructures (electric cables, antennas and communicationstructures)

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The Fire Propagation Model

We use a Bayesian probabilistic Model named Weights of Evidence testingtwo different themes

Dynamic themes provided by EPSON MeteoI TemperatureI HumidityI Wind VelocityI Wind DirectionI Cumulative Precipitation

Static themesI SlopeI AspectI DEM

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Conceptual Schema

Model acquisition Training Points Adjacent Province Training Points Negative Model Test Training Points Model Test Training Points

Static Social Data (50m)

Population density

Road Network

ZPS (Zone Di Protezione

Speciale)

Static Envinromental Data

(50m)

Slope

Aspect

Dem

Dynamic Meteorological Data (5km)

Mean Monthly Temperature

Mean Monthly Humidity

Mean Monthly Wind Velocity

Mean Monthly Wind Direction

Monthly Cumulative Precipitation

Static Probability Map Dynamic Probability Map

Fire Probability Map

Receiver Operator curve

(ROC)

Success-rate curve

(SRC)

Prediction-rate curve

(PRC)

Additional spatial test

Accuracy of the Probability Map

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SISTEMATI framework – VGI retrieval and querying

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VGI retrieval and querying – Query types

Category search Full text search

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VGI retrieval and querying – Query types

Spatial (fuzzy) search Temporal (fuzzy) search

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VGI retrieval and querying – Query answer

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VGI clustering

Based on DBSCAN

Clusters possibly non-convex

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VGI clustering – Spatial clusteringMax distance from at least another report in the cluster: 18 km

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VGI clustering – Temporal clusteringMax distance from at least another report in the cluster: 10 h

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VGI clustering – Spatio-temporal clusteringMax distance from at least another report in the cluster: 16 km, 5 days

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Querying susceptibility maps

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Querying susceptibility maps – Kinds of queries

direct query on current parameters: soft conditions on the currentparameters

I ”the temperature has been hot in the last week, the rainfall has beenlow in all the previous month and the wind yesterday was blowing inNS direction with a high speed”

direct query on trend: increasing/decreasing trend of the parametersI ”the temperature has been mildly hot in the last month, hot in the last

week and very hot yesterday”

inverse query: a susceptibility level (or an interval of consecutivesusceptibility levels) in a (sub)region with the aim of retrieving thevalues of the parameters that more likely produce the specifiedsusceptibility level in the specified (sub)region.

I “what are the values of the dynamic parameters that more likelyproduce a susceptibility level greater than medium?”.

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SISTEMATI framework

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pgModeler, PostgreSQL 9.1 + PostGIS, pgAdmin

Inheritance

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Geographic information system I

Quantum GIS

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Geographic information system II

Qt4 + pyQt

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Geographic information system III

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Workflow tool

Platform Independent Petri net Editor

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Workflow tool: our objective

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