Previsione pericolosità GA 2015

Valutazione della pericolosità a scala di bacino!

F.Catani, A.Ba,s.ni, D.Lagomarsino, A.Rosi, G.Rossi, S.Segoni, N.Casagli!

Sommario

• Il sistema di allerta a 2 livelli • Il livello 1 a soglie pluviometriche • Il livello 2 con modello determinis7co • Lo strumento di monitoraggio • Risulta7 su casi reali in Toscana

Soglie di pioggia Modelli matema.ci

• Computa.onal .me

• Needed field data

+ +

• High spa.al resolu.on • High .me resolu.on

+ +

-‐ -‐ •  Spa.al resolu.on •  Time resolu.on

• Computa.onal .me

• Needed field data

-‐ -‐

Schemi di previsione frana

Primo livello

Output : Livelli di allerta aggregati bacini idrografici

Soglie pluviometriche locali

Output : mappe di probabilità di fattore di sicurezza in tempo reale

Secondo livello: modello di stabilità distribuito

Sistema a due livelli

Da. di Pioggia

AGuale

S.me da satellite + pluviometri + LAM

A breve

RADAR meteo + pluviometri + LAM

Codici di calcolo

Primo livello Secondo livello

Soglie di pioggia mul.ple

Analisi sta.s.ca dei da. pluviometrici Massive CUMulate Brisk Analyzer

• Relazione intensità-‐durata • Analisi automa7ca • Approccio standardizzato • Definizione di soglie locali • Bilancio tra falsi allarmi e manca7 allarmi

Primo livello di allerta

First level opera.onal chain

Mul.ple-‐thresholds and News event analysis

Rainfall database

Automated analysis

Landslide database

Threshold 1

Threshold i

Threshold n

Calibra.on Thresholds

Valida.on

Error analysis

NEWS DATA MINING

A PRELIMINARY STUDY!

•  Diameter à number of triggered landslides in a single rainfall event!•  Color à different provinces!

I=13.97D-0.62

Single-‐threshold analysis of I-‐D

Problem: general purpose threshold for EW à lowest envelope equation à false alarms !


general threshold for Tuscany (this preliminary study)

Single-‐threshols analysis of I-‐D

Problem: general purpose threshold for EW à lowest envelope equation à false alarms !

I=13.97D-0.62

A single regional threshold would be affected by a too large degree of

overestimation of hazard!

Example: rain gauge 077 Year 2008:

11 false alarms


Elevation (meters a.s.l)

Single-‐threshold analysis of I-‐D

RAINFALL DATABASE!

•  hourly rainfall measurements!•  332 automated rain gauges !•  data from the period 2000 – 2009 !

DATA ORGANISATION!


Mul.ple thresholds of I-‐D

25 Alert Zones (AZ)!

LANDSLIDES DATABASE!

•  2132 landslides, grouped into 408 events!•  accurate temporal and spatial location! !•  calibration dataset: period 2000 – 2007!•  validation dataset: period 2008 – 2013 !

DATA ORGANISATION!



Parameters used:!

ANALYSIS!

•  I = Critical rainfall intensity (mm/h)!•  D = Duration of critical rainfall (h)!•  AR = Antecedent rainfall (mm)!

Conceptual model – Intensity duration curves!

From (Aleotti 2004)


MACUMBA CODE!

• The triggering rainfall event is characterized in terms of: !

Duration (D)!!Intensity (I)!!60 days Antecedent Rainfall (AR)!! Two main issues:!!1.  Time shift within rain path!2.  Which recording is best

reproducing the triggering rain?!

!

Calculation of the critical parameters!

AR

D

I

Automa.on of I-‐D analysis

Parameters used:!

ANALYSIS!Analysis of pluviometric paths!

Time (hours)

Cum

ulat

ive

rain

(m

m)

I = Critical rainfall intensity (mm/h)!D = Duration of critical rainfall (h)!AR = Antecedent rainfall (mm)!

Two main issues:!!1.  Time shift within rain path!2.  Which recording is best

reproducing the triggering rain?!


Rainfall Event Splitting! Deep in-event analysis – SUB EVENT ANALYSIS!

main

1

3

2

• Calculate every sub-‐event I, D and return .me

• Compare sub-‐events and main event return .me

• Select the I -‐ D parameters associated to the highest return .me event or sub-‐event

Is it the main event actually the triggering one?!


The highest return time !among the nearest rain gauges to each landslide!

Automatic selection of the proper rain gauges!

Landslide

MACUMBA CODE!Automa.on of I-‐D analysis

MACUMBA CODE

•  I\D graph ! (logarithmic axes)!

•  Different color according to different amount of Antecedent Rainfall!

Intensity – Duration graph!


MACUMBA CODE

The procedure is repeated for each landslide within the same Alert Zone!

Iteration!

Each point represents a rainfall condition that has triggered at least one landslide in the past!


MACUMBA CODE

•  Classic statistical threshold tracing!•  Statistical threshold tracing with statistical predictor!

Automatic tracer of thresholds !

Power law!!

I = α D-β!


CALIBRATION!Rainfalls that did not trigger landslides!

Definition of threshold equations using data from 2000 to 2007.

Optimization of

prediction power with priority given to the reduction of missed

alarms.

Statistical threshold Statistical predictive threshold

Alert Zone A4: Low Serchio

Valley

Thresholds

VALIDATION!Validation period: Jan. 2008 – Jan. 2009!

Alert Zone A4: Low Serchio

Valley

4 events correctly predicted (14 landslides) 1 false alarm

No missed alarms

How many false alarms on an independent rainfall event sample?

Valida.on

VALIDATION!Alert Zone E3: Upper Arno Valley!

Correct predictions: threshold not exceeded, no landslides Correct predictions: threshold exceeded, occurrence of landslides

False alarms: threshold exceeded, no landslides Missed alarms: threshold not exceeded, occurrence of landslides

I = 41.64 D -0.85

JAN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

FEB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

MAR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

APR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

MAY 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

JUN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

JUL 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

AUG 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

SEP 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

OCT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

NOV 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

DEC 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

JAN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Valida.on

I = 79D-0.997

I = 32 D-0.846

0.1

1

10

100

1 10 100

Pioggia senza frane

Frane previste

Mancati allarmi

Falsi allarmi (elevata)

Falsi allarmi (moderata)

Alert Zone A2: Versilia

Rainfall without landslides

Correctly predicted landslides

Missed alarms

False alarms (high)

False alarms (moderate)

Different alert levels for each alert zone!

Large amount of

data needed especially for

landslide activations

On the basis of the number of

triggered landslides

Setup of alert system

t_start

t_end

cumulRain

rainMax

t_rainMax

maxSovrCum

t_maxSovrCum

t_start_Mob: time when event starts in the worst case maxSovrCumMob: maximum value t h a t e x c e e d t h e d a n g e r threshold (or minimum distance from) in the worst case t_maxSovrCumMob: time of occurrence of the previous value

t_start_Mob

Worst scenario research

Rainfall – threshold comparison

Early Warning System

Web-GIS interface – Warning system


Time of threshold exceedence

Web-GIS interface – Online event database and query system!


Landslide Event of 24-‐25 October 2010 Nothern Tuscany and Liguria Cinque Terre

Early Warning System – Actual example

Pontremoli Rain Gauge

370 mm total, max: 66 mm/h, 200 mm in 4 h

Landslide Event of 24-‐26 October 2010 Nothern Tuscany and Liguria Cinque Terre

Early Warning System – Actual example

Primo livello

Output : Livelli di allerta aggregati bacini idrografici

Soglie pluviometriche locali

Output : mappe di probabilità di fattore di sicurezza in tempo reale

Secondo livello: modello di stabilità distribuito

Sistema a due livelli

Second Level Determinis.c Model HIgh REsolu.on Slope Stability Simulator

Soil thickness

Geomechanical params

Morfology

P(FoS)

•  Physically based, high resolu.on model •  Large scale opera.vity •  Coded for real-‐.me applica.ons •  Fast parallel computa.onal scheme

On areas with Level-‐1 Alert

Hydrology

Model Structure and Governing Equa.ons

Hydrological Model

Slope Stability Model

Pore Pressure

Rainfall Intensity

Factor of Safety

∂h∂ t

dθdh

=∂∂ x

KL h( ) ∂h∂ x

− sinα%&'

()*

+

,-

.

/0 +

∂∂ y

KL h( ) ∂h∂ y%

&'(

)*+

,-

.

/0 +

∂∂ z

KZ h( ) ∂h∂ z

− cosα%&'

()*

+

,-

.

/0

h Z( ) = Zβ 1− dZ

#$%

&'(+ Z I

KZ

R tZ 2 / 4D0 cos

2α

#

$%&

'(*

+,

-

./

h Z( ) = Zβ 1− dZ

#$%

&'(+ Z I

KZ

R tZ 2 / 4D0 cos

2α

#

$%&

'(− R t − T

Z 2 / 4D0 cos2α

#

$%&

'(*

+,

-

./

FS = tanϕtanα

+c '

γ NSzsinα+ua − uw( ) tanϕ b

γ NSzsinα

FS = tanϕtanα

+c '

γ NS z − h( ) + γ Sh( )sinα−

h z,t( )γ w tanϕγ NS z − h( ) + γ Sh( )sinα

Unsaturated conditions

Saturated conditions

During rainfall

After rainfall

Hydrological model • Parallel code solution of Richards equations • Inclusion of hydraulic diffusivity in the model •  Real-time computational steps (during rainfall event)

Slope Stability Model • Infinite slope with distributed cell • Suction effects • Variable soil density with saturation • Variable depth analysis

Second Level Determinis.c Model

3100 Km2 - 10 m res = 5⋅107 pixels

Physical model

Monte Carlo simulations

(avg)

Multiple depth slope

stability calculation

24 h prediction at 1 h time step

5⋅1011 FLO

5⋅1014 FLO

3.6⋅1016 floating point operations

1.5⋅1015 FLO

Computa.onal issues Example for 1 rainfall event with dura.on 24 h, .me step 1 h


Supercomputers (HPC)

Multi-CPU workstation Shared memory

Hybrid or distributed memory

Up to 24 CPU

Some thousands of CPUs

•  Processor: IBM Power6 4.7 Ghz •  5376 CPU •  21 TB RAM •  1.2 PB hard disk space •  Internal network Infiniband x4 DDR

IBM SP6/5376

HIRESSS testing hardware


WEB Mobile devices

SERVER UNIFI

Monitoring system

min max

Level 1 – Cri.cality defini.on

Moderate High Ordinary None

Rela.ve triggering probability

Level 2

Monitoring system

Monitoring GUI

First level opera.onal chain

Mul.ple-‐thresholds and News event analysis

Rainfall database

Automated analysis

Landslide database

Threshold 1

Threshold i

Threshold n

Calibra.on Thresholds

Valida.on

Error analysis

NEWS DATA MINING

Con.nuous Valida.on and Upda.ng The News Search System

World Wide Web! Data Mining! Geocoded disaster database!Disaster News!

Presence in News Headlines Italian term for Landslide (“Frana”)

Presence in News Headlines English Term “Landslide”

WEB News

Broadcasted as FEED (units of informa.on)

RSS and XML Atom derivated

collected by FEED aggregators

Continuous data mining flow

Automated News Data Mining

CONTENUTO DELLA NOTIZIA

INDIVIDUAZIONE DEI TOPONIMI

(DB GEOITALY)

RANKING OF PLACE-NAMES

-  Uppercase characters; -  Surrounding words; -  Sentence positioning; -  Articles and prepositions; -  Possible generic meanings; -  Person names; -  Hierarchical chains; -  Number of citations; -  Existence of similar or identical place-names in DB;

GEOLOCATION OF MOST PROBABLE

PLACE-NAME

GOOGLE NEWS

CHECK SE GIÀ CATALOGATA

GOOGLE MAPS

GEOCODING

ANALISI DELLA NOTIZIA

Based on searching GEOLOCATION terms in the News Our case: Database GEOITALY with the following categories of toponyms: •  20 regions; •  110 provinces; •  8100 municipali.es; •  30983 loca.on names; •  461 rivers; •  206 lakes; •  1838 mountains.

Geoloca.on of News

NEWS ANALYSIS

CHECK IF ALREADY IN DB

PARSING OF NEWS CONTENT

EXTRACTION OF CANDIDATE GEOLOCATION TERMS

Each news is filtered and classified according to: •  Alleged relevance of the news; •  Magnitude of news (number of single FEEDS connected to the news);

•  Filtering of false alarms through nega.ve ranking keywords

Classifica.on and filtering of News

News and loca.on sources in Italy

News Search GUI

Landslide events found 2014

Landslide events found

2011-‐2014

Previsione pericolosità GA 2015

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