Data science appliquée au climat
Arthur Charpentier (UQAM & Univ. Rennes)
MAIF Data Days, Novembre 2022

November 15, 2022

@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

1 / 30Arthur Charpentier
Université du Québec à Montréal@freakonometrics
freakonometrics
freakonometrics.hypotheses.org
Modélisation prédictive, Science actuarielle,Économie mathématique, Risque, Inégalités,
Économétrie, statistiques, apprentissage automatiqueModélisation du climat, Extrêmes, Équité@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

2 / 30Data science (individuel vs temporel) [1]x1x2x3temps

yx4

yt−2 yt−1yt

yt+1modèlemodèle@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

3 / 30Data science (individuel vs temporel) [2]x1,i ′ x2,i ′ x3,i ′ x4,i ′yi ′x1x2x3x4y?

modèlex1,i x2,ix3,ix4,iyix1,i+1 x2,i+1 x3,i+1 x4,i+1 yi+1

@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

4 / 30Data science (individuel vs temporel) [3]yt−1yt+1yt−2 yt−1yt−2yt

yt

yt+1?

modèleOn suppose que la dynamique liant yt+1 et son passé se réplique dans le temps
Mais il est aussi possible de mélanger données spatiales et temporelles...

@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

5 / 30Data science (individuel vs temporel) [4]x1,i ′ x2,i ′ x3,i ′ x4,i ′yi ′x1,i ′ x2,i ′ x3,i ′ x4,i ′yi ′x1,i ′ x2,i ′ x3,i ′ x4,i ′yi ′?

date t − 1@freakonometrics

freakonometrics

date tdate t + 1

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

6 / 30La dynamique des catastrophes naturelles [1][1]

A. Charpentier. “On the return period of the 2003 heat wave”. In: Climatic change 109.3 (2011),
pp. 245–260.

[2]

A. Charpentier and D. Sibaı̈. “Dynamic flood modeling: combining Hurst and Gumbel’sapproach”. In: Environmetrics 20.1 (2009), pp. 32–52.

@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

7 / 30La dynamique des catastrophes naturelles [2]@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

8 / 30La dynamique des catastrophes naturelles [3]“seismic gap hypothesis” / dynamique des inondation / persistence de la température@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

9 / 30Les inondations France [1][1]

A. Charpentier, L. Barry, and M. James. “Insurance against Natural Catastrophes: Balancing
Actuarial Fairness and Social Solidarity”. In: Geneva Papers on Risk & Insurance (2021). doi:
10.1057/s41288-021-00233-7.

[2]

France Info. “Seine-et-Marne : la ville de Thoméry se remet progressivement des inondations”.
In: (2018). url: http://tinyurl.com/mtc9tprm.

@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Les inondations France [2][1]A. Charpentier, L. Barry, and M. James. “Insurance against
Natural Catastrophes: Balancing Actuarial Fairness and Social Solidarity”. In: Geneva Papers on Risk & Insurance(2021). doi: 10.1057/s41288-021-00233-7.

Sur la solidarité du systèmeConstitution Française (1946)
12. La Nation proclame la solidarité et l’égalité detous les Français devant les charges qui résultent
des calamités nationales.
82-600 Law (1982)
régime d’indemnisation des catastrophes naturelles@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Les inondations France [3]source: https://www.ccr.fr/en/-/indemnisation-des-catastrophes-naturelles-en-france@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Les inondations France [4]Deux types d’inondation analysées ici: par débordement (de rivière) et costale
PPRIs (plan de prévention du risque inondation) à Roquebrune-sur-Argens, Puget etSaint-Raphaël. Les zones bleues sont reconnues comme “à risque” (sur la based’évènements passés)@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Les inondations France [5][1]France 3. “Tempête Xynthia: la responsabilité de La Faute-sur-Mer pourrait être alourdie”. In:(2019).

@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Les inondations France [6]PPRLs (plan de prévention des risques littoraux) en Vendée. L’aire hachurée est lazone à risque.

Voir aussi  https://github.com/freakonometrics/floods@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

15 / 30Les inondations France [7]10% des polices d’assurances concentrent 73.6% des pertes... qui vit dans les zones àrisque ?
Base des “ventes de biens fonciers”, https://cadastre.data.gouv.fr/dvf, 2014-2018,(biais possible, 5 ans de transactions notarielles)@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Les inondations France [8]E.g. dans 4 départements (Loire-Atlantique, Vendée, Oise, Isère)• maison & apartement vendu, ■ PPRI-PPRL@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Pays-LoireVendée

Les inondations France [9]Non risky
Risky
Non risky
Risky

Apartments
HousesApartments
HousesApartments
HousesApartments
HousesAverage
Price
4293
2928
3302
10253
4399
3019
6784
3245

Difference(%)-23%+250%+54%+7%Maximum
Price2184065909
9773
71483
79913
75472
68478
22895

Number
329
2795
39637
8411
12678
1001
765

Proportion
(%)
9%
74%
1%
17%
37%
55%
4%
3%

Welcht test

Table 1: Prices (e per m2 ) of houses sold (2014-2018) for Vendée - Western part of France,with PPRL (coastal risk). The Difference is the relative difference between average prices (perm2 ) between the risky and the non-risky zones, either for apartments or houses.

@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 202218 / 30

1.0-60.1-8.6-2.7

Les inondations France [10]Var

Non risky
Risky

Seine etMarne

HauteLoire

Non risky@freakonometrics

Risky
Non risky
Risky

freakonometrics

Apartments
HousesApartments
HousesApartments
HousesApartments
HousesApartments
HousesApartments
HousesAverage
Price
5392
5957
4190
4172
2399
1314
2163
1247
6260
3356
4333
2693

Difference(%)

MaximumPrice-22%-30%-11%-5%-30%-20%

38333
20625
28125
7432
79710
79167
40000
54096

Number
9874
6913
1471
226
3403
8857
319
272
82133
98824
2177
1784freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

Proportion
(%)
53%
37%
8%
1%
27%
69%
2%
2%
44%
53%
1%1%

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Welcht value

6.4
5.2

1.6
0.9

8.0
7.5

Les inondations France [11]IsèreNon risky
Risky

Oise

Non risky
Risky

Apartments
HousesApartments
HousesApartments
HousesApartments
HousesAverage
Price
4960
2429
3252
2543
6170
3126
5725
2866

Difference(%)-3%+5%-7%-8%

Maximum
Price
79800
69375
35714
14067
79963
78214
50000
62184

Number
27982
24600
885
43524613
44737
1385
1640

Proportion(%)
52%
45%2%1%
34%
62%
2%
2%

Welcht value

Table 2: Prices (e per m2 ) of houses sold (2000-2020) for several départements in France, withPPRI (overflow risk, or non-costal).

@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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6.1
-1.2

2.1
4.6

Risque de sécheresse (subsidence) en France [1][1]

A. Charpentier, M. R. James, and H. Ali. “Predicting Drought and Subsidence Risks in France”.
In: Natural Hazards and Earth System Sciences 22 (2022), pp. 2401–2418. doi:
10.5194/nhess-22-2401-2022.

[2]

France Bleu. “La sécheresse coûte de plus en plus cher en assurances”. In: (2019). url:
http://tinyurl.com/yeyr67xu.

@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Risque de sécheresse (subsidence) en France [2][1]A. Charpentier, M. R. James, and H. Ali. “Predicting Drought and Subsidence Risks in France”.
In: Natural Hazards and Earth System Sciences 22 (2022), pp. 2401–2418. doi:
10.5194/nhess-22-2401-2022.

“La ubsidence est causée par le retrait et le gonflement des sols argileux.”
Facteur géotechnique: Superficie des communes à risque moyen ou élevé > 3%.
(catégories basées sur la concentration d’argile dans le sol et les relevéshistoriques)
Facteur météorologique: Indice standardisé d’humidité du sol (SSWI), si un
indicateur de la saison est inférieur à une période de retour de 25 ans, alors toutela saison est éligible pour la commune concernée.

@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Risque de sécheresse (subsidence) en France [3]Données 1989-2018@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Risque de sécheresse (subsidence) en France [4]Indicateurs 2020: ESPI (précipitations) ESSTI (température du sol) & ESSWI(humidité du sol), ERA5-Land 9 km × 9 km

(via ESDAC (European Soil Data Centre) pour la concentration des sols)@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Risque de sécheresse (subsidence) en France [5]Quelques prédictions de fréquence de sinistre par différents modèles@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Risque de sécheresse (subsidence) en France [6]2017, random forest Poisson, zero inflated, observé, arrêté Nat Cat@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Risque de sécheresse (subsidence) en France [7]2018, random forest Poisson, zero inflated, observé, arrêté Nat Cat@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Feux de forêt (“wildfire”) au Québec (Canada) [1][1]

A. Benchallal, Y. Bouroubi, and A. Charpentier. “Human-caused wildland fire occurrence
prediction over the province of Quebec using mahine learning algorithms and free geospatial
datasets”. In: 10th International Conference on Agro-Geoinformatics and 43rd Canadian
Symposium on Remote Sensing (2022).

[2]

France Info. “Canada : le ”dôme de chaleur” provoque de violents incendies à Lytton”. In: (2021).

@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Feux de forêt (“wildfire”) au Québec (Canada) [2]@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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Feux de forêt (“wildfire”) au Québec (Canada) [3]@freakonometrics

freakonometrics

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, Novembre 2022

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