implementation_example.py

import numpy as np
from flood_protection_model import flood_protection_modelling
from vulnerability_model import vulnerability_modelling

## Year of the event
E_year = 2020
## Return period of the event in years
RPv1_event = 14
## List of return periods by region in years
RPv1_region = [10, 25, 15]
## List of NUTS3 regions affected
Regions_model = ['FR531', 'FR532', 'FR612']
## Duration of the event in days
Duration_model = 15
## Duration of the event in individual regions in days
Duration_region = [5, 15, 10]
## Average annual potential modelled economic loss relative to regional GDP
Eco_risk = 0.02
## Floods in previous 20 years in the event area
Past_events_number_20y = 3
## Floods in previous 30 years in the event area
Past_events_number_30y = 5
## Floods in previous 30 years per region
Past_events_number_region = [2,4,5]
## potential modelled fatalities of the event relative to regional population
Fat_potential = 0.005
## potential modelled population affected of the event relative to regional population
Pop_potential = 0.04
## potential modelled economic loss of the event relative to regional population
Eco_potential = 0.03
## Average water depth in m
Average_water_depth = 0.8

## Data of the vine model
Vine_data_fp = np.load('Vine_model_data_fp.npy', allow_pickle=True)
Vine_data_vn = np.load('Vine_model_data_vn.npy', allow_pickle=True)

FP_P, FP_regio = flood_protection_modelling(E_year, RPv1_event, RPv1_region, Regions_model, Duration_model,
                               Duration_region, Eco_risk, Past_events_number_20y,
                               Past_events_number_region, Vine_data_fp)

RelLoss, RelLoss_unc = vulnerability_modelling(Regions_model, Past_events_number_20y, Past_events_number_30y,
                                               RPv1_event, Eco_risk, Fat_potential, Pop_potential, Eco_potential,
                                               Average_water_depth, E_year, Vine_data_vn)