9. Sensitivity analysis

Bridge sensitivity analysis

gmtra.sensitivity.regional_bridge(file, data_path, param_values, income_lookup, eq_curve, design_tables, depth_threshs, wind_threshs, rail=False)

Function to estimate the summary statistics of all bridge damages in a region

Arguments:

file : path to the .feather file with all bridges of a region.

data_path : file path to location of all data.

param_values : A NumPy Array with sets of parameter values we would like to test.

income_lookup : A dictionary that relates a country ID (ISO3 code) with its World Bank income goup.

eq_curve : A pandas DataFrame with unique damage curves for earthquake damages.

design_table : A NumPy array that represents the design standards for different bridge types, dependent on road type.

depth_thresh : A list with failure depth thresholds.

wind_threshs : A list with failure wind gustspeed thresholds.

Optional Arguments:

rail : Default is False. Set to True if you would like to intersect the railway assets in a region.

Returns:

DataFrame : a pandas DataFrame with summary damage statistics for the loaded region.

Cyclone damage sensitivity analysis

gmtra.sensitivity.regional_cyclone(file, data_path, events, param_values, rail=False)

Function to estimate the summary statistics of all cyclone damages in a region to road assets

Arguments:

file : path to the .feather file with all bridges of a region.

data_path : file path to location of all data.

events : A list with the unique cyclone events.

param_values : A NumPy Array with sets of parameter values we would like to test.

Optional Arguments:

rail : Default is False. Set to True if you would like to intersect the railway assets in a region.

Returns:

DataFrame : a pandas DataFrame with summary damage statistics for the loaded region.

Earthquake damage sensitivity analysis

gmtra.sensitivity.regional_earthquake(file, data_path, global_costs, paved_ratios, events, wbreg_lookup, rail=False)

Function to estimate the summary statistics of all earthquake damages in a region to road assets

Arguments:

file : path to the .feather file with all bridges of a region.

data_path : file path to location of all data.

global_costs : A pandas DataFrame with the total cost for different roads in different World Bank regions. These values are based on the ROCKS database.

paved_ratios : A pandas DataFrame with road pavement percentages per country for each road type.

events : A list with the unique earthquake events.

wbreg_lookup : a dictioniary that relates countries (in ISO3 codes) with World Bank regions.

Optional Arguments:

rail : Default is False. Set to True if you would like to intersect the railway assets in a region.

Returns:

DataFrame : a pandas DataFrame with summary damage statistics for the loaded region.

Flood damage sensitivity analysis

gmtra.sensitivity.regional_flood(file, hazard, data_path, global_costs, paved_ratios, flood_curve_paved, flood_curve_unpaved, events, wbreg_lookup, rail=False)

Function to estimate the summary statistics of all flood damages in a region to road assets

Arguments:

file : path to the .feather file with all bridges of a region.

data_path : file path to location of all data.

global_costs : A pandas DataFrame with the total cost for different roads in different World Bank regions. These values are based on the ROCKS database.

paved_ratios : A pandas DataFrame with road pavement percentages per country for each road type.

flood_curve_paved : A pandas DataFrame with a set of damage curves for paved roads.

flood_curve_unpaved : A pandas DataFrame with a set of damage curves for unpaved roads.

events : A list with the unique flood events.

wbreg_lookup : a dictioniary that relates a country ID (ISO3 code) with its World Bank region.

Optional Arguments:

rail : Default is False. Set to True if you would like to intersect the railway assets in a region.

Returns:

DataFrame : a pandas DataFrame with all damage results for the loaded region per road or railway asset type.