# Modeling Alabama Tornado Emergency Relief (MATER) Joe Cordell Spencer Timmons Michael Fleischmann.

## Presentation on theme: "Modeling Alabama Tornado Emergency Relief (MATER) Joe Cordell Spencer Timmons Michael Fleischmann."— Presentation transcript:

Modeling Alabama Tornado Emergency Relief (MATER) Joe Cordell Spencer Timmons Michael Fleischmann

Overview  Background  Problem Abstract  Network Overview (Nodes, Arcs)  Mathematical Model  Scenarios  Results  Conclusions  Further Work  Video Link Video Link 2

Background  State of Alabama Major Cities: Birmingham, Montgomerey, Huntsville, Mobile, Tuscaloosa Population: 4.7 million  Average 23 Tornados Per Year \$13 million in average annual damages 3

Background  Tornado Outbreak on April 27 th 2011 165 tornados across the United States 248 fatalities Over \$16 billion in damages over 3 days Listed by NOAA as the fourth deadliest in United States history 4

April 27 th, 2011 – Tornados  62 Tornados in Alabama alone  2219 injuries  192 fatalities  Only the second day in history that there were three or more F5 or EF5 tornadoes. 5

Background  Cordova Population: 2260 Two tornados Four fatalities 6

Problem Abstract  Relief supply flow as a Min-Cost Flow Model  Goal: To supply damaged cities in the least amount of time and determine if prepositioning of supplies will affect total travel time  Key modifications to the basic model Randomized delay Interdiction represented by arc delays  Measures of Effectiveness: Total travel time Access to damaged cities 7

Nodes 8 Huntsville Birmingham Tuscaloosa

Arcs 9 Huntsville Birmingham Tuscaloosa

Abstract Network 10

April 27 th, 2011 – Tornados 11 We Modeled Jasper, AL Area

Mathematical Model 12 MIN-COST FLOW Objective: Move humanitarian supplies to damaged towns in shortest time where costs are hours of movement required to deliver supplies. There is a demand for supplies at each damaged town. MATER looks at worst case scenario by implementation of a “smart” tornado which seeks to damage roads so as to inflict the greatest cost on the operator. AirPort City t s C=0 C=20 C=24 C=5

Mathematical Model 13 MIN-COST FLOW Objective: Move humanitarian supplies to damaged towns in shortest time where costs are hours of movement required to deliver supplies. There is a demand for supplies at each damaged town. MATER looks at worst case scenario by implementation of a “smart” tornado which seeks to damage roads so as to inflict the greatest cost on the operator. AirPort City s C=0 C=20 C=24 t C=50 C=5 1

Damaged City City Node Airport Node Scenario 1a Destroyed Roads-Jasper Only 14

Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 15

Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 16

Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 17

Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 18

Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 19

Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 20

Damaged City City Node Airport Node Scenario 1b Destroyed Roads-Jasper and Blount Springs 21

Damaged City City Node Airport Node Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta 22

Damaged City City Node Airport Node 23 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

Damaged City City Node Airport Node 24 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

Damaged City City Node Airport Node 25 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

Damaged City City Node Airport Node 26 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

Damaged City City Node Airport Node 27 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

Damaged City City Node Airport Node 28 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

Damaged City City Node Airport Node 29 Scenario 1c Destroyed Roads-Jasper, Blount Springs and Oneonta

Damaged City City Node Airport Node 30 Scenario 1c Destroyed Roads-Jasper and Blount Springs

Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 31

Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 32

Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 33

Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 34

Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 35

Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 36

Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 37

Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 38

Damaged City City Node Airport Node Scenario 2c Delays Roads-Jasper, Blount Springs and Oneonta 39

Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 40

Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 41

Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 42

Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 43

Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 44

Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 45

Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 46

Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 47

Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 48

Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 49

Damaged City Prepositioned Stocks City Node Airport Node Scenario 3 Prepositioned Aid 50

Scenario 1: Operator Resilience Curve 51

52 Scenario 1 Results  With roads completely destroyed, tornado quickly cuts off access to affected area. 5 Roads knocked out cuts off Jasper from relief supplies Must then use Chinook helicopters to deliver supplies to the city, and vehicle delivery for surrounding areas affected less  Most damaging path with fewer destroyed roads is south of the city, taking out the roads from 2 of the 3 airports Supplies then flow through Huntsville Airport Main storm actually followed this path

Scenario 2: Operator Resilience Curve 53

54 Scenario 2 Results  With delayed roads, ramp-up in time is more gradual Spikes when moving across multiple delayed roads  Most damaging tornado path remains the same  No longer possible to cut off supplies to ground shipment

Scenario 3: Operator Resilience Curve 55

56 Scenario 3 Results  With supplies pre-positioned instead of flown-in, travel time is decreased, but not significantly Original flown-in supply model does not include flight time to airport Change in travel time due to proximity of prepositioned supplies to area

Prepositioned Supplies Comparison 57

Model Useability  Model is easily customizable to a given scenario Can be used to show movement of supplies to any affected city/area Scalable for multiple damaged cities via adding demands to those nodes Can use flown-in supplies or prepositioned supplies  Useful to quickly formulate delivery plan for FEMA/military responders

Conclusions  Depending on the city, tornado damage can quickly cut off area from relief supplies if roads are rendered unusable Helicopter delivery via US Army National Guard would then be necessary  Best option for high network resiliency is to keep road network in good repair and clear of neighboring trees  Prepositioned stocks of relief supplies would not make a large difference Must still get vehicles and personnel to distribute Not much closer than airports

Potential Future Work  Model entire state or other areas prone to natural disasters  Adjust model to depict hurricane or earthquake damage instead  Analyze changes in results with a more micro- resolution network (more roads, towns)

References  Map images and road distance maps.google.com  Past tornado path and strength data: www.tornadohistoryproject.com  City statistics/demographic info: www.city-data.com  Consolidated list of information and articles: http://en.wikipedia.org/wiki/April_25%E2%80%9328,_ 2011_tornado_outbreak

62 Questions?