In this document, we will refer to a generalized and broader use case of AIML and IoT into Disaster Response scenarios. Watch this live session on Youtube: https://www.youtube.com/watch?v=pgZDsGBzJuE&t=1414s This guest talk at the IIT-Guwahati zoom-live session on the launch of the 2022 Batch of AIML certification, addressed the questions like; How AIML is helping this world to be a safer planet for a living? How humans have become intelligent over years by using Artificial Intelligence and Machine Learning to handle Disasters? How the wildfire in technologically advanced countries is getting handled or maturing to get ready to handle? How big is this disaster problem? How drones are helping to fight disasters? Connect with me at: http://abhilashshukla.com/ For download. kindly Mention/DM me on LinkedIn or Twitter: https://www.linkedin.com/in/abhilashshuklaa/ https://twitter.com/AbhilashShuklaa

1. Artificial Intelligence &
Machine Learning
Nature can be disruptive
so can technology
27th March 2022
Public Safety and Disaster Response
Abhilash Shukla
@abhilashshuklaa
#AIML #IoT #ClimateChange
#DigitalTransformation

2. Artificial Intelligence sprouts with an “if Can” and “then What”
The
AI
Artificial Intelligence
encompasses creating intelligent
machines that can mimic human
behaviour and thinking
capability
The machine
improves on
those
decisions
4
The machine
make decisions
for those
instructions
3
We give
instructions to
a machine
1 ► Step-by-Step
Instructions to handle
all known events
The machine
learn from
instructions
2
► Expose more and more data for continuous learning
► Look for patterns in massive datasets
► With enough data, make forecasts & predictions
Then
What?
Think and
Perform
Identify and understand
the problem
Collect datasets to
detect and prove the
problem
Use dataset for
projections and
forecasting (1)
Take decisions and
perform the actions
1
2
3
4
5
Gather knowledge about
the problem
Turn on the TV, a sum of
numbers, Rotate a wind
turbine, etc.
► Register the success and
failures to retrain the Machine
with historical results and
identify the possible anomalies
► Try projects to optimize
educate the model
► Use computations to produce reliable, repeatable
decisions and results.
► Build forecasts for choosing the best path
Quarantine
malware
Bank detecting
credit card frauds
Suggest right
clothes to buy
And
ML
Machine learning is the process
by which machines learn from
data without being explicitly
programmed.
(1) A projection focuses on the desired outcome,
a forecast focuses on the most likely outcomes
Download Link: https://bit.ly/AIML-IITG-Imarticus
Can
Self Driving Cars
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3. How AIML can help in dealing with
Disaster Response?
Use case

4. Understanding the problem.
Download Link: https://bit.ly/AIML-IITG-Imatricus
As per Cambridge University, the
COVID19 put the cost to the global
economy at USD$82 trillion over five
years.
US$520billion/year
In the past 2 decades (1998-2017)
natural or man-made emergencies and
disasters reported USD$2.9 trillion in
the direct economic losses
Is the cost of disaster mitigation
globally
Number of
people affected
per disaster type
1998-2017
Numbers of disasters
per type 1998-2017
Data Source: The World Bank - Results Brief - Climate Insurance (2017) Available at: https://www.worldbank.org/en/results/2017/12/01/climate-insurance
All economic losses and GDP are adjusted at 2017 US$
© 2009-2022 abhilashshukla.com | All rights reserved.

5. Loss of
private
properties
Public
property
damage
Destruction
of a site of
cultural
significance
Business
hubs, and
commercial
area
Gather knowledge about the problem: Wildfire
Download Link: https://bit.ly/AIML-IITG-Imatricus
Humans are the most important
priority and any life loss will be
considered as severe
Limiting the effect on the
direct impacts on food,
agriculture, and livestock to
make sure that a parallel
concern of starvation doesn’t
arise
Save everything else that
you can
How to
decide?
2
What is
important?
1
Minimizing the
infrastructure loss to
mitigate the huge
rebuilding cost
(Part 1)
THE BASICS
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6. Download Link: https://bit.ly/AIML-IITG-Imatricus
Gather knowledge about the problem: Wildfire (Part 2)
A
• How fast the fire is
penetrating?
• In which direction is it
penetrating?
B
• What places should be
evacuated first?
C
• What should be the right
evacuation route?
• How to know which
routes need impromptu
diversions?
D
• What would be the right
Incident Action Plan?
• What are the hot areas where
Search and Rescue teams
should be assigned?
• How to do an impromptu
routing for the search and
rescue team
E
• How to handle missing person
phone calls?
• To which SAR team the
missing person should be
assigned?
F • Training the team with
updated information
• The communication plan
C
D
E
F
A
Evacuation
of people
and animals
Managing
the traffic
Contamination
of fire
Communication
for Public and
Ground Control
Teams
Missing
Persons
Search & Rescue
(SAR) operations
First 24
Hours
Activities
B
THE STORY
Let’s build a Storyline
There is a wildfire because of an industrial fire in a coal
mine. The coal mine is inside a forest.
❑ Within 10KM there are 3 suburban areas
❑ Nearest city is 15KM from the fire origination center
❑ Nearest biggest city has 10 million population
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7. Importance of data
Knowledge is key.
Data Acquisition

8. Download Link: https://bit.ly/AIML-IITG-Imatricus
Collect dataset and model projections and forecasting.
USE OF IoT SENSORS
Light Sensor
Hall effect Sensor
Motion Sensor
Infrared Sensor
Proximity Sensor
Pressure Sensor
Ultrasonic Sensor
Humidity Sensor
Colour Sensor
Current Sensor
Temperature Sensor
Compass Sensor
Atmospheric Pressure Sensor
Wind Speed & Direction Sensors
GAS Sensor
Present Weather Sensors
Ceilometers
Precipitation gauges and Dendrometers
© 2009-2022 abhilashshukla.com | All rights reserved.

9. We have all the data!
Take decisions and perform the actions.
What if…?

10. Use dataset 1) project 2) decide 3) act
Download Link: https://bit.ly/AIML-IITG-Imatricus
08:00
Fire erupted at the coal-mine
11:30
The wind directed the fire towards the city
8:30
The sensors picked up anomalies
and started sending data to AI Models
9:30
The AI
Deployed
Drones
to the most
probable
penetrable
locations
13:00
Again the wind directed the fire towards the city
11:45
The AI triggered the safest route of evacuation
13:05
The AI re-strategized the plan immediately with
the new datasets and executed the plan on-ground
13:05
All the evacuation strategies were notified to ground control
teams and traffic rerouting planned for faster evacuation
Because of the new probable datasets
The Support AI extended for an efficient
disaster response
 The AI models identified the blind spots and
deployed drones for data fidelity
 With the help of enough data, the AI models were
able to predict the most probable touchpoints of
the city that can get hit by the wildfire
 The AI models triggered many effective
communication plans for on-ground disaster
response teams
 AI models designed with the most effective plans
for the evacuation considering the 3600 approach
to handle this problem of scale
 AI models helped the ground control teams with
effective traffic management and real-time
rerouting of city exit navigations
 Etc., etc., etc….
© 2009-2022 abhilashshukla.com | All rights reserved.

11. Abhilash Shukla
@abhilashshuklaa
#AIML #IoT #ClimateChange #DigitalTransformation
Thank you!
Connect with me on LinkedIn and Twitter @AbhilashShuklaA
www.abhilashshukla.com