A Simple AI Project Idea presentation slide on Flood Forecasting.

1. Flood
Forecasting AI

2. 2
Hello!

3. presented To
3
Shahana Shultana
Lecturer
Computer Science and Engineering
Daffodil International University
Course: CSE315 - Artificial Intelligence

4. Introduction
Let’s start with Abstract

5. Flood hazards have increased dramatically in recent years as a result
of climate change, and large-scale floods endanger lives and
property while causing significant economic losses in flood-prone
areas around the world.
Abstract

6. AGENTS

7. Agents
Agents for Flood Forecasting:
● Hydrology Database
● Flood Related Variables
● Notification Agent
● Virtual Emergency Operation Center
● Report Generator Agent

8. Agent diagram of
Flood forecasting
8

9. PEAS Description

10. PEAS
▪ Performance Measure:
Safety, time, location, message, risk reduction.
▪ Environment:
Cyclone/Storms, Landslide, Surge, Earthquake, Tsunami, weather,
Temperature, Water level.
▪ Actuators:
Alarm, radio, television, SMS Alert, satellite radar.OTT RLS Radar.
▪ Sensors:
Video, Ultrasonic Sensor, Telemetry, GPS.

11. 11
Agent function with input and output
The study is divided into four areas:
▪ Signal preprocessing
▪ Integrated GA
▪ Schematic application of weather radar data and
▪ Multiple input in flow routing.

12. Environment
types
An environment in artificial intelligence is the surrounding of the agent. The
agent takes input from the environment through sensors and delivers the
output to the environment through actuators.

13. types of environments
★ Fully Observable vs Partially
Observable
★ Deterministic vs Stochastic
★ Competitive vs Collaborative
★ Single-agent vs Multi-agent
★ Static vs Dynamic
★ Discrete vs Continuous
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14. Fully Observable
Governments and
disaster
management
agencies to improve
the accuracy and
extend the lead
time of their
forecasts
The Flood Forecasting AI project environment types
Deterministic
Once a river is
predicted to reach
flood level, the next
step in generating
actionable warnings
is to convert the
river level forecast
into a prediction for
how the floodplain
will be affected
Competitive
ML-based
approach, using
almost exclusively
data that is globally
publicly available,
measurements,
public satellite
imagery, and low
resolution elevation
maps 14

15. The Flood Forecasting AI project environment types
Dynamic
We train the model to use the data it
is receiving to directly infer the
inundation map in real time. And they
keep changing based on the water
level
Multi-agent
There are several agents on this
project like Notification, Rain,
Maps, Water level, Report
Generator.
15
Continuous
In an effort to continue improving flood forecasting, we have developed
HydroNets, a specialized deep neural network architecture built specifically for
water level forecasting. So it is a continuous environment.

16. Conclusion
It is also worth mentioning that the multidisciplinary nature of
this work was the most challenging difficulty to overcome in
this project. For future work, conducting a survey on spatial
flood prediction using machine learning models and AI are
highly encouraged.
16

17. Thank
You
17