How AI models can assist in mitigating flood-induced casualties and damages

[Ali-Pourzangbar]

• Flood Susceptibility Mapping (FSM) involves identifying areas prone to flooding to mitigate future floods and assess susceptibility.
• Floods are among the most destructive natural disasters, causing significant economic and environmental damage.
• Accurate Flood Susceptibility Mapping (FSM) can reduce the extent of loss and destruction caused by floods.
• Conventional methods of flood study are often costly and inaccurate, highlighting the need for advanced FSM techniques.
• Advances in high-performance computing and machine learning have improved the modeling of flood susceptibility.
• Data-driven approaches using training data and influencing factors can forecast flood susceptibility more accurately.
• Hybrid models combining various machine learning techniques address the problem of model accuracy.
• Black-box machine learning models lack transparency and interpretability.

Title

Enhancing Flood Susceptibility Models with Integration of Evolutionary Genetic Programming and Machine Learning Algorithms

Description

Objectives:

• Use Genetic Programming (GP) for flood susceptibility modeling.
• Enhance the interpretability of black-box machine learning algorithms.
• Develop a hybrid model combining supervised and unsupervised machine learning algorithms for FSM.

Research Questions:

• Can we use reliably ML to construct flood susceptibility maps?
• Does GP enhance the interpretability of machine learning models?

Modeling tool

• Genetic Programming (GP)
• Logistic Regression (LR)
• A combination of Genetic Programming and Logistic Regression (GP-LR)
• A combination of Genetic Programming and Logistic Regression implemented on the principal components detected by Principal Component Analysis (GP-LR-PCA)

Final outcome: a ML-based model developed via hybrid algorithms for the prediction of flood susceptibility

Organizational

Institut für Wasser und Umwelt Fachbereich Wasserbau und Wasserwirtschaft

Host(s)

Ali Pourzangbar ali.pourzangbar@kit.edu

Format

Introduction talk followed by open discussion

Timeframe

30min

Number of participants

There is NOT any limitation in the number of people who attend this session

Material

Flipchart, markers, post-its, pen

Our work at one glance

AP#74 Poster.ppt - Compatibility Mode.pdf

[helenehoffmann]

Thank you for your contribution. Can you please add the format you have in mind? Will this be a hackathon or a discussion round or a presentation or something else?

[Ali-Pourzangbar]

[helenehoffmann]

@Ali-Pourzangbar Thank you so much for hosting a session at this Unconference! Your participation is what makes this event truly special. Please, be prepared to give a 1-minute pitch for your topic to share at the beginning of the Unconference.

We’re excited to see you there and can’t wait for an amazing event! Best, The Organizing Team

[SusanneWenzel]

Dear @Ali-Pourzangbar ,

many thanks for your contribution! We hope your session was successful and constructive!

To make this not only a nice experience during the conference but a sustainable format, we would like to release a short result on the Helmholtz AI Website. Please provide us with a little report about the discussion in your session:

• One or two intro sentences about the topic and the respective format of your session
• What was most controversial, or about what did all participants agree on?
• What were the key ideas?
• Do you see any follow up on this discussion?

Depending on the intensity of the session it can be shorter (1/3 page) or longer (one pager).

Please post your report here by end of next week. Ina already confirmed to release that at a HAICON24 subpage at Helmholtz.ai.

Best regards, @helenehoffmann and Susanne