Disaster Forecast — Machine Learning for Flood Prediction

Abstract

The flood of June 2021 caught Germany and other parts of Central and Western Europe by surprise. Unprecedented destruction took place with little to no adequate reaction and lacking assistance from the German government, leaving the houses of most affected citizens in shambles. As our title suggests, we prepared weather data and trained a machine learning model using logistic regression and random forest with feature importance analysis and hyperparameter optimization to predict these flood events, in order to raise awareness and help prevent another catastrophe of similar order, which in our generation seem to become more and more likely to occur due to climate change.

Figure 1: Random Forest example
Figure 2: Feature importance analysis of logistic regression model

Results

We managed to create a model with an accuracy rate of 80% for the region of Bangladesh. Unfortunately, we did not have enough time to train it based on a dataset for Germany, but in our Github repository you will find all necessary tools and a dataset provided by the DWD (which still needs to be modified) if you want to give the code a run on your own!

The team

Constantin Michel Data Science (LinkedIn)

Roles inside the team

As previously described, we split our Team in two camps to increase effective work patterns and not having to attend meetings, while not being able to contribute anything to the topic.

Mentor

Marcus Cramer

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