Summary:
The team hopes to create a model that can accurately predict damage to buildings after an earthquake. The objective is to provide some baseline that governments can use to help communities renovate the buildings most at risk of an earthquake. The data used comes from a large dataset of surveyed buildings after the Nepal earthquake in 2015.
What I like about the proposal:
I think the model and its uses are easily understood and marketable. It makes sense that the physical makeup of a building and its location would have significant effects on damage done.
From the description of the dataset, it seems that there is large potential to construct a very accurate model due to the intricacy of the data collected and having over 600,000 buildings surveyed.
The project’s objective aims to provide insight to a humanitarian issue and potentially help save lives.
Suggestions:
It seems likely that the buildings nearest the earthquake were hit the hardest which a model might learn. Some explanation on how generalization might be accomplished (e.g. neighboring countries use similar construction materials) might be beneficial if the aim is to potentially help areas outside of Nepal.
Summary: The team hopes to create a model that can accurately predict damage to buildings after an earthquake. The objective is to provide some baseline that governments can use to help communities renovate the buildings most at risk of an earthquake. The data used comes from a large dataset of surveyed buildings after the Nepal earthquake in 2015.
What I like about the proposal:
Suggestions: