Using AI to Predict Droughts, Floods and Conflict Displacements in Somalia
A neural network that predicts and compares the weekly Vegetation Health Index with the number of forced displacements.
The problem to be solved
Millions of people are forced to leave their current area of residence or community due to resource shortage and natural disasters such as droughts, floods. Our challenge partner, UNHCR, provides assistance and protection for those who are forcibly displaced inside Somalia.
The goal of this challenge was to create a solution that quantifies the influence of climate anomalies on forced displacement and/or violent conflict through satellite imagery analysis.
Facts Opinions and Guesses Phase
The most challenging task for this challenge was to find out what and where is the real data to rely on.
The UNHCR Innovation team provided the displacement dataset, which contains:
Month End, Year Week, Current (Arrival) Region, Current (Arrival) District, Previous (Departure) Region, Previous (Departure) District, Reason, Current (Arrival) Priority Need, Number of Individuals. These internal displacements are weekly recorded since 2016.
While searching for how to extract the data I learned about NDVI (Normalized difference vegetation index), and NDWI (Normalized Difference Water Index).
Our focus was on finding a way to apply NDVI and NDWI on satellite imagery:
Landsat (EarthExplorer) and MODIS, Hydrology (e.g. river levels, river discharge, an indication of floods/drought), Settlement/shelters GEO (GEO portal). These images have 13 bands and take up around 1GB of storage space per image.
Also, the National Environmental Satellite, Data, and Information Service (NESDIS) and National Oceanic and Atmospheric Administration (NOAA) offer very interesting data like Somalia Vegetation Health print screens taken from STAR — Global Vegetation Health Products.
By looking at the above picture points I figured that the Vegetation Health Index could be having a correlation with people displacements…
I found an interesting chart, which captured my attention,
- Go to STAR’s web page.
- Click on Data type and select which kind of data you want
- Check the following image
4. Click on the region of interest and follow the steps below
STAR’s web page provides SMN, SMT, VCI, TCI, VHI index’s weekly since 1984 split in provinces.
SMN= Provincial mean NDVI with noise reduced
SMT=Provincial mean brightness Temperature with noice reduced
VCI = Vegetation cond index ( VCI <40 indicates moisture stress; VCI >60: favorable condition)
TCI= thermal condition Index (TCI <40 indicates thermal stress; TCI >60: favorable condition)
VHI =vegetation Health Index (VHI <40 indicates vegetation stress; VHI >60: favorable condition))
Drought vegetation
VHI<15 indicates drought from severe-to-exceptional intensity
VHI<35 indicates drought from moderate-to-exceptional intensity
VHI>65 indicates good vegetation condition
VHI>85 indicates very good vegetation condition
In order to take the findings to the next level, the following questions have to be answered.
If conflict correlates to displacement, then what is the observed lag in time? Is there a lag between conflict incidents and observed displacement or between the establishment of displaced settlements and environmental degradation?
Correlation between Vegetation Health Index values of Shabeellaha Hoose and the number of individuals registered due to Conflict/Insecurity.
Correlation between the Number of Individuals from Hiiraan Displacements caused by flood and VHI data.
Correlation between the Number of Individuals from Sool Displacements caused by drought.
Building the neural network
A neural network that predicts the weekly VHI of Somalia by using historical data as described above. You can find it here.
The model produces a validation loss of 0.030 and training loss of 0.005, Below is the prediction of the neural network using test data.
Being part of this AI challenge
The most important is that you know that the final product will be used to save people's lives, what can be more motivating than that?
By engaging in Omdena’s global community, I learned about satellite imagery and about enhancing datasets by creating fake images with GANS in a record time of two months.
A metaphor for aspiring ML engineers.
Learning that Apple trees produce some mixture of great fruit and wormy messes. Yet the apples in high-end grocery stores display 100% perfect fruit. Between orchard and grocery, someone spends significant time removing the bad apples or throwing a little wax on the salvageable ones. As an ML engineer, you’ll spend enormous amounts of your time tossing out bad examples and cleaning up the salvageable ones. Even a few “bad apples” can spoil a large data set.
Want to become an Omdena Collaborator and join one of our tough AI for Good challenges, apply here.
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