Using Neural Networks to Predict Droughts, Floods and Conflict Displacements in Somalia

Using Neural Networks to Predict Droughts, Floods and Conflict Displacements in Somalia

 

The Problem

 

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 project 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 change anomalies on forced displacement and/or violent conflict through satellite imaging analysis and neural networks for Somalia.

 

The Data 

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 we 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 Imaging and Neural Networks to prevent Climate Change disasters.

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 (VHI) could be having a correlation with people displacement.

 

We 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

 

 

 

  •  Click on the region of interest and follow the steps below

 

 

 

 

VHI index’s weekly since 1984

 

 

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 derive insights from the findings, the following questions needed to be answered.

Does vegetation health correlate to displacements? And is there a lag between vegetation health and observed displacement? Below visualizations provide answers.

 

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.

 

 

The Solution: Building the Neural Network

We developed a neural network that predicts the weekly VHI of Somalia using historical data as described above. You can find the model 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.

 

Prediction versus the original value

 

 

 

More about Omdena

Omdena is an innovation platform for building AI solutions to real-world problems through the power of bottom-up collaboration.

 

Using Unsupervised Learning on Satellite Images to Identify Climate Anomalies

Using Unsupervised Learning on Satellite Images to Identify Climate Anomalies

 

This work is a part of Omdena’s AI project with the United Nations High Commissioner for Refugees. The objective was to predict forced displacements and violent conflicts as a result of climate change and natural disasters in Somalia.

We used unsupervised learning techniques on satellite images for capturing sudden environmental changes (after-effects of natural disasters or conflicts) to provide immediate relief to people affected. The solution functions as an alert system.

 

The problem

Somalia is a small country in the continent of Africa. The country exhibits a lot of natural disasters and terrorism as a result of which people of Somalia go through mass displacements leading towards a situation of lack of food and shelter.

This article shows how to build an anomaly detection system using Machine Learning. The system is capable of capturing sudden vegetation changes, which can be used as an alert mechanism to provide immediate relief to the people and communities in need.

 

 

What is Anomaly Detection?

Anomaly Detection System using satellite images is an area where a lot of research is happening to discover new and better methods.

We approached the problem using unsupervised learning technique i.e using Principal Component Analysis and K-Means. In the case of anomaly detection, unsupervised learning will take multi-temporal images to find changes in the images. Finally, the output map will have highlighted regions of change that could be used to send an alert to representatives at UNHCR if any major deviation occurs between two continuous temporal images.

 

Unsupervised Learning Climate Change

Fig 2: In 2017 Bomb Attack in Mogadishu (Somalia) Kills 276

 

The approach

First try: Convolutional Neural Networks

The first approach that I came up with was to use deep learning techniques, namely CNN+LSTM, where CNN could help extract relevant features from the images and LSTM could help to learn the sequential changes. This way our model could learn the changes that occur gradually and if any major changes such as natural disaster or conflict occurred in that area, the predicted value of our model and actual value would have the difference much greater than the normal value. This would signify that something major has happened to send an alert UNHCR.

As often in the real world, there was not enough data to apply deep learning Therefore we looked for an alternative.

The solution: Less shiny algorithms

The problem of anomaly detection could be solved with both supervised and unsupervised learning techniques. Since the data was not labeled we went with unsupervised learning techniques. Change detection can be solved using NDVI values, PCA analysis, Image difference methods, etc.

We went through some great methods for anomaly detection including a split based approach to unsupervised learning detection[1]. Comparing two images of the same geographical area at two different times pixel by pixel and then using some algorithms like thresholding algorithms, Bayes theory to generate change map[2]. After doing some research I finally went with the PCA + K-means technique [3] as some previous methods were either taking a lot of assumptions or were directly applied to raw data which could bring a lot of noise.

 

The data

For this project, we needed the satellite data of regions from Somalia. The images can be downloaded either from the earth explorer website or from Google Earth Engine API. You must ensure that the data downloaded has cloud coverage as minimal as possible. This is a common problem working with satellite images.

Unsupervised Learning Climate Change

Fig 3: EarthExplorer Image

 

 

The solution: Unsupervised Learning

 

Unsupervised Learning Climate Change

Fig 4: Satellite Image of an area from Somalia. Here you can see a lot of vegetation and greenery

 

Unsupervised Learning Climate Change

Fig 5: Satellite image of the same area at a different time. Here you can see that vegetation is less than in the previous image 4.

 

Calculating the difference between both images

Differences between the two greyscale images were calculated through pixel by pixel subtraction. The computed value will be such that the pixel of areas associated with the change will have a much larger difference than unchanged areas.

Xd = |X1 – X2| where Xd is the absolute difference of the two image intensities.

Unsupervised Learning Climate Change

Fig 6: The difference image of the bi-temporal images shown earlier.

 

Principal Component Analysis

The next step was to create an eigenvector space using PCA. The first step is converting your image into h X h non-overlapping blocks where h can be anything greater than 2. Let’s call these sets of vectors Y. Principal Component Analysis is used to correct for decorrelation caused by atmospheric noise or striping. PCA drops the outline component from the bands and which then can be then used to classify.

 

Creating a feature vector space

The next step was to create a feature vector space. A feature vector space was constructed for each pixel of the difference image by projecting the neighborhood of each pixel on eigenvector space. This was done by creating a h X h overlapping blocks in the neighborhood of each pixel to maintain contextual information. Now we have a clean and high variance set of vectors that can be used for classification.

Clustering

This step involves generating two clusters based on feature vector space by applying K Means. The two clusters will be one that will represent change and others that will represent change. These feature vector already carries the information whether they carry changed pixel or unchanged one. When there is a change between two images in a region, the assumption is that the values of the difference vector over that region will be higher than in other regions. Therefore K Means will partition the data into two clusters based on the distance between cluster average mean and pixel vector. Finally, the change map was constructed with higher values of pixels over regions of change.

 

Fig 7: The highlighted part depicts the difference between the two images. The image is flooded with white spots because there was a lot of loss of vegetation in the two images.

 

The highlighted areas could be further used to examine the extent of change that occurred in a continuous sequence of time and therefore could help UNHCR take necessary actions. Loss of vegetation to such an extent like fig 7 would happen only when sudden large conflicts or natural disasters will occur and thus creating an alarm.

 

Conclusion

In this project, we were able to develop an anomaly detection model using PCA and K Means which could highlight areas of change. The highlighted areas could be further used to examine the extent of change that occurred in a continuous sequence of time and therefore could help UNHCR take necessary actions. Loss of vegetation to such an extent like fig 7 would happen only when sudden large conflicts or natural disasters will occur and thus creating an alarm.

Since cloud coverage is a common problem while working with satellite images (bottom left region of the image), human intervention is required. Hence there is an area of improvement.

 

More about Omdena

Omdena is an innovation platform for building AI solutions to real-world problems through the power of bottom-up collaboration.

 

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