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Cologne AI and Machine Learning Meetup
November 14th, 2019
6:30 p.m. – 9:30 p.m.
factor-a – part of Dept
Cologne AI and Machine Learning Meetup #9 will happen on November 14, 2019, at factor-a. This time, we will see how AI can be used for social good and to address societal challenges.
Gernot Heisenberg (Professor at Technische Hochschule Köln, https://www.linkedin.com/in/gernotheisenberg/): Combining socio-economic and remote sensing data for food insecurity prediction using neural networks
Aid organizations and governments are applying great effort in resolving the negative impacts of food insecurity induced crisis like famines or mass migration. One of the most limiting resources these actors face is the lack of preparation time for consistent and sustainable planning for emergency relief like setting refugee camps or securing supply with food and energy. Hence, increasing the lead time for preparation is an essential step and will result in saving many lives. The aim of this research is to increase the lead time by developing a machine learning based mathematical prediction model that is able to compute the probability for food insecure areas by learning from historical data.
For performing such computations, our prediction model is developed and trained on historic open access data for the Horn of Africa [masked]). We used precipitation and vegetation data derived by remote sensing, as well as socio-economic, medical, armed conflict and disaster data. To overcome spatial inconsistencies in the input data and to meet the requirements of spatially homogenous input for neural networks, all data has been converted to geo-referenced raster maps. Disaster and armed conflict data has been fitted to districts while local food market prices have been interpolated. The IPC (Integrated Phase Classifier) has been used as the food security label.
In order to find a prediction model, deep learning methods have been used. Several analyses were applied on the collected data such as multicollinearity checks and principal component analyses. Preliminary cross-validated results have encouraged us to further investigate the detection of food insecure areas using open access data.
The agenda and more details will follow soon!