Omdena Spearheads Disaster Logistic Prediction Tool for Cyclones Commissioned by the World Food Programme

Omdena Spearheads Disaster Logistic Prediction Tool for Cyclones Commissioned by the World Food Programme

By Beth Seibel

 

Whether termed cyclone, typhoon or hurricane, these natural weather occurrences pack a serious punch and are responsible for approximately 10,000 deaths per year and, “in some cases, causing well over $100 billion in damage. There’s now evidence that the unnatural effects of human-caused global warming are already making hurricanes stronger and more destructive. The latest research shows the trend is likely to continue as long as the climate continues to warm (Berardelli, 2019).”

It is for these reasons that the World Food Programme teamed up with Omdena to more accurately predict the types and amount of aid required when disaster strikes. “Assisting almost 100 million people in around 83 countries each year, the World Food Programme (WFP) is the leading humanitarian organization saving lives and changing lives, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience.”

Omdena gathered a team of 34 collaborators specializing in artificial intelligence and machine learning spanning 19 different countries for eight weeks with the goal of developing an AI data-driven way to help the WFP and other humanitarian organizations to know exactly what resources the people affected by cyclones (or any other disaster) will need and to expedite deployment as fast as possible. A priority on the team’s list, were answers to questions such as, how much food and water is required? What sort of shelters and how many are needed? What types and how much non-food essentials are appropriate? Before AI models could be developed, relevant data had to be gathered for this disaster response problem.

The team collected data from a variety of sources, such as NOAA, to determine affected populations and critical features of these populations such as income level, injuries, deaths, and more. Important factors were determined about cyclones including wind speed, total hours on land, damage factors, and whether the populations were rural versus urban. Below we see the correlation mapped based on income level and the number of people affected revealing populations most in need of assistance.

 

 

Understanding the attributes of the people affected by a disaster helps to reveal the types of aid required. So that the WFP and other aid organizations can determine what and how much relief to send with precision, the team used mathematical models to create a tool that calculates the needs of the people in the targeted disaster zones. The tool calculates how much food, non-food items, shelter, etc., the population should need for a determined number of days.

Relief Package

 

This exciting AI prototype can be used as the basis to assist disaster response organizations around the world to accurately customize aid resources to the specific needs of the people impacted. The team identified a more precise way to allocate aid in times of disaster. This will allow the World Food Programme and other organizations to respond to the needs of affected people faster and more efficiently than ever before thus reducing suffering and saving lives.

Find all details about the project here

References

Berardelli, J. (2019, July 8). How climate change is making hurricanes more dangerous. Yale Climate Connections. Retrieved June 7, 2020, from https://www.yaleclimateconnections.org/2019/07/how-climate-change-is-making-hurricanes-more-dangerous/

World Food Programme Overview. (2020). Retrieved June 07, 2020, from https://www.wfp.org/overview

 

More About Omdena

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

Overcoming Data Challenges through the Power of Diverse & Collaborative Teams

Overcoming Data Challenges through the Power of Diverse & Collaborative Teams

In this demo day, we talked about the inevitable data challenges/roadblocks that come up in real-world AI projects. The insights shared came from our experiences with more than 20 AI projects, working with partners including the UN Refugee Agency (UNHCR), the World Resources Institute, the World Energy Council, and numerous NGOs and corporations.

Omdena is a collaborative platform to build innovative, ethical, and efficient AI solutions to real-world problems. Since our founding in May 2019, over 1250 AI experts from more than 80 countries have come together on Omdena projects to address significant issues related to hunger, sexual harassment, land conflicts, gang violence, wildfire prevention, and energy poverty.

We’ve seen that the way that we approach AI development, via bottom-up collaboration with diverse team members, fosters innovation and creativity which leads to the breakdown of data roadblocks. Innovation is inherent in the Omdena process.

We shared three Omdena projects to act as case studies for these innovative approaches to tackling data challenges.

 

Data Roadblock 1: Incomplete Data Sets

In the real world, datasets are rarely complete. We find having large teams of dozens of people means that data gathering, cleaning, and wrangling happen at a phenomenal speed. And by taking a bottom-up approach, we have multiple sub-teams looking at data problems from different angles, allowing for innovative approaches to be explored.

In the following case study, the Omdena team worked out ways to identify safe routes in a city in the aftermath of an earthquake, where the relevant data sets were inconsistent and unreliable.

 

Case Study : Disaster Response: Improving the Aftermath Management of an Earthquake

In collaboration with Istanbul’s Impact Hub innovation center, Omdena data scientists combined satellite imagery of Istanbul with street map data in order to build a tool that facilitates family reunification by indicating the shortest and safest route between two points after an earthquake.

“Omdena´s approach to AI development is by far the best that I have seen in 2019” — Semih Boyaci, Co-Founder Impact Hub Istanbul

You can learn more about this project here:

 

 

Data Roadblock 2: No Data

We don’t see the lack of data as a showstopper. On those projects without data, the team starts by asking what do we need to know to address the problem? Where might that data live? If it doesn’t exist, how can we create it from something that does exist? Here the diversity of the team members is very powerful.

We’ve seen time and again the impact of bringing together people with vastly different professional and life experiences. Our teams are typically 30% or more female. On any project, we’ll have on average 14 countries represented. Our collaborators range in age from 17 to 65. Not only does this diversity lead to ethical and trusted solutions, but it also fosters creativity and alternative ideas about what data is relevant and where to find it.

In the following project, we looked at how to assess post-traumatic stress disorder among those that have suffered trauma in low-resource environments. In this case, the team started with no data in-hand.

 

Case Study : Building a chatbot for Post-traumatic-stress-disorder (PTSD) assessment

32 Omdena collaborators developed a machine learning-driven chatbot for PTSD assessment in war and refugee zones.

 

The unique aspect of the project was that we did not start with a data set.

Through the collaborative efforts of the project community, the team identified and annotated suitable patient data. The teams applied linear classifiers for Natural Language Processing (NLP) for PTSD risk assessment and transfer learning for data augmentation.

You can learn more about this project here:

 

Data Roadblock 3: Disparate Data Sources

Relevant data doesn’t typically come packaged in just one form. We often need to meld disparate data sources to get at a solution. Through collaboration, sub-teams focused on separate data and AI techniques come together to integrate those efforts to derive insights about the problem.

In the following project, the goal was to uncover domestic violence in India hidden due to COVID lockdowns. Among the many challenges the team addressed was the integration of data culled from disparate sources.

 

Case Study : Analyzing Domestic Violence through Natural Language Processing

This project was done with the award-winning Red Dot Foundation. Within Omdena’s collaborative platform, the team looked craft a dataset to reveal domestic violence and online harassment patterns in India during COVID-19 lockdowns. The AI experts scrapped data from news articles as well as social media to apply various natural language processing (NLP) techniques such as topic modeling, document annotations, and stacking machine learning models.

 

 

You can learn more about this and related projects here:

 

 

 

More about Omdena

Omdena is the collaborative platform to build innovative, ethical, and efficient AI and Data Science solutions to real-world problems. 

| Demo Day Insights | AI for Disaster Response: World Food Programme Project

| Demo Day Insights | AI for Disaster Response: World Food Programme Project

Helping affected populations during a disaster most effectively through AI. A collaborative Omdena team of 34 AI experts and data scientists worked with the World Food Programme to build solutions to predict affected populations and create customized relief packages for disaster prevention.

The entire data analysis and details about the relief package tool including a live demonstration can be found in the demo day recording at the end of the article.

 

The problem: Quick disaster response

When a disaster strikes, the World Food Programme (WFP), as well as other humanitarian agencies, need to design comprehensive emergency operations. They need to know what to bring and in which quantity. How many shelters? How many tons of food? These needs assessments are conducted by humanitarian experts, based on the first information collected, their knowledge, and experience.

The project goal: Building a disaster relief package tool for cyclones (applicable to other use cases and disaster categories)

 

AI Disaster

Cyclones

 

Use Case: Cyclones (Solution applicable to other areas)

Tropical cyclones cost about 10,000 human lives a year. Many more are injured with homes and buildings destructed, which results in financial damage of several billions of USD. Due to changes in climate and extreme weather events, the impact is growing steadily.

 

AI Disaster

Long Beach after Hurricane Katrina. Estimated damage of 168 billion dollars (Source: Wikipedia).

 

The data

The Omdena team gathered data from several sources:

  • IBTrACS – Tropical cyclone data that provides climatological speed and directions of storms (National Oceanic and Atmospheric Administration)
  • EmDAT – Geographical, temporal, human, and economic information on disasters at the country level. (Université Catholique de Louvain)
  • Socio-Economic Factors from World Bank
  • The Gridded Population of the World (GPW) collection – Models the distribution of the human population (counts and densities) on a continuous global raster surface

Missing data was collected manually or partially automated by scraping from Wikipedia or cyclone reports.

 

Data exploration: Determining affected populations

All five data set were aggregated and included more than 1000 events and 45 features characterizing cyclones and affected populations.

 

AI Disaster Response

Data Exploration

 

AI Disaster

Impact Cyclones (Landing vs. No-landing)

 

Important correlation factors to determine affected populations:

  • Rural Population
  • Human Development Index
  • GDP per capita
  • Landing
  • Wind Speed
  • Exposed population
  • Total hours in Land
  • Total damage
  • Total deaths

 

The team mapped the correlation factors to determine which populations are most in need. As an example, below the income level is correlated with the number of people affected. Taking advantage of past data, the data model predicts affected populations.

 

AI Disaster Response

Predicting affected populations based on income level

 

The tool: Calculating relief packages

Once an affected population has been identified, humanitarian actors need to design comprehensive emergency operations including how much food and what type of food is needed. The project team built a food basket tool, which facilitates calculating the needs of affected populations. The tool looks for various different features such as days to be covered, the number of affected people, pregnancies, kids, etc.

 

AI Disaster Response

The relief package tool

 

The entire data analysis and details about the relief package tool including a live demonstration can be found in the video.

 

 

The team: Collaborators from 19 countries

This Omdena project hosted by the WFP Innovation Accelerator united 34 collaborators and changemakers across four continents. All team members worked together for two months on Omdena´s innovation platform to build AI solutions with the mission to improve disaster response. To learn more about the project check out our project page.  

 

AI Disasters

Omdena Collaborators

 

All changemakers: Ali El-Kassas, Alolaywi Ahmed Sami, Anel Nurkayeva, Arnab Saha, Beata Baczynska, Begoña Echavarren Sánchez, Chinmay Krishnan, Dev Bharti, Devika Bhatia, Erick Almaraz, Fabiana Castiblanco, Francis Onyango, Geethanjali Battula, Grivine Ochieng, Jeremiah Kamama, Joseph Itopa Abubakar, Juber Rahman, Krysztof Ausgustowski, Madhurya Shivaram, Onassis Nottage, Pratibha Gupta, Raghuram Nandepu, Rishab Balakrishnan, Rohit Nagotkar, Rosana de Oliveira Gomes, Sagar Devkate, Sijuade Oguntayyo, Susanne Brockmann, Tefy Lucky Rakotomahefa, Tiago Cunha Montenegro, Vamsi Krishna Gutta, Xavier Torres, Yousof Mardoukhi

 

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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