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Afghanistan

Demystifying machine learning for disaster risk management

Giuseppe Molinario's picture
Also available in: العربية | Español | Français

To some, artificial intelligence is a mysterious term that sparks thoughts of robots and supercomputers. But the truth is machine learning algorithms and their applications, while potentially mathematically complex, are relatively simple to understand. Disaster risk management (DRM) and resilience professionals are, in fact, increasingly using machine learning algorithms to collect better data about risk and vulnerability, make more informed decisions, and, ultimately, save lives.

Artificial intelligence (AI) and machine learning (ML) are used synonymously, but there are broader implications to artificial intelligence than to machine learning. Artificial (General) Intelligence evokes images of Terminator-like dystopian futures, but in reality, what we have now and will have for a long time is simply computers learning from data in autonomous or semi-autonomous ways, in a process known as machine learning.

The Global Facility for Disaster Reduction and Recovery (GFDRR)’s Machine Learning for Disaster Risk Management Guidance Note clarifies and demystifies the confusion around concepts of machine learning and artificial intelligence. Some specific case-studies showing the applications of ML for DRM are illustrated and emphasized. The Guidance Note is useful across the board to a variety of stakeholders, ranging from disaster risk management practitioners in the field to risk data specialists to anyone else curious about this field of computer science.

Machine learning in the field

In one case study, drone and street-level imagery were fed to machine learning algorithms to automatically detect “soft-story” buildings or those most likely to collapse in an earthquake. The project was developed by the World Bank’s Geospatial Operations Support Team (GOST) in Guatemala City, and is just one of many applications where large amounts of data, processed with machine learning, can have very tangible and consequential impacts on saving lives and property in disasters.

The map above illustrates the “Rapid Housing Quality Assessment”, in which the agreement between ML-identified soft-story buildings, and those identified by experts is shown (Sarah Antos/GOST).

Bribery and limited access to banking are challenges for Afghan private firms

Arvind Jain's picture

The World Bank Group’s Enterprise Surveys benchmark the business environment based on actual experiences of firms. In a new blog series we kicked off last week, we’re sharing these findings from recently analyzed surveys conducted through extensive face-to-face interviews with managers and owners of firms in several countries.
 
In this post we focus on Afghanistan. We’ve conducted a survey with 410 firms across five regions and four business sectors—manufacturing, construction, retail, and services.

The International Monetary Fund (IMF) has noted that considerable political and security uncertainties have posed challenges for Afghanistan. Furthermore, the financial sector has been vulnerable with eight out of 15 banks classified as weak in late 2014. Within this context, the Afghanistan Enterprise Surveys (ES) shed light on several interesting findings:

Corruption is a challenge

According to the Afghanistan Enterprise Survey, firms face almost a 50 percent chance of having to pay a bribe if they applied for an electricity connection, tried to obtain permits, or met with government officials for tax purposes (“Bribery incidence”).  This is more than double of what private firms in landlocked developing countries experience on average.