In 2016, Category 4 Hurricane Matthew made landfall in the south of Haiti, causing destruction unseen in more than a decade during the hurricane season, with winds as high as 230 kilometers per hour and 600 millimeters of rain in less than 24 hours. The toll was very high, with a total of 546 victims, 128 missing, 439 injured, and 2.1 million people affected.
From mapping hazard-prone urban areas in Tanzania to providing drought early warnings, disaster risk management professionals are finding new applications of machine learning (ML) at a rapid pace.
In disaster risk management, ML can help create actionable information faster and at lower cost: whether evaluating satellite imagery to determine flooded areas; processing street-level photography to identify structural characteristics of buildings; or assessing urban growth patterns to understand future vulnerabilities.
Road infrastructure is vulnerable to geohazards. Imagine you’re driving on a single lane rural road in Himalayas where you have a steep hill on your left and deep cliff on your right. It’s raining and the road is covered with mud eroded from the hill side.
Road infrastructure is vulnerable to geohazards. Imagine driving on a single-lane rural road in the Himalayas, with a steep hill to your left and a steep cliff to your right side. It’s raining and the road is covered with mud from the hillside. A large truck is passing by, leaving little space to maneuver. If a landslide occurs, this road will be blocked for a long time because there is no alternate way to get there and heavy equipment has to be brought from a distance to remove debris and clear the road.
Over the past 20 years, floods have displaced more Indonesians than any other disaster type, causing significant damage and disrupting local economies. The poor and vulnerable often bear the brunt of flood hazards and are affected disproportionately. They tend to live in hazardous areas such as dense settlements situated below flood levels, highly-exposed coastal areas, and along riverbanks that often overflow. They also often have limited access to financial services and basic support to cope with the aftermath of flood events.
While many South Asian governments have made significant and important strides towards addressing social inclusion issues in DRM policies and frameworks, there often remains a gap in translating these commitments into de facto actions on the ground.
With support from the Global Facility for Disaster Reduction and Recovery (GFDRR), the World Bank recently completed a strategic review of Moldova’s disaster risk management (DRM) and climate resilience challenges, highlighting opportunities for the country to shift from a reactive, ex post DRM system to a more proactive, ex ante approach.
As a country highly prone to disasters, Indonesia is committed to addressing comprehensively their impacts on lives and infrastructure. The Government continues working intensively on improving specifications, guidelines and practices to enhance road and bridge resilience to such events, leading to reduced material and immaterial damages and overall costs.
Water utilities must act now to ensure water security for millions of Indonesians. To do so, we’ll need to enhance resilience through risk-based planning and engineering design, and be better prepared to respond rapidly in an emergency.
The government and the World Bank, with funding from the Global Facility for Disaster Reduction and Recovery (GFDRR), conducted a study to develop strategies for large-scale coral reef restoration. This assessment prioritized 15 locations on the three main islands where coral restoration could reduce coastal risks and enhance biodiversity. According to the study, in most locations, coral restoration would need to be combined with artificial structures to deliver significant coastal protection.