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Francesco Varotto
Giovanni Michele Toglia
Claudia Soto
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.
Nicholas Jones
Robert Soden
Grace Anna Doherty
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.
Akiko Toya
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Masatsugu Takamatsu
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.
Akiko Toya
Masatsugu Takamatsu
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.
Jun Erik Rentschler
Christoph Klaiber
Jian Vun
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.