Open AccessUsing deep neural networks for predictive modelling of informal settlements in the context of flood risk
Author(s) -
Yue Zhu,
Christian Geiß,
Emily So
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1343/1/012032
Subject(s) - flood myth , human settlement , flooding (psychology) , vulnerability (computing) , context (archaeology) , geography , informal settlements , settlement (finance) , china , climate change , natural disaster , environmental planning , environmental resource management , business , environmental science , economic growth , archaeology , computer security , computer science , ecology , psychology , meteorology , economics , finance , payment , psychotherapist , biology
Global climate change has substantially increased the risks of cities being adversely affected by natural hazards such as floods. Among the inhabitants of cities at risk, residents dwelling in informal settlements are the most vulnerable group. To identify the future exposure of informal settlements, we adopt a data-driven model from the machine learning domain to anticipate the growth patterns of formal and informal settlements in flood-prone areas. The potential emergence of informal settlements in Shenzhen, China, is predicted by the proposed method. Then, through an analysis of the flood susceptibility of the predicted informal settlement areas, the emerging vulnerability of Shenzhen towards flooding is revealed.