Authors
Mohammed Khalaf, Abir Jaafar Hussain, Dhiya Al-Jumeily, Thar Baker, Robert Keight, Paulo Lisboa, Paul Fergus, Ala S Al Kafri
Publication date
2018/7/8
Conference
2018 IEEE Congress on Evolutionary Computation (CEC)
Pages
1-8
Publisher
IEEE
Description
In this paper, a novel application of machine learning algorithms including Neural Network architecture is presented for the prediction of flood severity. Floods are considered natural disasters that cause wide-scale devastation to areas affected. The phenomenon of flooding is commonly caused by runoff from rivers and precipitation, specifically during periods of extremely high rainfall. Due to the concerns surrounding global warming and extreme ecological effects, flooding is considered a serious problem that has a negative impact on infrastructure and humankind. This paper attempts to address the issue of flood mitigation through the presentation of a new flood dataset, comprising 2000 annotated flood events, where the severity of the outcome is categorised according to 3 target classes, demonstrating the respective severities of floods. The paper also presents various types of machine learning algorithms for …
Total citations
20192020202120222023202452111944
Scholar articles
M Khalaf, AJ Hussain, D Al-Jumeily, T Baker, R Keight… - 2018 IEEE Congress on Evolutionary Computation …, 2018