Evolving spiking neural networks methods for classification problem: a case study in flood events risk assessment

Abstract

Analysing environmental events such as predicting the risk of flood is considered as a challenging task due to the dynamic behaviour of the data. One way to correctly predict the risk of such events is by gathering as much of related historical data and analyse the correlation between the features which contribute to the event occurrences. Inspired by the brain working mechanism, the spiking neural networks have proven the capability of revealing a significant association between different variables spike behaviour during an event. Personalised modelling, on the other hand, allows a personal model to be created for a specific data model and experiment. Therefore, a personalised modelling method incorporating spiking neural network is used to create a personalised model for assessing a real-world flood case study in Kuala Krai, Kelantan based on historical data of 2012-2016 provided by Malaysian Meteorological Department. The result shows that the method produces the highest accuracy among the selected compared algorithms.