Inference and Multi-level Learning in a Belief Rule-Based Expert System to Predict Flooding

Floods are one of the most dangerous catastrophic events. By the year 2050 flooding due to rise of ocean level may cost one trillion USD to coastal cities. Since flooding involves multi-dimensional elements, its accurate prediction is difficult. In addition, the elements cannot be measured with 100% accuracy. Belief rule-based expert systems (BRBESs) can be considered as an appropriate approach to handle this type of problem because they are capable of addressing uncertainty. However, BRBESs need to be equipped with the capacity to handle multi-level learning and inference to improve its accuracy of flood prediction. Therefore, this paper proposes a new learning and inference mechanism, named joint optimization using belief rule-based adaptive differential evolution (BRBaDE) for multi-level BRBES, which has the capability to handle multi-level learning and inference. Various machine learning methods, including Artificial Neural Networks (ANN), Support Vector Machine (SVM), Linear Regression and Long Short Term Memory have been compared with BRBaDE. The result exhibits that our proposed learning mechanism performs betters than learning techniques as mentioned above in terms of accuracy in flood prediction. Contribution-A new learning and inference mechanism for multi-level BRBES has been proposed in this paper, which has the capability to handle multi-level learning and inference.