Knowledge Extraction from Artificial Neural Networks for Rainfall-Runoff Model Combination Systems

Abstract

Artificial neural networks (ANNs) are generally regarded to behave as black-box systems. Recent research explores various methods that can provide an insight into the internal connections and relationships existing within the network. Various methodologies that understand the input variable contribution are analyzed in detail, and rule extraction approaches for a trained artificial neural network are addressed. To understand the contribution of input variables to rainfall-runoff model combination systems, this paper for the first time investigates knowledge extraction from artificial neural network, which is used to combine the results obtained from different competing rainfall-runoff models, using three different approaches: (1) Garson’s algorithm; (2) neural interpretation diagram (NID); and (3) sensitivity analysis (SA). For the purpose of investigating knowledge extraction techniques, the trained multilayer perceptron neural network to combine the results from four different rainfall-runoff models for the Brosna Catchment located in Ireland has been chosen. The results of the three approaches obtained in this study indicate that they can be used to reduce the complexity of rainfall-runoff model combination systems by eliminating the least significant contributing input variables. Based on these approaches, the paper helps to provide guidance in the optimal number of rainfall-runoff models that best perform in a combination system.