Stream flow predictions using nature-inspired Firefly Algorithms and a Multiple Model strategy - Directions of innovation towards next generation practices

摘要

Graphical abstractDisplay OmittedHighlights•FireFly Algorithm (FFA) is synthesised with Multi-Layer Perceptrons – MLP-FFA.•MLP-FFA is compared with MLP using traditional Levenberg-Marquardt (LM): MLP-LM.•Improved FFA predictions are significant, attributed to identifying global minimum.•Another potential improvement arises by Multiple Models (MM) of MLP-FFA and MLP-LM.•FFA and MM are identified as two directions for Innovations towards next generation.AbstractStream flow prediction is studied by Artificial Intelligence (AI) in this paper using Artificial Neural Network (ANN) as a hybrid of Multi-Layer Perceptron (MLP) with the Levenberg–Marquardt (LM) backpropagation learning algorithm (MLP-LM) and (ii) MLP integrated with the Fire-Fly Algorithm (MLP-FFA). Monthly stream flow records used in this prediction problem comprise two stations at Bear River, the U.S.A., for the period of 1961–2012. Six different model structures are investigated for both MLP-LM and MLP-FFA models and their results were analysed using a number of performance measures including Correlation Coefficients (CC) and the Taylor diagram. The results indicate a significant improvement is likely in predicting downstream flows by MLP-FFA over that by MLP-LM, attributed to identifying the global minimum. In addition, an emerging multiple model (ensemble) strategy is employed to treat the outputs of the two MLP-LM and MLP-FFA models as inputs to an ANN model. The results show yet another further possible improvement. These two avenues for improvements identify possible directions towards next generation research activities.