A Pareto-optimal moving average-multigene genetic programming model for rainfall-runoff modelling

The effectiveness of genetic programming (GP) in rainfall-runoff modelling has been recognized in recent studies. However, it may produce misleading estimations if autoregressive relationship between runoff and its antecedent values is not carefully considered. Meanwhile, GP evolves alternative models of different accuracy and complexity, where selecting a parsimonious model from such alternatives needs extra attention. To cope with these problems, this paper proposes a new hybrid model that integrates moving average filtering with multigene GP and uses Pareto-front plot to optimize the evolved models through an interactive complexity-efficiency trade-off. The model was applied to develop single- and multi-day-ahead rainfall-runoff models and compared to stand-alone GP, multigene GP, and multilayer perceptron as the benchmarks. The results indicated that the new model provides substantial improvements relative to the benchmarks, with prediction errors 25-60% lower and timing accuracy 80-760% higher. Moreover, it is explicit and parsimonious, motivating to be used in practice.