Using evolutionary algorithms for continuous simulation of long-term reservoir inflows

Abstract

Data-driven models provide upgradeable environments to simulate inflow to reservoirs. An important aspect in the development of an adaptive neural fuzzy inference system (ANFIS) model is to choose the correct procedure (i.e. efficient and effective) to train the model. In this study, a daily timescale ANFIS-based model was developed to simulate aggregated monthly long-term inflow to the Ross River reservoir in northern Queensland, Australia. The suitability of different evolutionary algorithms (EAs) was evaluated to train an ANFIS-based model, including a genetic algorithm (GA), particle swarm optimisation (PSO), shuffled frog leaping algorithm, biogeography-based optimisation, harmony search algorithm, differential evolution algorithm, invasive weed optimisation (IWO) and a cultural algorithm. Four indices – the root mean square error, Nash–Sutcliffe efficiency, reliability index and vulnerability index – were used to measure the performance of the model. It was found that application of either IWO, GA or PSO provided accurate simulated inflow time series. The outputs obtained using the other EAs were not sufficiently accurate. Use of a coupled EA–ANFIS-based model was found to improve the accuracy of simulating long-term monthly inflow compared with other models used in a few previous recent studies.