An ensemble simulation approach for artificial neural network: An example from chlorophyll a simulation in Lake Poyang, China

- We examined the values of ensemble simulation to artificial neural network (ANN).
- Four ANN ensemble models were developed to predict chlorophyll a in Lake Poyang.
- Ensemble simulation is efficient to improve the model fit and stability of ANN model.

Artificial neural network (ANN) models have been widely used in environmental modeling with considerable success. To improve the reliability of ANN models, ensemble simulations were applied in this study to develop four ANN ensemble models for chlorophyll a simulation in the largest freshwater lake (Lake Poyang) in China. Reliability (evaluated by model fit and stability) of these ANN ensemble models was compared with that of single ANN models from ensemble members. The model fit of these single ANN models varied significantly over repeated runs, indicating the unstable performance of the single ANN models. Comparing with the single ANN models, the ANN ensemble models showed a better model fit and stability, implying the potential of ensemble simulation in achieving a more reliable model. An ensemble size of 30 was adequate for the ANN ensemble models to achieve a good model fit, while an ensemble size of 50 was adequate to achieve good stability. This case study highlighted both the necessity and potential of the ensemble simulation approach to achieve a reliable ANN model with good model fit and stability.