Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction

There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of theartificial neural network(ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers-whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)-for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5-20%. Scenario 2 achieved the highestR(2)when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load.

Automated summary

This study explored the effectiveness of artificial neural network (ANN) models in predicting suspended sediment load (SSL) and found that a hybrid ANN-WA model outperformed other ANN models, leading to improved accuracy in SSL prediction. An optimisation algorithm was introduced in a multi-objective framework to determine the best combination of inputs for predicting SSL, resulting in significant improvement in accuracy when hybridised with the ANN model and WA algorithm.