Ehteram, Mohammad and Ahmed, Ali Najah and Latif, Sarmad Dashti and Huang, Yuk Feng and Alizamir, Meysam and Kisi, Ozgur and Mert, Cihan and Ahmed El-Shafie, Ahmed Hussein Kamel (2021) Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction. Environmental Science and Pollution Research, 28 (2). pp. 1596-1611. ISSN 0944-1344, DOI https://doi.org/10.1007/s11356-020-10421-y.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | Institute of Postgraduate Studies and Research (IPSR) of Universiti Tunku Abdul Rahman |
| Uncontrolled Keywords: | Soft computing model; Whale algorithm; ANN; Suspended sediment load; Optimisation; Iran |
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QH Natural history |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 08 Jul 2022 01:16 |
| Last Modified: | 08 Jul 2022 01:16 |
| URI: | http://eprints.um.edu.my/id/eprint/33974 |
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