ArticleEnvironmental science and pollution research international2021
Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction.
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Forecasting urban water demand using different hybrid-based metaheuristic algorithms' inspire for extracting artificial neural network hyperparameters.Scientific reports · 2024Article
- Suspended sediment load prediction using sparrow search algorithm-based support vector machine model.Scientific reports · 2024Article
- Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study.Heliyon · 2023Article
- Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm.Environmental science and pollution research international · 2022Article
- Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms.Scientific reports · 2022Article
- A hybrid novel SVM model for predicting COEnvironmental science and pollution research international · 2021Article
- Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete.Environmental science and pollution research international · 2021Article
- Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models.Environmental science and pollution research international · 2021Article
- Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia.Scientific reports · 2021Article
- Suspended sediment load prediction based on soft computing models and Black Widow Optimization Algorithm using an enhanced gamma test.Environmental science and pollution research international · 2021Article
- Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing.Sensors (Basel, Switzerland) · 2021Article
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8 authors.
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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 the artificial 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 highest R
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