ArticleScientific reports2024
Suspended sediment load prediction using sparrow search algorithm-based support vector machine model.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A predictive model for flow index performance of pit drip irrigation emitters using BP-PSO algorithm.Scientific reports · 2026Article
- Investigating the impact of meteorological parameters on daily soil temperature changes using machine learning models.Scientific reports · 2025Article
- Analysis of influenza-like illness trends in Saudi Arabia: a comparative study of statistical and deep learning techniques.Osong public health and research perspectives · 2025Article
- Exploring PMScientific reports · 2025Article
- A hybrid technique to enhance the rainfall-runoff prediction of physical and data-driven model: a case study of Upper Narmada River Sub-basin, India.Scientific reports · 2024Article
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5 authors.
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Abstract
Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R
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