ArticleEnvironmental science and pollution research international2021
Suspended sediment load prediction based on soft computing models and Black Widow Optimization Algorithm using an enhanced gamma test.
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 6 papers.
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6 citing papers in PubMed, 35 citations in OpenAlex.
- An Intelligent Temperature Compensation Method for Pressure Sensors Under High-Temperature and High-Pressure Conditions Based on a Modified Slime Mold Algorithm.Micromachines · 2026Article
- Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique.Scientific reports · 2024Article
- Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study.Heliyon · 2023Article
- Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH.Environmental science and pollution research international · 2022Article
- An RBF neural network based on improved black widow optimization algorithm for classification and regression problems.Frontiers in neuroinformatics · 2022Article
- Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review.Evolving systems · 2022Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The suspended sediment load (SSL) prediction is one of the most important issues in water engineering. In this article, the adaptive neuro-fuzzy interface system (ANFIS) and support vector machine (SVM) were used to estimate the SLL of two main tributaries of the Telar River placed in the north of Iran. The main Telar River had two main tributaries, namely, the Telar and the Kasilian. A new evolutionary algorithm, namely, the black widow optimization algorithm (BWOA), was used to enhance the precision of the ANFIS and SVM models for predicting daily SSL. The lagged rainfall, temperature, discharge, and SSL were used as the inputs to the models. The present study used a new hybrid Gamma test to determine the best input scenario. In the next step, the best input combination was determined based on the gamma value. In this research, the abilities of the ANFIS-BWOA and SVM-BWOA were benchmarked with the ANFIS-bat algorithm (BA), SVM-BA, SVM-particle swarm optimization (PSO), and ANFIS-PSO. The mean absolute error (MAE) of ANFIS-BWOA was 0.40%, 2.2%, and 2.5% lower than those of ANFIS-BA, ANFIS-PSO, and ANFIS models in the training level for Telar River. It was concluded that the ANFIS-BWOA had the highest value of R
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