Suspended sediment load prediction based on soft computing models and Black Widow Optimization Algorithm using an enhanced gamma test

• Published in: Environmental science and pollution research international Vol. 28; no. 35; pp. 48253 - 48273
• Main Authors: Panahi, Fatemeh, Ehteram, Mohammad, Emami, Mohammad
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021; Springer Nature B.V
• Subjects: Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Evolutionary algorithms; Fuzzy logic; hybrids; Iran; Mathematical models; Model accuracy; Optimization algorithms; Parameter uncertainty; Particle swarm optimization; prediction; rain; Rainfall; Research Article; Rivers; sediment contamination; Sediment load; Soft computing; Support vector machines; Suspended load; suspended sediment; Suspended sediments; temperature; Training level; Tributaries; Uncertainty analysis; Waste Water Technology; Water engineering; Water Management; Water Pollution Control; Iran; ANFIS; SVM; Black Widow Optimization Algorithm; Suspended sediment load
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-021-14065-4

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 2 among other models in the Telar River. The MAE of the ANFIS-BWOA, SVM-BWOA, SVM-PSO, SVM-BA, and SVM models were 899.12 (Ton/day), 934.23 (Ton/day), 987.12 (Ton/day), 976.12, and 989.12 (Ton/day), respectively, in the testing level for the Kasilian River. An uncertainty analysis was used to investigate the effect of uncertainty of the inputs (first scenario) and the model parameters (the second scenario) on the accuracy of models. It was observed that the input uncertainty higher than the parameter uncertainty.