Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

نویسندگان

چکیده

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and design operation hydraulic structures. presented study proposes a new method for accurately estimating daily SSs using antecedent discharge sediment information. novel is developed by hybridizing multivariate adaptive regression spline (MARS) Kmeans clustering algorithm (MARS–KM). proposed method’s efficacy established comparing its performance with neuro-fuzzy system (ANFIS), MARS, M5 tree (M5Tree) models predicting at two stations situated on Yangtze River China, according to three assessment measurements, RMSE, MAE, NSE. Two modeling scenarios are employed; data divided into 50–50% model training testing first scenario, test sets swapped second scenario. In Guangyuan Station, MARS–KM showed improvement compared ANFIS, M5Tree methods term RMSE 39%, 30%, 18% scenario 24%, 22%, 8% respectively, while was 34%, 26%, 27% 7%, 16%, 6% Beibei Station. Additionally, provided much more satisfactory estimates only values as inputs.

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ژورنال

عنوان ژورنال: Sustainability

سال: 2021

ISSN: ['2071-1050']

DOI: https://doi.org/10.3390/su13094648