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Application of soft computing models with input vectors of snow cover area in addition to hydro-climatic data to predict the sediment loads

Ul Hussan, W.; Shahzad, M. K.; Seidel, F.; Nestmann, F.

Abstract:
The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000120768
Veröffentlicht am 12.07.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wasser und Gewässerentwicklung (IWG)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2073-4441
KITopen-ID: 1000120768
Erschienen in Water
Band 12
Heft 5
Seiten Art. Nr.: 1481
Vorab online veröffentlicht am 22.05.2020
Schlagwörter suspended sediment concentrations, Gilgit basin, snow cover fraction, artificial neural network, MARS model, Hindukush
Nachgewiesen in Scopus
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