A comparison of performance of SWAT and machine learning models for predicting sediment load in a forested Basin, Northern Spain
نویسندگان
چکیده
In water bodies, sediment transport is a potential source of numerous negative effects on resource projects and can damage environmental services. Two machine learning (ML) algorithms, the M5P random forest (RF) models, have been explored for first time as alternatives to Soil Water Assessment Tool (SWAT) model estimate suspended load (SSL) in Oskotz river basin, forested experimental basin Navarra, northern Spain. ML streamflow precipitation data were used daily SSL, testing different combinations these inputs. The models more accurate than physically based hydrological SWAT all input scenarios tested at scale. Moreover, although results improved considerably monthly scale, statistics obtained generally inferior compared models. For best combination inputs, demonstrated superior ability SSL (R2 = 0.73, MAE 135.04, RSR 0.54, NSE 0.71 PBIAS 5.19), RF 0.72, 143.39, 0.57, 0.67 11.60) 181.24, 0.65, 0.57 -1.27). average loads winter, season with highest generation 2,094.04, 1,831.08 2,242.67 tonnes M5P, SWAT, respectively, an observed 1,878.16 tonnes. These indicate that are suitable simulating fluvial production since they model, which also requires set up calibrate. However, does not require input, it remains useful achieving acceptable basins limited data.
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ژورنال
عنوان ژورنال: Catena
سال: 2022
ISSN: ['0008-7769', '1872-6887', '0341-8162']
DOI: https://doi.org/10.1016/j.catena.2021.105953