Spatial Interpolation of Soil Temperature and Water Content in the Land-Water Interface Using Artificial Intelligence
نویسندگان
چکیده
The distributed measured data in large regions and remote locations, along with a need to estimate climatic for point sites where no have been recorded, has encouraged the implementation of spatial interpolation techniques. Recently, increasing use artificial intelligence become promising alternative conventional deterministic algorithms interpolation. present study aims evaluate some machine learning-based against strategies interpolating soil temperature from region southeast Canada an area 1000 km by 550 km. radial basis function neural networks (RBFN) deep learning approach were used railroad after spline method failed interpolate gridded on desired locations. showed weaknesses areas sudden changes. This limitation did not improve even nonlinearity. Although both had successful performances sharp transition areas, outperformed former normalized RMSE 9.0% 16.2% R-squared 89.2% 53.8%. finding was confirmed same investigation water content.
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ژورنال
عنوان ژورنال: Water
سال: 2023
ISSN: ['2073-4441']
DOI: https://doi.org/10.3390/w15030473