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|Title:||Multilayer perceptron, generalized regression neural network, and hybrid model in predicting the spatial distribution of impurity in the topsoil of urbanized area|
|Authors:||Subbotina, I. E.|
Buevich, A. G.
Shichkin, A. V.
Sergeev, A. P.
Tarasov, D. A.
Tyagunov, A. G.
Sergeeva, M. V.
Baglaeva, E. M.
|Publisher:||American Institute of Physics Inc.|
|Citation:||Multilayer perceptron, generalized regression neural network, and hybrid model in predicting the spatial distribution of impurity in the topsoil of urbanized area / I. E. Subbotina, A. G. Buevich, A. V. Shichkin et al. // AIP Conference Proceedings. — 2018. — Vol. 1982. — 20004.|
|Abstract:||The study is based on the data obtained as a result of soil screening in the city of Noyabrsk, Russia. A comparison of two types of neural networks most commonly used in this type of research was carried out: multi-layer perceptron (MLP), generalized regression neural network (GRNN), and a combined MLP and ordinary kriging approach (MLPRK) for predicting the spatial distribution of the chemical element Chromium (Cr) in the surface layer of the urbanized territory. The model structures were developed using computer modeling, based on minimizing of a root mean squared error (RMSE). As input parameters, the spatial coordinates were used, and the concentration of Cr - as the output. The hybrid MLPRK approach showed the best prognostic accuracy. © 2018 Author(s).|
|Conference name:||2nd International Conference on Mathematical Methods and Computational Techniques in Science and Engineering|
|Conference date:||16 February 2018 through 18 February 2018|
|Appears in Collections:||Научные публикации, проиндексированные в SCOPUS и WoS CC|
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