№3 (2023) УДК 504.064.2.001.18 https://doi.org/10.47148/1609-364X-2023-3-63-70 Baglaeva E.M., Sergeev A.P., Shichkin A.V., Buevich A.G., Butorova A.S. Key words: representativeness of points, sampling, heavy metals, artificial neural networks, choice of training subset Section: Geoecology
№2 (2022) Sergeev A.P., Butorova A.S., Shichkin A.V., Buevich A.G., Baglaeva E.M., Subbotina I.E. УДК 504.064.2.001.18 https://doi.org/10.47148/1609-364X-2022-2-42-53 Key words: permutation method, randomization, predicted values, observed values, spatial distribution, predictive ability assessment, artificial neural networks Section: Modeling geo objects and geo-processes
DOI:10.47148/1609-364X-2022-2 Potential of the magnetotelluric data analysis block of the GIS INTEGRO complexKupriyanov I.S. The use of geoinformation technologies in applied tasks of agricultural lands monitoringAfanasyev V.S. Geoinformation technology for road surface planning and accountingEremeev S.V., Egai M.V., Abakumov A.V. Improving the … Continue reading
№4 (2019) Buevich A.G.,Sergeev A.P.,Rakhmatova A.Yu., Baglaeva E.M.,Shichkin A.V., Subbotina I.E.,Sergeeva M.V.,Markelov Yu.I. Section: Modeling geo objects and geo-processes Keywords: artificial neural networks, multilayer perceptron, modeling, sampling.
Optimization of the initial data division for prediction chromium spatial distribution by a feedforward neural network.Buevich A.G., Sergeev A.P., Rakhmatova A.Yu., Baglaeva E.M., Shichkin A.V., Subbotina I.E., Sergeeva M.V., Markelov Yu.I. … » The article proposes an approach to optimize … Continue reading