Counter-prediction method of the spatial series on the example of the dust content in the snow cover

№1 (2022) УДК 504.064.2.001.18https://doi.org/10.47148/1609-364X-2022-1-32-39 Butorova A.S., Sergeev A.P., Shichkin A.V., Buevich A.G., Baglaeva E.M.,Sergeeva M.V. Keywords: counter-prediction method, missing data recovering, artificial neural networks, snow cover, dust. Section: Modeling geo objects and geo-processes

Prediction of prospective areas for gold-cupper-porphyry type of mineralization based on analysis of informative feature search and their functional and correlation relationships.

№2 (2016) Ivanova J.N., Bochneva A.A. Section: Modeling geo objects and geo-processes Keywords: gold-cupper-porphyry type of mineralization, expert assessments, risk zones, bifurcation diagram, predictive function, the Polar Urals.

Operative regional prediction activity of landslide process on the example of Sochi polygon.

№4 (2018) Vozhik A.A.,Shamurzaeva D.A. Section: Geoecology Keywords: Sochi polygon, landslide process, prediction, meteorological factors of activation, landslide hazard.

Optimization of the initial data division for prediction chromium spatial distribution by a feedforward neural network.

№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.

Development of a methodology for empirical scientific research using standard libraries of automatic machine learning on the example of predicting oil and gas reservoirs in the Tomsk Region

№1 (2024) Vladimir A. Ostanin УДК 004.89, 550.8.05, 167.7 https://doi.org/10.47148/1609-364X-2024-1-4-18 Key words: interpretation of geophysical data, design of geological exploration works for oil and gas, design of oil and gas field development, automatic machine learning, AutoML, AI, autokeras, SHAP, LIME … Continue reading →

Geoinformation technologies of computer-aided design of public places in urban areas: problems and experience (on the example of Saransk)

№ 4 (2023) Semina I.A., Malakhova O.E., Teslenok S.A., Yakovenko N.V. УДК 004:004.9:528:528.94:528.48:711.4:912.4:911(470.345) https://doi.org/10.47148/1609-364X-2023-4-15-28 Key words: geoinformation technologies, computer design, three-dimensional information modeling of cities, urban areas, public places, planning and design, AutoCAD program Section: Application of GIS technologies

Individual and pair representativeness of sampling points selection in interpolation tasks of the heavy metals distribution in the topsoil

№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

Errors in average strength estimates of sedimentary rock masses in South Yakutia based on geometric induction sounding data

№1 (2023) Neradovskii L.G. УДК 550.370+552.08 https://doi.org/10.47148/1609-364X-2023-1-48-62 Key words: sandstone, dolomite, strength, geometric EMI sounding, high-frequency vertical magnetic dipole field, attenuation coefficient, probabilistic model, prediction error Section: Modeling geo objects and geo-processes

DDLAFS — QGIS plugin for dominant directions of the local active fault system estimation

№4 (2022) Emelyanov I.V., Nekrasova A.K. УДК 004.67 https://doi.org/10.47148/1609-364X-2022-4-54-62 Key words: QGIS, plugin, anisotropic propagation, dominant direction, active faults Section: Conference proceedings ITES-2022

Application of the permutation method to the assessment of predictive ability of the models of spatial distribution of copper and iron concentrations in the topsoil

№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

Geoinformatika

Journal of geoinformation technologies in geology and geoecology

Search Results for: prediction