An approach is proposed for preparing geospatial datasets with missing values for the modeling of exogenous geological processes using neural networks. Based on an analysis of current data reconstruction techniques, the choice of the iterative imputation algorithm with Random Forest as the base estimator is justified. Key advantages of this method include its ability to capture complex nonlinear relationships, robustness to noise, and preservation of the correlation structure of features. The algorithm was implemented in the Python environment. The methodology was tested using monitoring data on the technical condition of engineering structures of the Kyiv-Pechersk Lavra. In the test case, 19% of missing values were reconstructed, achieving a root mean square error of approximately 5% and a coefficient of determination (R²) exceeding 0.9, which confirmed the high accuracy of recovery. The reconstructed data improved the quality of training datasets and the stability of geohazard zone predictions by neural networks. The proposed approach is recommended for the preprocessing of complex-structured geospatial data in tasks related to the prediction of hazardous geological processes.
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