## 5 Conclusions  
For efficient and safe uranium mining using the ISL method, it was necessary to determine as accurately as possible the lithological composition of the host rocks and the depth of permeable and impermeable strata, as well as the filtration properties of the host rocks. The necessary dependencies could be determined by comparing data from hydrogeological studies carried out at the exploration stage with logging data. In Kazakhstan, a method for determining the filtration properties was used, developed more than 50 years ago, which often produced incorrect results. Inaccuracies in the assessment of the filtration properties of host rocks led to the incorrect assessment of recoverable reserves and poor production planning. The currently accepted technique was based on the dependence of \(K_{f}\) on the average value of the AR within the boundaries of the selected lithological layer and had the following disadvantages:  
* Only the AR curve data were used; if the AR data were poorly recorded, the results would be unreliable. * When interpreting data from acidified blocks, where the properties of rocks were distorted by the action of acid, the values of the AR turned out to be underestimated, and therefore, the calculation of the filtration properties was not correct. * Since a downhole tool with a distance between electrodes of 1 m was used to record AR logs in the fields of Kazakhstan, it was possible to reliably measure the average resistivity value only for lithological layers with a thickness of more than 2 m. Therefore, the adopted technique was not suitable for thin intervals (<2 m). To overcome the shortcomings of the existing approach, we proposed a method for calculating the filtration coefficient based on the use of regression models [48]. The proposed model received electric logging data as an input and the calculated filtration coefficient as an output. Thanks to a wider range of regression models, it was possible to improve the previously obtained result. The LGBM regression model with AR and LC as the input variables demonstrated the best results (\(R^{2}\) = 0.710, R = 0.845). At the same time, the proposed method had the following limitations:  
* It was not applicable to fields where exploration had been carried out for a long time, and not all the necessary data were available. * The learning process depended on the lithological code set by the expert, which could be wrong, especially in the case of acidified blocks. In this regard, directions for further research are the following:* Exploring the possibility of transferring the trained model to similar fields for which there are no data required for training;
* Improving the reliability of determining the lithological code of the rock during lithological classification;
* Automatic identification of zones of technological acidification by the characteristic distortion of the AR curve. Also, as one of the future research directions, it is suggested to use the construction of a forecasting model based on Fuzzy Decision Trees [49]. Conceptualization, R.M., Y.K. and E.Z. ; methodology, R.M. ; software, A.S.; validation, Y.K., E.Z., V.L. and A.S.; formal analysis, R.M. and N.Y.; investigation, K.A., V.L., E.M. and Y.K. ; resources, Y.K., R.M. and E.M.; data curation K.A., A.S. and V.L. ; writing--original draft preparation, R.M., Y.K. and E.Z. ; writing--review and editing, Y.K. and N.Y.; visualization, Y.K. ; supervision, R.M. ; project administration, Y.K. ; funding acquisition, N.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No.