## 1 Introduction
The introduction of a supplemented model in defining the resulting volume of spherical expansion, expansion and deepening of a borehole improved the original method presented in [PERSON] 2018 [2], and further facilitated the use of the application \"Busotine\" when blasting in clays in geotechnical practice. Since the clay is hydroalumosilicate, which means that it absorbs water and therefore becomes brittle, the blasting in the clay can only be carried out in dry months and when the air temperature is between 15 and 25 \({}^{\circ}\)C. The data obtained also showed that there is a dependence of air temperature and precipitation on the time period in the year in which clay exploitation is possible, and guidelines for successful clay exploitation are presented [7,8]. The amount of precipitation has a direct impact on the success of blasting in clay, because when the precipitation in the clay soil during detonation of explosive charge increases, the pressure wave of the explosion has a stronger impact and larger spherical expansions volume occur [9, 10, 11]. In the field of geotechnics and blasting in clay soils, artificial intelligence (AI) was mainly investigated in terms of soil properties prediction models, i.e., the study [12] used genetic programming (GP), support vector regression and ANN-s to model the relationship between volumetric water content in soil and input parameters in the form of solid density, initial water content, and soil suction. Similarly, in study [13], generalized regression neural networks (GRNN) and backpropagation neural networks (BPNN) were used to model soil temperature based on half-hourly gauged air temperature, relative humidity, wind speed, vapor pressure deficit, and solar radiation, while in the study [14], an ANN-based predictor was developed to model dependence between current hourly soil temperature and hourly precipitation rates, previous average soil temperature and average air temperature. Another study [15] used adaptive network-based fuzzy inference system (ANFIS) to predict and evaluate ground vibrations and frequencies caused by blasting in a tunnel tube. Different kernel based Support Vector Machine (SVM) models were used in study [16] to predict peak particle velocity induced by mine blasting, while the model inputs depend on results of boosted-Chi-Squared Automatic Interaction Detection (CHAID). Scarcity of research has been identified that investigates reliable predictions of the influence of air temperature and precipitation on blasting performance in clay soil. By using Artificial Neural Networks (ANN) with meteorological input data in the form of precipitation and air temperature in addition to blasting related input data, it is possible to determine the optimal amount of explosive charge and the length of the borehole stem for successful blasting in clay soil. However, when dealing with sequential data, as i.e., time series data like precipitation and/or air temperature, often a time dependence is present (data at time \(t\), depends on the data preceding it), so the application of a traditional feedforward neural network is limited [17]. Therefore, when processing timeseries data (like i.e., meteorological measurements) Recurrent Neural Networks [18] (RNN), and especially the more advanced version of RNNs, namely, Long Short-term Memory (LSTM) [19] networks, are often chosen. Several applications of LSTM networks in hydrology [17,20,21, 22], remote sensing [23,24] and air quality modelling [25] tasks have been found. However, to our knowledge no publishedresearch investigates the implementation of LSTM neural networks and meteorological data influence on blasting in soft clay. Therefore, the integration of ANNs and LSTMs in the field of blasting to predict the amount of the explosive charge and stemming length is presented for the first time.