## 1 Introduction  
Karstification geomorphology is widely distributed and large in Southwestern China [1; 2]. In these areas where karst is developed, the seepage control of karst fissured strata is a common challenge [3; 4; 5; 6; 7; 8]. Grouting method has been more and more widely used as an effective means to control water leakage disaster [9; 10; 11; 12]. Karst curtain grouting is a reliable method to solve the problem of karst leakage in view of the special conditions in karstification areas [13]. In particular, with the successive use of many new materials and equipment, the effectiveness of grouting has been greatly improved. However, due to the nonuniformity and randomness of karst-crack distribution, the prediction of the unit grouting quantity in karst curtain grouting has always been one of the important problems in the design and construction of karst curtain grouting [14; 15; 16]. If the unit grouting quantity can be reasonably predicted in the design stage of karst curtain grouting, it can provide a reliable basis for the design of grouting materials. This avoids the fact that the curtain body cannot meet the anti-seepage standard of curtain grouting design due to the insufficient injection of grouting materials in the process of curtain grouting construction, and then affect the quality of karst curtain grouting and the effect of anti-seepage. Especially in the curtain grouting project of karstification mine, because of the particularity of the mine itself, the quality of curtain grouting and the effect of water shutoff must be successful at one time. The main criterion that can make such a judgment is the anti-seepage standard of mine curtain grouting [17]. Therefore, the prediction of the unit grouting quantity in karst curtain grouting is of great significance to the design and construction of karst curtain grouting. At present, the prediction methods of the unit grouting quantity in karst curtain grouting can be divided into three categories. The first is the artificial experience prediction method, which is basically based on the engineering experience of technicians and relies on manual experience to predict the unit grouting quantity. [PERSON] et al. [18] used the Bakhtiari dam site as a research case to establish an empirical estimation method of the unit grouting quantity. [PERSON] et al. [19] established a prediction method of the unit grouting quantity in grouting construction based on empirical knowledge according to grouting characteristics and hydrogeological data. However, due to the roughness and difference of empirical estimation, the results can only be used as a reference for the design of grouting materials and cannot be used as a basis. The second is the traditional statistical prediction method, which mainly uses regression cluster analysis to establish the prediction model on the basis of collecting a large number of grouting data. [PERSON] et al. [20] established a statistical prediction model of the unit grouting quantity based on the statistics of permeability and groutability of Ostur dam site rock mass. [PERSON] et al. [21] obtained a prediction model of the unit grouting quantity in the karst area by counting the curtain grouting data of Zhongguan iron mine for the past three years. However, the application effect of this kind of method is not very ideal, and it is not very good to use and popularize. The third is intelligent grouting prediction method, which applies artificial intelligence to grouting engineering of rock mass. Based on all kinds of prediction theory, the intelligent prediction model of the unit grouting quantity is established to realize the prediction of the unit grouting quantity. According to the prediction theory, this kind of method includes the prediction method of fuzzy theory, the prediction method of grey theory, and the prediction method of artificial neural network theory. [PERSON] et al. [22] established a fuzzy comprehensive evaluation model for grouting scheme optimization to predict the unit grouting quantity.