## 1 Introduction  
Determination of physio-mechanical parameters of soil is a significant task for the economical and safe design of civil engineering structures. Soil compression coefficient (SCC) is one of these parameters which reflects the potential of volume decrease in the soil [1]. The importance of this parameter lies in the changes in the structure of soil coming after the mentioned compression [2, 3]. More clearly, once a load is applied to saturated soils, pore water pressure is increased, and the soil mass experiences a reduction in volume over time in the presence of drainage. This process leads to soil deposition induced by SCC [4]. Utilizing typical methods comprising soil sampling and implementing field measurement or laboratory tests requires spending a massive cost as well as time and attention. Moreover, temporal and spatial variations in the problem circumstance have made this task more difficult [5; 6; 7]. Hence, employing indirect SCC measurement methods has been more and more highlighted during the last decades. Artificial intelligence-based techniques are one of the most popular predictive models which have shown high capability in modeling plenty of engineering problems, and especially, geotechnical engineering [8; 9]. These models use non-linear relationships to analyze the relationship between target variable(s) and influential factors. In literature, artificial neural networks (ANNs) have been successfully used for the SCC estimation. [PERSON], et al. [10], for example, employed an ANN for predicting the compression and recompression indices using basic soil properties of initial void ratio, the natural water content, plasticity index, and liquid limit. It was found that this model can satisfactorily predict the compression index. However, the recompression results were not as accurate as of that. As an alternative to empirical formulas for calculating the soil compression index, [PERSON] and [PERSON] [11] used ANN for soil information on sites over the Republic of Korea. Their study proved the higher reliability of ANN compared to empirical techniques. Similarly, other data mining techniques like genetic expression programming and adaptive neuro-fuzzy inference system (ANFIS) have outperformed existing empirical formulas [12; 13; 14]. [PERSON], et al. [5] demonstrated the higher effectiveness of ANN (correlation = 0.63) in comparison with regression method (correlation = 0.47). In some studies, scholars have conducted comparison between machine learning methods. The accuracy of three well-known intelligent approaches, namely minimax probability machine regression, genetic programming regression, and multivariate adaptive regression splines (MARS) was evaluated by [PERSON], et al. [15]. Considering the applied accuracy criteria, it was shown that the MARS is a reliable predictive model for simulating the SCC. Likewise, the efficiency of three common predictive models of support vector machine (SVM), ANN, and ANFIS was explored along with Monte Carlo sensitivity analysis method by [PERSON], et al. [16]. Notably, the applied Monte Carlo method revealed the higher contribution of four input parameters namely clay, degree of saturation, specific gravity and depth of sample. Eventually, they concluded that the SVM performs more efficiently than two other colleagues. More recently, hybrid swarm science has been broadly used in various geotechnical engineering issues (e.g., landslide modeling) for prevailing the computational shortcomings of regular data mining techniques [8; 17]. For the purpose of SCC estimation, multi-gene genetic programming was used by [PERSON], et al. [18] for compression index modeling of fine-grained soil. The results of the proposed technique found to be more promising than traditional models. In another application of hybrid algorithms by [PERSON], et al. [19], particle swarm optimization (PSO) was synthesized with ANN to forecast the SCC of an urban project in Vietnam.