## 2 Materials and Methods
### Experimental Database  
While in the process of developing a forecast model, much attention has been devoted to the computational model itself, only marginal attention has been paid by researchers to the actual database used for the development, training, and validation of the model.  
Figure 4: Kernels’ formula.  
Figure 3: SVM arrangement for regression.  
Without underestimating the high importance and added value of research efforts towards the development of new computational models, we strongly believe that the reliability of the database is of utmost importance in achieving the ultimate goal of a reliable forecast. In fact, in addition to reliable data, a reliable database must comprise a sufficient amount of data, covering the full range of parameter (input and output) values that influence the problem under investigation.  
It should be noted that the term \"sufficient amount of data\" does not necessarily imply a high amount of data, but rather datasets that cover a wide range of combinations of input parameter values, thus assisting in the model capability to simulate the problem. The demand for a reliable and capable database is especially crucial in the case of experimental databases, that is databases which are compiled using experimental results. In this case, high deviation between experimental values is frequently noticed, not only between experiments conducted by different research teams and laboratories, but even between datasets that derive from experiments conducted on specimens of the same synthesis, produced by the same technicians, cured under the same conditions and tested implementing the same standards and the same testing instruments.  
In light of the above discussion, a big experimental database consisting of 166 datasets was composed. To provide a significant amount of data for the calculation of the environmental effects of mine blasting, we studied four quarries in Malaysia. Details for these sites are presented in Figure 5. The goal of mine explosion is to provide aggregate material for various applications. Depending on the weather, six to twelve mine explosions are performed each month.  
Among these 166 data samples and investigated blasting events, 80 blasting events were investigated in Kulai quarry site. Then, 31, 29, and 26 blasting events were investigated in Bukit Indah, Senai Jaya, and Taman Bestari quarry sites, respectively. The lowest hole depth (10 m) was in the Kulai site, while the biggest hole depth (28 m) was in the Bukit Indah site. We compiled a database of 166 data samples from field measurements. The following parameters influencing the blast effect were recorded: powder factor (kg/m\({}^{3}\)), spacing (m), stemming length (m), burden (m), the maximum charge per delay (kg), and the blast-face distance to the monitoring point (m). Actually, the mentioned parameters are considered as a common blasting data and have been utilized by many published works in literature [1; 17; 19; 22]. It is also important to mention that the most important input factors in measuring/predicting the PPV are the maximum charge per delay and the distance from the blast-face [21; 22; 96; 97]. In the established database, we used a 115 mm diameter for blast-holes. Fine gravel as a well-known stemming material was used in these operations. We recorded the PPV using a VibraZEB seismograph equipment at specific locations. Table 2 presents a summary of the measured input and output variables including unit, maximum, minimum, mean, and standard deviation. The frequency distributions of the PPV employed in this investigation are presented in Figure 6.  
Figure 5: Location information of the quarry sites.