Modern Machine Learning Techniques for Univariate Tunnel Settlement Forecasting: A Comparative Study

Algorithm 1

PSO algorithm looking for the optimal parameter set for SVR.

Input: Searching space of vector (C, γ, k), where C ranges from 1 to 10000; γ ranges from

-100 to 100; and k is the rolling window size, ranges from 1 to 20.

Output: The optimal values of C, γ, k based on MAPE evaluation of SVR.

Step 1: For each particle p, a location vector l_p and a velocity vector v_p are assigned.

Step 2: For each particle p, the fitness function is evaluated, which is the MAPE value of

SVR using this particular particle’s location vector.

Step 3: At each iteration, if the fitness function is not satisfied, all particles update their

historical optimal location h and global optimal location g according to their current

location and velocity.

Step 4: When the maximum iteration is reached, or the MAPE value is less than a

pre-defined value, the global optimal location g in the search space is outputted.