10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration

      , , , , ,
      Applied Sciences
      MDPI AG

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In mining and civil engineering applications, a reliable and proper analysis of ground vibration due to quarry blasting is an extremely important task. While advances in machine learning led to numerous powerful regression models, the usefulness of these models for modeling the peak particle velocity (PPV) remains largely unexplored. Using an extensive database comprising quarry site datasets enriched with vibration variables, this article compares the predictive performance of five selected machine learning classifiers, including classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for PPV analysis. Before conducting these model developments, feature selection was applied in order to select the most important input parameters for PPV. The results of this study show that RF performed substantially better than any of the other investigated regression models, including the frequently used SVM and ANN models. The results and process analysis of this study can be utilized by other researchers/designers in similar fields.

          Related collections

          Most cited references8

          • Record: found
          • Abstract: not found
          • Article: not found

          Application of artificial neural networks for the prediction of the compressive strength of cement‐based mortars

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            An introduction to variable and feature selection

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Putting ground vibration predictors into practice

                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                ASPCC7
                Applied Sciences
                Applied Sciences
                MDPI AG
                2076-3417
                February 2020
                January 27 2020
                : 10
                : 3
                : 869
                Article
                10.3390/app10030869
                34306737
                061dd121-7d4a-4699-a8ae-c68be64b5311
                © 2020

                https://creativecommons.org/licenses/by/4.0/

                History

                Comments

                Comment on this article