<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The application of robotics continues to advance and lends itself to various disciplines. Previous work has shown how Markov decision processes can be integrated in an algorithm to handle pipeline inspection. This has been extended to building models and addressing the errors in the model from the data collected. This paper made use of data collected by simulated robots in a pipeline environment, to build an ensemble. Five models were explored and analysed viz: Linear model, Generalised linear model, K-nearest neighbour, Neural network, and Random Forests models. The models were used to explore the responses acquired from the pipeline corrosion data. The performance of each of the ensembles are evaluated based on the Root Mean Square Error (RMSE), Adjusted R2 and Mean Absolute Error. Model evaluation show that although the averaging ensemble has a better fit (34.56%) than the stacking ensembles (17.22% and 16.98% respectively) considered, it also has the higher RMSE,(81.90) than the stacking models ( 40.91 and 38.53 respectively) forcing the decision maker to make a trade-off between fit and precision.</p>
</abstract>

The application of robotics continues to advance and lends itself to various disciplines. Previous work has shown how Markov decision processes can be integrated in an algorithm to handle pipeline inspection. This has been extended to building models and addressing the errors in the model from the data collected. This paper made use of data collected by simulated robots in a pipeline environment, to build an ensemble. Five models were explored and analysed viz: Linear model, Generalised linear model, K-nearest neighbour, Neural network, and Random Forests models. The models were used to explore the responses acquired from the pipeline corrosion data. The performance of each of the ensembles are evaluated based on the Root Mean Square Error (RMSE), Adjusted R2 and Mean Absolute Error. Model evaluation show that although the averaging ensemble has a better fit (34.56%) than the stacking ensembles (17.22% and 16.98% respectively) considered, it also has the higher RMSE,(81.90) than the stacking models ( 40.91 and 38.53 respectively) forcing the decision maker to make a trade-off between fit and precision.

Ensemble Algorithm for Simulated Corrosion Data-tentative

Annals of Science and Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Ensemble Algorithm for Simulated Corrosion Data-tentative"}

The application of robotics continues to advance and lends itself to various disciplines. Previous work has shown how Markov decision processes can be...

Ensemble Algorithm for Simulated Corrosion Data-tentative

Data publikacji: 06 sty 2024

Zakres stron: 56 - 62

Otrzymano: 19 maj 2023

Przyjęty: 18 wrz 2023

DOI: https://doi.org/10.2478/ast-2023-0015

Słowa kluczowe
Linear model, Generalised linear model, K-nearest neighbour, Neural network, Random Forests

© 2023 Chika O. Yinka-Banjo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Ensemble Algorithm for Simulated Corrosion Data-tentative

Data publikacji: 06 sty 2024

Zakres stron: 56 - 62

Otrzymano: 19 maj 2023

Przyjęty: 18 wrz 2023

DOI: https://doi.org/10.2478/ast-2023-0015

Słowa kluczoweLinear model, Generalised linear model, K-nearest neighbour, Neural network, Random Forests

© 2023 Chika O. Yinka-Banjo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Linear model, Generalised linear model, K-nearest neighbour, Neural network, Random Forests