PMID- 36934187
OWN - NLM
STAT- MEDLINE
DCOM- 20230510
LR  - 20230510
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 30
IP  - 22
DP  - 2023 May
TI  - Optimization of biocementation responses by artificial neural network and random 
      forest in comparison to response surface methodology.
PG  - 61863-61887
LID - 10.1007/s11356-023-26362-1 [doi]
AB  - In this article, the optimization of the specific urease activity (SUA) and the 
      calcium carbonate (CaCO(3)) using microbially induced calcite precipitation 
      (MICP) was compared to optimization using three algorithms based on machine 
      learning: random forest regressor, artificial neural networks (ANNs), and 
      multivariate linear regression. This study applied the techniques in two existing 
      response surface method (RSM) experiments involving MICP technique. Random 
      forest-based models and artificial neural network-based models were submitted 
      through the optimization of hyperparameters via cross-validation technique and 
      grid search, to select the best-optimized model. For this study, the random 
      forest-based algorithm is aimed at having the best performance of 0.9381 and 
      0.9463 in comparison to the original r(2) of 0.9021 and 0.8530, respectively. 
      This study is aimed at exploring the capability of using machine learning-based 
      models in small datasets for the purpose of optimization of experimental 
      variables in MICP technique and the meaningfulness of the models by their 
      specificities in the small experimental datasets applied to experimental designs. 
      This study is aimed at exploring the capability of using machine learning-based 
      models in small datasets for experimental variable optimization in MICP 
      technique. The use of these techniques can create prerogatives to scale and 
      mitigate costs in future experiments associated to the field.
CI  - (c) 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Pacheco, Vinicius Luiz
AU  - Pacheco VL
AUID- ORCID: 0000-0003-4979-107X
AD  - Graduate Program in Civil and Environmental Engineering, University of Passo 
      Fundo (UPF), Campus I, Km 171, BR 285, Passo Fundo, Rio Grande Do Sul, CEP: 
      99001-970, Brazil. vinimanfroipacheco@gmail.com.
FAU - Bragagnolo, Lucimara
AU  - Bragagnolo L
AD  - Graduate Program in Civil and Environmental Engineering, University of Passo 
      Fundo (UPF), Campus I, Km 171, BR 285, Passo Fundo, Rio Grande Do Sul, CEP: 
      99001-970, Brazil.
FAU - Dalla Rosa, Francisco
AU  - Dalla Rosa F
AD  - Graduate Program in Civil and Environmental Engineering, University of Passo 
      Fundo (UPF), Campus I, Km 171, BR 285, Passo Fundo, Rio Grande Do Sul, CEP: 
      99001-970, Brazil.
FAU - Thome, Antonio
AU  - Thome A
AD  - Graduate Program in Civil and Environmental Engineering, University of Passo 
      Fundo (UPF), Campus I, Km 171, BR 285, Passo Fundo, Rio Grande Do Sul, CEP: 
      99001-970, Brazil.
LA  - eng
PT  - Journal Article
DEP - 20230318
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
RN  - H0G9379FGK (Calcium Carbonate)
SB  - IM
MH  - *Random Forest
MH  - *Neural Networks, Computer
MH  - Algorithms
MH  - Machine Learning
MH  - Calcium Carbonate
OTO - NOTNLM
OT  - Artificial neural networks
OT  - Cross-validation
OT  - MICP
OT  - Random forest
OT  - Response surface method
EDAT- 2023/03/20 06:00
MHDA- 2023/05/10 06:42
CRDT- 2023/03/19 00:19
PHST- 2022/02/11 00:00 [received]
PHST- 2023/03/05 00:00 [accepted]
PHST- 2023/05/10 06:42 [medline]
PHST- 2023/03/20 06:00 [pubmed]
PHST- 2023/03/19 00:19 [entrez]
AID - 10.1007/s11356-023-26362-1 [pii]
AID - 10.1007/s11356-023-26362-1 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2023 May;30(22):61863-61887. doi: 
      10.1007/s11356-023-26362-1. Epub 2023 Mar 18.