SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Alexandre Pardelinha(alexandre.pardelinha@aluno.cefet-rj.br)
2 Marcos Ceddia(marcosceddia@gmail.com)
3 Roberto Pontes(robertosgpontes@gmail.com)
4 Kele Belloze(kele.belloze@cefet-rj.br)
5 Carolina Aguilar(carolina.aguilar@cefet-rj.br)
6 Laura Assis(laura.assis@cefet-rj.br)
7 Diego Brandão(diego.brandao@cefet-rj.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Al-Qinna, M. and Jaber, S. (2013). Predicting soil bulk density using advanced pedotransfer functions in an arid environment. Transactions of the ASABE, 56(3):963–976.
2 Ceddia, M. B. et al. (2015). Spatial variability of soil carbon stock in the Urucu river basin, central Amazon-Brazil. Science of the Total Environment.
3 Ceddia, M. B. et al. (2016). The use of pedotransfer functions and the estimation of carbon stock in the central Amazon region. Scientia Agricola.
4 Ferreira, A. C. S. et al. (2023). Predicting soil carbon stock in remote areas of the central Amazon region using machine learning techniques. Geoderma Regional - Elsevier.
5 Gomes, L. C. et al. (2019). Modelling and mapping soil organic carbon stocks in brazil. Geoderma, 340:337–350.
6 Haddad, D. B. et al. (2017). A first approach using neural network to estimating soil bulk density of Urucu basin in central Amazon-Brazil. IEEE - Institute of Electrical and Electronic Engineers.
7 Haddad, D. B. et al. (2018). Brazilian soil bulk density prediction based on a committee of neural regressors. IEEE - Institute of Electrical and Electronic Engineers.
8 Japa, L. et al. (2023). A population-based hybrid approach for hyperparameter optimization of neural networks. IEEE Access, 11:50752–50768.
9 Kumar, S. et al. (2023). Potential impact of data-centric ai on society. IEEE Technology and Society Magazine, 42(3):98–107.
10 Mahmoudzadeh, H. et al. (2020). Spatial prediction of soil organic carbon using machine learning techniques in western Iran. Geoderma Regional - Elsevier.
11 Mousavi, S. R. et al. (2022). Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran. Journal of the International Measurement Confederation - Elsevier.
12 Silatsa, F. B. et al. (2020). Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon. Geoderma - Elsevier.
13 Song, J. et al. (2022). Estimation of soil organic carbon content in coastal wetlands with measured vis-nir spectroscopy using optimized support vector machines and random forests. Remote Sensing - MDPI.
14 Szatmari, G. et al. (2023). Countrywide mapping and assessment of organic carbon saturation in the topsoil using machine learning-based pedotransfer function with uncertainty propagation. Catena - Elsevier.
15 Tranter, G. et al. (2007). Building and testing conceptual and empirical models for predicting soil bulk density. Soil Use and Management, 23(4):437–443.
16 Wadoux, A. M.-C. et al. (2020). Machine learning for digital soil mapping: applications, challenges and suggested solutions. Earth-Science Reviews - Elsevier.
17 Wadoux, A. M.-C. et al. (2023). Shapley values reveal the drivers of soil organic carbon stock prediction. Soil.
18 Ye, Z. et al. (2021). Using machine learning algorithms based on gf-6 and Google Earth engine to predict and map the spatial distribution of soil organic matter content. Sustainability - MDPI.