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Li, Y., Zou, C., Berecibar, M., Nanini-Maury, E., Chan, J.C.-W., Van den Bossche, P., Van Mierlo, J. and Omar, N. (2018) Random Forest Regression for Online Capacity Estimation of Lithium-Ion Batteries. Applied Energy, 232, 197-210.
https://doi.org/10.1016/j.apenergy.2018.09.182

has been cited by the following article:

• TITLE: A Hybrid Spatial Dependence Model Based on Radial Basis Function Neural Networks (RBFNN) and Random Forest (RF)

  AUTHORS: Mamadou Hady Barry, Lawrence Nderu, Anthony Waititu Gichuhi

  KEYWORDS: Spatial Data, Spatial Dependence, Hybrid Model, Machine Learning Algorithms

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.11 No.3, July 27, 2023

  ABSTRACT: The majority of spatial data reveal some degree of spatial dependence. The term “spatial dependence” refers to the tendency for phenomena to be more similar when they occur close together than when they occur far apart in space. This property is ignored in machine learning (ML) for spatial domains of application. Most classical machine learning algorithms are generally inappropriate unless modified in some way to account for it. In this study, we proposed an approach that aimed to improve a ML model to detect the dependence without incorporating any spatial features in the learning process. To detect this dependence while also improving performance, a hybrid model was used based on two representative algorithms. In addition, cross-validation method was used to make the model stable. Furthermore, global moran’s I and local moran were used to capture the spatial dependence in the residuals. The results show that the HM has significant with a R2 of 99.91% performance compared to RBFNN and RF that have 74.22% and 82.26% as R2 respectively. With lower errors, the HM was able to achieve an average test error of 0.033% and a positive global moran’s of 0.12. We concluded that as the R2 value increases, the models become weaker in terms of capturing the dependence.