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.
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