Akademik Veri Yönetim Sistemi
NEURAL COMPUTING AND APPLICATIONS, cilt.34, ss.14777-14791, 2022 (SCI-Expanded)
Geographically weighted regression (GWR) models varying relationships
and is a local spatial regression approach. It has been used in several
application domains, such as meteorology, environmental management,
ecology, etc. The datasets collected in these applications include
spatial (latitude, longitude), altitudinal, and temporal
nonstationarities and so the values of parameters that are found in such
datasets change over space and time. In the literature, several GWR
models have been proposed to handle such datasets. However, these
methods do not consider spatial, altitudinal, and temporal
nonstationarity in the datasets simultaneously. This study deals with
developing a GWR technique, 4D-GWR, to capture spatial, altitudinal, and
temporal nonstationarities to improve the prediction accuracy. In
addition, a new parameter estimation strategy, which utilizes
n-dimension golden section search algorithm, is proposed to estimate
parameters of 4D-GWR approach. Experimental evaluations were conducted
to compare the proposed 4D-GWR approach with the classical approaches of
GWR (2D-GWR), GAWR, GTWR, and GWANN using real-time and synthetic
meteorological datasets. The results showed that 4D-GWR approach
outperforms other approaches in terms of RMSE between the predicted and
actual air temperatures, runtime, and dataset size. Experiments showed
that GWANN could not handle more than 15,000 observation points, 2D-GWR,
GAWR, and GTWR could not handle more than 40,000 observation points
and, in contrast, the 4D-GWR could handle all dataset sizes,
successfully. When the actual and predicted air temperature values are
compared, the highest correlation of 0.95 was obtained by 4D-GWR and the
lowest correlation of 0.77 was obtained by GWANN.