Page 61 - Çevre Şehir İklim İngilizce - Sayı 4

Hacı Abdullah Uçan - Tayfun Dede - Sinan Nacar

and population were obtained from the relevant institutions and organizations
in quarterly periods from 2002 to 2020 in order to create forecast models for
the urban transformation process in Türkiye. These data used is available in
the study of Uçan, 2022. The prediction models of the urban transformation
process were examined with regression-based and ANN-based modeling. In
regression analyses, KRA: Classical Regression Analysis, MARS: Multivariate
Adaptive Regression Splines (Friedman, 1991), TreeNet Gradient Boosting
Machine (Friedman, 2001) were used, and in the ANN model, GWO: Gray
wolf optimization algorithm (Mirjalili et al. 2014) was coded in the MATLAB
programming language and added to the ANN.
While starting the studies on estimating the dependent variables of the
number of flats, number of licenses and surface area, the independent
variables that may have major effect on these variables were determined.
At this stage all possible regression analyses were performed to determine
which one of these variables are more effective and it was identified that the
model using all variables has the highest performance. In this context, gross
national product (GNP), inflation rate (%), interest rate (%) and population
were determined as independent variables. In addition to these variables, the
number of periods in each year was included in the independent variables as
another independent variable. These independent variables were used for
all three dependent variables without modification. Modeling studies were
started by using three different dependent variables against five independent
variables in total. In order to test the accuracy of the models established in
the modeling studies, the data sets are divided into training, verification and
test data sets. In this way, the accuracy of the models established with the
training and verification data sets could be evaluated using the test data.
The performances of the models, the root mean squared error (RMSE), mean
absolute error (MAE), and Nash-Sutcliffe (NS) performance statistics were
calculated using the following equations.
RMSE= (1)

MAE= (2)

NS= (3)

In the given equations; t indicates the measurement values, t di shows the
i
forecast values, the average of the measurement values and the number of n
data. The performance of the smallest value among RMSE and MAE values is
evaluated as high, while the NS value is between −∞ and 1. NS=1 indicates
that the method used is excellent.

50 The Journal of Environment, Urban and Climate