## 2 Literature Review  
In recent years, studies have been conducted using artificial intelligence to make predictions based on Ecological Footprint data. [PERSON] et al. [6] performed EF prediction for Beijing by utilizing a dataset comprising 10 different input variables from 1996 to 2015, including population, total foreign trade, and total energy consumption, with EF as the output variable. Tested the prediction performances of the BPNN and SVM models for 2014 and 2015. According to the results of the relative error rates, it has been reported that the SVM model made more successful predictions compared to the BPNN model. Consequently, the SVM model was employed for EF prediction for the years 2016-2020. [PERSON] et al. [7] calculated the EF value for the years 1999-2018 and simulated the EF value with ARIMA and Grey Model (GM). The models were evaluated according to the fitting performance criterion. Since the \(p\) value of the GM (1,1) model is lower in the fitting performance evaluation, the EF values for 2019 and 2024 are estimated with the GM (1,1) model. [PERSON] et al. [8] used ARIMA and the hybrid ARIMA-ANN models for estimating Ecological Footprint and ecological capacity in China. The Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) metrics were used in comparing the model performances. The forecasting results of the study demonstrated that the hybrid ARIMA-ANN performed better than other models. [PERSON] [9] predicted the per capita ecological carrying capacity of Shenzhen, China using the ARIMA-LSTM hybrid method. Data from the years 2013 to 2018 were utilized in the study. Different learning rate values for the model were experimented with, and the optimal parameter (Learning rate 0.0005) was chosen for the best MAE and RMSE. [PERSON] et al. [10] utilized the ARIMA model for the estimation of the Ecological Footprint (EF) in Henan Province, China. In the study, EF and Ecological Carrying Capacity (EC) were initially calculated for the years 1949-2006. The calculated values were then used to evaluate the forecasting performance of the ARIMA model. The success of the ARIMA model was assessed based on fitting performance and was not compared with the success of a different method. [PERSON] et al. [11] calculated the Ecological Footprint of the top 10 countries with the best tourism destinations. Multiple regression and artificial neural network models were employed for this purpose. The study utilized information on natural resources, international tourists, economic growth, human capital, and Ecological Footprint between 1995 and 2019. Findings from the study indicated a significant positive correlation between economic growth and Ecological Footprint, with the ANN model demonstrating greater success. Therefore, the ANN model was reported to be more suitable for Ecological Footprint estimation in these countries. [PERSON] et al. [12] aimed to predict total EF consumption using information on population, oil, gas, coal, solar, other renewables, wind, nuclear, and hydro. For this purpose, K-Nearest Neighbors Regression (KNNReg), Random Forest Regression (RFR), Artificial Neural Network (ANN) with Rectified Linear Unit (ReLU), and ANN with Scaled Parametric Orthogonal Code Update (SPOCU) methods were employed. The performance of these methods was measured using MASE, NRMSE, MAPE, and SMAPE. The results indicated that the KNNReg model outperformed the others. Additionally, in the study, EF predictions are made using a GUI developed with the model that exhibited the best performance. [PERSON] et al. [13] conducted the prediction of Biocapacity and Ecological Footprint using a deep neural network. Different parameters of the DNN model were tested, and the performance of the models was measured using MAE and MSE metrics. With the DNN model exhibiting the lowest error, the footprint estimates for fishing grounds, grazing lands, forest lands, cropland, and built-up land worldwide for the years 2018-2030 were made.