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Industrialization and urbanization are increasing with the effect of globalization worldwide. The waste management problems are rising with the rising population rate, industrialization, and economic developments in the cities, which turned into environmental problems that directly affect human health. This study aims to examine waste management performance in the districts located in the city of Istanbul. To ensure that the districts are clustered in terms of the similarities and differences base on waste management. On this occasion, the authorized unit managers of the districts in the same cluster will be able to establish similar management policies and make joint decisions regarding waste management. In addition, the division of districts into clusters according to the determining indicators can provide information about the locations of waste storage centers. Also, these clusters will form the basis for the optimization constraints required to design appropriate logistics networks.Waste management performance of 39 districts in Istanbul in 2019 was compared by taking into consideration domestic waste, medical waste, population, municipal budget, and mechanical sweeping area. The data were obtained from The Istanbul Metropolitan Municipality (IMM) and Turkey Statistical Institute (TURKSTAT). One of the nonhierarchical clustering methods, the K-means clustering method, was applied using IBM SPSS Modeler data mining software to determine the relations between 39 districts. As a result, the waste management performance of the districts was evaluated according to the statistical data, similarities and differences were revealed by using the determined indicators.

This chapter aims to propose prediction models to estimate Turkey's manufacturing sector's capacity utilization rate between 2008-2019 monthly basis using the adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA) and particle swarm optimization (PSO) via determined indicators. The model's accuracy will be tested using some of the performance evaluation criteria, namely mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2) values were used to compare the prediction ability. The coefficient of determination for GA-ANFIS, PSO-ANFIS, and ANFIS models are 0.9787, 0.9786, and 0.9679 in the training phase and 0.9591, 0.7677, and 0.7264 in the testing phase, respectively. The study results showed that the GA-ANFIS model showed better predictive ability with the least prediction error among other models. As a result, ANFIS, whose parameters are adjusted with GA, can predict the Turkish capacity utilization rate in the manufacturing industry with high accuracy.

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