Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2022Scopus SJRQ1/r/munzur/2484
  DEEP LEARNING BASED A COMPREHENSIVE ANALYSIS FOR WASTE PREDICTION

  Utku, Anıl; Kaya, Sema Kayapinar

  In its simplest definition, waste can be defined as any substance that is used, not needed and causes harm to the environment. Waste management covers control activities such as prevention of the formation of waste, reuse, separation according to its characteristics and type, storage, transportation, recycling and disposal. The main purpose of waste management is to leave a livable world to future generations, to create a sustainable environment, to protect natural resources, to save energy and costs, to reduce the rate of pollution and the amount of hazardous waste. In today's world where urbanization and industrialization rates are increasing, waste management is gaining importance. The aim of this study is to utilize waste data from Istanbul, Turkey's largest and fastest growing city, to estimate waste amount using a constructed Long Short-Term Memory (LSTM) based deep learning model. The developed LSTM-based model has been compared in practice with k-Nearest Neighbors (kNN), random forest (RF), Support Vector Machine (SVM), multi-layered perceptron (MLP) and Gated Recurrent Unit (GRU). As a result of the comparative and comprehensive analyzes, the experimental results showed that the developed LSTM-based deep learning method is more successful in the waste prediction problem than the other compared models.

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  AtıfScopus13·OpenAlex13

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• MakaleAçık Erişim2020/r/munzur/3540
  Evaluation of the effect of COVID-19 on countries' sustainable development level: A comparative MCDM framework

  Kaya, Sema Kayapinar

  The extent of the outbreak of coronavirus disease (COVID-19) had a major impact on health, social life, economic and environmental activities in almost every country over the world. It has disrupted the sustainable development of countries and brought many uncertainties for their future capabilities. In this study, the effects of the COVID-19 on OECD countries' sustainable development were investigated, and the sustainable development performance of the countries was evaluated by the MultiAttributive Ideal-Real Comparative Analysis (MAIRCA) method. Data for the second quarter of 2020 and the same quarter of the previous year is considered. Then, the results obtained by the MAIRCA method were compared with two different multi-criteria decision-making (MCDM) methods called MABAC (Multi‐Attributive Border Approximation Area Comparison) and WASPAS (Weighted Aggregated Sum Product Assessment). The effectiveness and validity of the results obtained from these methods were tested with Spearman's correlation coefficient. Finally, to examine the effect of COVID-19 on the indicators of sustainable development, a non-parametric Wilcoxon signed-rank test was applied. As a result, it was concluded that COVID-19 negatively affected the sustainable development of countries. However, sustainable development performances of developed countries have been observed to be better than developing countries.

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  AtıfScopus49·OpenAlex56

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