نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مدیریت صنعتی، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران
2 گروه مهندسی عمران، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران
کلیدواژهها
عنوان مقاله English
نویسندگان English
This paper aims to present a model for predicting carbon emissions by considering the role of suppliers, which can significantly contribute to reducing carbon dioxide in the region. To determine the influential variables, literature review and expert opinions (20 experts) were used through the fuzzy Delphi method, ultimately selecting 8 influential variables. For modeling, the Light Gradient Boosting (LGB) algorithm was chosen due to its ability to capture nonlinear dependencies, and the Jellyfish Search Optimizer (JSO) was employed for precise hyperparameter tuning. The research innovation lies in two areas: the integration of LGB and JSO to increase prediction accuracy, and simultaneously considering the role of suppliers as influential variables in carbon emission prediction. The models were implemented using 5,997 data points from Tehran's chemical industries (Khavaran region) during 2022-2025, utilizing Python programming language. Results showed that the hybrid LGB_JSO model, with R² values of 0.9918, 0.9538, and 0.9606 in training, validation, and testing phases respectively, performed better than other models. Temperature, pressure, and storage time were identified as the most important influential parameters.
کلیدواژهها English
