INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS

Mehmet Umut Salur; Nermin Elmas

doi:10.17780/ksujes.1642586

TR EN

TOPLULUK ÖĞRENME MODELLERİ İLE ÇEVRESEL ATIK SINIFLANDIRMADA YENİLİKÇİ YAKLAŞIM

Abstract

Artan atık üretimi ve yetersiz atık yönetimi, küresel çevre sorunlarını daha da karmaşık hale gelmiştir. Doğal kaynakların sınırlılığı ve atıkların çevreye verdiği zararlar atık yönetim sistemlerinin iyileştirilmesini zorunlu kılmaktadır. Atıkların doğru ve etkili sınıflandırılması hem ekonomik fayda sağlamakta hem de çevresel etkileri azaltmaktadır. Bu çalışmada, çevresel atıkların sınıflandırmak için derin öğrenme, makine öğrenmesi ve topluluk öğrenme knikleri birleştirilerek hibrit bir yaklaşım sunulmuştur. ResNet50, InceptionResNet-V2 ve DenseNet169 modelleri kullanılmış ve bu modeller önceden eğitilmiş ağırlıklar kullanılarak fine-tuning yapılmıştır. Her bir modelden elde edilen özellik haritaları birleştirilerek ensemble bir model oluşturulmuştur. Ensemble deep learning modeli tarafından çıkarılan öznitelikler arasından ANOVA, Variance Threshold, Mutual Information, Random Forests, Lasso, RFE, PCA ve Ridge Regresyon özellik seçim yöntemleri ile en etkili öznitelikler belirlenmiştir. Seçilen öznitelikler SVM, MLP ve Random Forest, XGBoost, çoğunluk oylama ve yumuşak oylama yöntemleriyle sınıflandırılmıştır. Bu çalışma çevresel atık sınıflandırmak için hem bireysel modellerin hem de ensemble modellerin sağladığı katkıları ortaya koymaktadır. Önerilen yöntemin etkinliği iki farklı veri kümesinde test edilmiş ve etkinliği doğrulanmıştır. Elde edilen sonuçlar, önerilen yöntemin atık yönetimi ve geri dönüşüm süreçlerine anlamlı bir katkı sağlayabileceğini göstermiştir.

Keywords

INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS

Abstract

Increasing waste production and inadequate waste management have further complicated global environmental problems. The limited natural resources and the damage caused by waste to the environment necessitate the improvement of waste management systems. Accurate and effective classification of waste provides both economic benefits and reduces environmental impacts. In this study, a hybrid approach is presented by combining deep learning, machine learning, and ensemble learning techniques to classify environmental waste. ResNet50, InceptionResNet-V2, and DenseNet169 models were used, and these models were fine-tuned using pre-trained weights. We created an ensemble model by combining the feature maps obtained from each model. Among the features extracted by the ensemble deep learning model, the most effective features were determined with ANOVA, Variance Threshold, Mutual Information, Random Forests, Lasso, RFE, PCA, and Ridge Regression feature selection methods. The selected features were classified with SVM, MLP and Random Forest, XGBoost, hard voting, and soft voting methods. This study presents the contributions of both individual and ensemble models for environmental waste classification. The effectiveness of the proposed method was tested on two different datasets, and its effectiveness was verified. The results show that the proposed method can make a significant contribution to waste management and recycling processes.

Keywords

Project Number

2024-GAP-Mühe-0008

Thanks

This study was supported by Gaziantep Islamic Science and Technology University Scientific Research Projects Coordination Unit under Project No: 2024-GAP-Mühe-0008.

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Details

Primary Language

English

Subjects

Image Processing, Deep Learning, Waste Management, Reduction, Reuse and Recycling

Journal Section

Research Article

Authors

Mehmet Umut Salur ^*
0000-0003-0296-6266
Türkiye

Nermin Elmas
0009-0004-5617-6344
Türkiye

Publication Date

June 3, 2025

Submission Date

February 18, 2025

Acceptance Date

April 21, 2025

Published in Issue

Year 2025 Volume: 28 Number: 2

DOI

https://doi.org/10.17780/ksujes.1642586

IZ

https://izlik.org/JA47XT55RR

Cite

RIS / Bibtex

APA

Salur, M. U., & Elmas, N. (2025). INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 933-956. https://doi.org/10.17780/ksujes.1642586

AMA

1.Salur MU, Elmas N. INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS. KSU J. Eng. Sci. 2025;28(2):933-956. doi:10.17780/ksujes.1642586

Chicago

Salur, Mehmet Umut, and Nermin Elmas. 2025. “INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 28 (2): 933-56. https://doi.org/10.17780/ksujes.1642586.

EndNote

Salur MU, Elmas N (June 1, 2025) INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 28 2 933–956.

IEEE

[1]M. U. Salur and N. Elmas, “INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS”, KSU J. Eng. Sci., vol. 28, no. 2, pp. 933–956, June 2025, doi: 10.17780/ksujes.1642586.

ISNAD

Salur, Mehmet Umut - Elmas, Nermin. “INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 28/2 (June 1, 2025): 933-956. https://doi.org/10.17780/ksujes.1642586.

JAMA

1.Salur MU, Elmas N. INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS. KSU J. Eng. Sci. 2025;28:933–956.

MLA

Salur, Mehmet Umut, and Nermin Elmas. “INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, vol. 28, no. 2, June 2025, pp. 933-56, doi:10.17780/ksujes.1642586.

Vancouver

1.Mehmet Umut Salur, Nermin Elmas. INNOVATIVE APPROACH TO ENVIRONMENTAL WASTE CLASSIFICATION WITH ENSEMBLE LEARNING MODELS. KSU J. Eng. Sci. 2025 Jun. 1;28(2):933-56. doi:10.17780/ksujes.1642586