Volume 15, Issue 4 (3-2023)
ijhe 2023, 15(4): 769-782 |
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Bagheri S, Nejadkoorki F, Afshani S A, Mousavi V. Performance of adaptive neural-fuzzy inference system in predicting household waste production in Tabriz, Iran. ijhe 2023; 15 (4) :769-782
URL: http://ijhe.tums.ac.ir/article-1-6694-en.html
URL: http://ijhe.tums.ac.ir/article-1-6694-en.html
1- Department of Environmental Science, School of Natural Resources and Desert Studies, Yazd University, Yazd, Iran
2- Department of Environmental Science, School of Natural Resources and Desert Studies, Yazd University, Yazd, Iran ,f.nejadkoorki@gmail.com
3- Department of Cooperation and Social Welfare, Faculty of Social Sciences, Yazd University, Yazd, Iran
4- Department of Watershed Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modarres University, Noor, Iran
2- Department of Environmental Science, School of Natural Resources and Desert Studies, Yazd University, Yazd, Iran ,
3- Department of Cooperation and Social Welfare, Faculty of Social Sciences, Yazd University, Yazd, Iran
4- Department of Watershed Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modarres University, Noor, Iran
Abstract: (675 Views)
Background and Objective: One of the important environmental problems is the mass production of urban waste, which has increased per capita household waste production with the ever-increasing population growth; Therefore, nowadays, the use of intelligent systems has been expanded as a new solution in the analysis of environmental issues. Estimation of household waste through modeling, including the use of the fuzzy-neural network, leads to its better management. Therefore, the current research was conducted to investigate the socioeconomic factors on household waste production using the Adaptive Neural Fuzzy Inference System (ANFIS) in Tabriz city.
Materials and Method: In this research, by using the adaptive neuro-fuzzy inference system (AFNIS) with the Fuzzy C-Means (FCM) method, domestic waste generation in Tabriz city has been predicted. According to the nature of the subject and the investigated indicators, the information collected in descriptive research was collected from the students of schools in Tabriz using a questionnaire. Also, socio-economic factors were statistically analyzed using SPSS version 26 software, and parameters affecting domestic waste production in Tabriz city were used for modeling in MATLAB software.
Results: The results of the study showed that the adaptive neuro-fuzzy inference system with the Fuzzy C-Means method has acceptable performance for domestic waste production in Tabriz city.
Conclusion: According to the results obtained based on the statistical index, the forecasted model in domestic waste production in the Fuzzy C-Means method with the highest R (0.75) and the lowest error has an acceptable performance model in predicting the production of dry domestic waste in the studied area.
Materials and Method: In this research, by using the adaptive neuro-fuzzy inference system (AFNIS) with the Fuzzy C-Means (FCM) method, domestic waste generation in Tabriz city has been predicted. According to the nature of the subject and the investigated indicators, the information collected in descriptive research was collected from the students of schools in Tabriz using a questionnaire. Also, socio-economic factors were statistically analyzed using SPSS version 26 software, and parameters affecting domestic waste production in Tabriz city were used for modeling in MATLAB software.
Results: The results of the study showed that the adaptive neuro-fuzzy inference system with the Fuzzy C-Means method has acceptable performance for domestic waste production in Tabriz city.
Conclusion: According to the results obtained based on the statistical index, the forecasted model in domestic waste production in the Fuzzy C-Means method with the highest R (0.75) and the lowest error has an acceptable performance model in predicting the production of dry domestic waste in the studied area.
Type of Study: Research |
Subject:
General
Received: 2022/08/2 | Accepted: 2023/03/12 | Published: 2023/03/15
Received: 2022/08/2 | Accepted: 2023/03/12 | Published: 2023/03/15
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