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Investigation of factors affecting rural drinking water consumption using intelligent hybrid models

Water Science and Engineering 2023年第2期16卷 175-183页

作者： Alireza Mehrabani Bashar hamed nozari Safar Marofi Mohamad Mohamadi Ahad Ahadiiman Department of Water Science and Engineering Faculty of AgricultureBu-Ali Sina UniversityHamedan 65178-38695Iran University of Kurdistan Sanandaj 66177-15175Iran Sustainable Development Office of hamedan Water and Wastewater Company Hamedan 65159-14478Iran

Identifying the factors affecting drinking water consumption is essential to the rational management of water resources and effective environment protection. In this study, the effects of the factors on rural drinking water demand were studied using the adaptive neuro-fuzzy inference system (ANFIS) and hybrid models, such as the ANFIS-genetic algorithm (GA), ANFIS-particle swarm optimization (PSO), and support vector machine (SVM)-simulated annealing (SA). The rural areas of Hamadan Province in Iran were selected for the case study. Five drinking water consumption factors were selected for the assessment according to the literature, data availability, and the characteristics of the study area (such as precipitation, relative humidity, temperature, the number of subscribers, and water price). The results showed that the standard errors of ANFIS, ANFIS-GA, ANFIS-PSO, and SVM-SA were 0.669, 0.619, 0.705, and 0.578, respectively. Therefore, the hybrid model SVM-SA outperformed other models. The sensitivity analysis showed that of the parameters affecting drinking water consumption, the number of subscribers significantly affected the water consumption rate, while the average temperature was the least significant factor. Water price was a factor that could be easily controlled, but it was always one of the least effective parameters due to the low water fee.

关键词： ANFIS Water distribution network Simulated annealing algorithm Support vector machine Adaptive neuro-fuzzy inference system

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Neutrosophic Mathematical Programming for Optimization of Multi-Objective Sustainable Biomass Supply Chain Network Design

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Computer Modeling in Engineering & Sciences 2021年第11期129卷 927-951页

作者： Mohammad Fallah hamed nozari Department of Industrial Engineering Islamic Azad UniversityCentral Tehran BranchTehranIran

In this paper,a multi-objective sustainable biomass supply chain network under uncertainty is designed by neutrosophic programming *** this method,for each objective function of the problem,three functions of truth membership,non-determination and falsehood are *** programming method in this paper simultaneously seeks to optimize the total costs of the supply chain network,the amount of greenhouse gas emissions,the number of potential people hired and the time of product transfer along the supply chain *** achieve the stated objective functions,strategic decisions such as locating potential facilities and tactical decisions such as optimal product flow allocation and vehicle routing must be *** results of the implementation of neutrosophic programming method show the high efficiency of this method in achieving the optimal values of each objective ***,by examining the rate of uncertainty,it was observed that with increasing this rate,the total costs of supply chain network design,greenhouse gas emissions and product transfer times have increased,and in contrast,the potential employment rate of individuals has decreased.

关键词： Neutrosophic mathematical programming sustainable supply chain biomass supply chain neutrosophic set uncertainty

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Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

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Journal of Zhejiang University-Science C(Computers and Electronics) 2012年第6期13卷 403-412页

作者： Hasan ABBASI nozari hamed DEHGHAN BANADAKI Mohammad MOKHTARE Somayeh HEKMATI VAHED Department of Mechatronics Faculty of EngineeringIslamic Azad UniversityScience and Research BranchTehranIran

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.

关键词：非线性的系统鉴定周期性的本地人线性 neuro 模糊(RLLNF ) 联网本地线性模型树(LOLIMOT ) 神经网络(NN ) 工业弯屈过程

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Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

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Frontiers of Information Technology & Electronic Engineering 2012年第9期 701-701页

作者： Hasan Abbasi nozari hamed Dehghan Banadaki Mohammad Mokhtare Somayeh Hekmati Vahed

关键词： NF Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

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