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Showing 2 results for Rahimian
A novel data driven and feature based forecasting framework for wastewater optimization of network pressure management system
Pegah Rahimian, Sahand Behnam,
Volume 31, Issue 3 (IJIEPR 2020)
Abstract
In this paper, a novel data driven approach for improving the performance of wastewater management and pumping system is proposed, which is getting knowledge from data mining methods as the input parameters of optimization problem to be solved in nonlinear programming environment. As the first step, we used CART classifier decision tree to classify the operation mode -number of active pumps- based on the historical data of the Austin-Texas infrastructure. Then SOM is applied for clustering customers and selecting the most important features that might have effect on consumption pattern. Furthermore, the extracted features will be fed to Levenberg-Marquardt (LM) neural network which will predict the required outflow rate of the period for each operation mode, classified by CART. The result show that F-measure of the prediction is 90%, 88%, 84% for each operation mode 1,2,3, respectively. Finally, the nonlinear optimization problem is developed based on the data and features extracted from previous steps, and it is solved by artificial immune algorithm. We have compared the result of the optimization model with observed data, and it shows that our model can save up to 2%-8% of outflow rate and wastewater, which is significant improvement in the performance of pumping system.
Selection of Strategic Value Chain using Hybrid Entropy-TOPSIS-Fuzzy Approach
Mohammad Reza Zare Banadkouki, Mohammad Hossein Abdorrahimian,
Volume 36, Issue 3 (IJIEPR 2025)
Abstract
Nowadays, it is very important to pay attention to the influential factors and economic and social drivers in developing countries, and identifying and evaluating the performance of Strategic value chains (SVCs) is in this direction. Evaluating local VCs and planning to upgrade them to the region and the world can be part of the development programs of each country. The correct selection of SVCs, considering the economic and competitive environment of the region, causes economic and social transformation. In this research, considering the development programs of Iran and especially Yazd province, the value-creating sectors of Yazd province were considered. After studying the upstream documents, the active VCs in the Yazd province were first extracted. Then, by reviewing the literature on the subject and asking for opinions from experts, the criteria for evaluating SVCs from the economic, social, and regional perspectives were determined. The Shannon entropy method was used to weight the evaluation criteria, and the fuzzy TOPSIS method was used to select the most effective VCs. From the literature review, documents, and field observations, 20 local VCs and 8 evaluation criteria were extracted and approved by experts. The study results revealed that the evaluation criteria of “High job creation” and “Positive effect on gross domestic product (GDP)” had the highest weights, and “Tourism and handicrafts”, “Textile and clothing”, “Commercial logistics and transportation”, and “Non-metallic minerals” had the highest impacts in Yazd Province, according to the evaluation criteria known as SVCs. Moreover, a sensitivity analysis was performed to determine the stability and robustness of the proposed approach by changing the weights of the desired criteria.

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