A decision-support framework for nuclear facility siting using machine learning and half quadratic programming: Insights from Türkiye

IF 3.2 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Progress in Nuclear Energy Pub Date : 2026-05-01 Epub Date: 2026-02-13 DOI:10.1016/j.pnucene.2026.106296

Ayhan Doğan

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引用次数: 0

Abstract

This study introduces an innovative methodological framework for nuclear power plant (NPP) site selection in Mersin Province, integrating the three best-performing Machine Learning (ML) algorithms from a set of seven, each offering distinct advantages, with Half Quadratic Programming (HQP) and Geographic Information Systems (GIS). The study achieved the most favorable outcomes with Categorical Boosting (CB), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). The integration of these algorithms, SVM (R² = 0.960), MLP (R² = 0.953), and CB (R² = 0.912), significantly enhanced the accuracy and objectivity in determining criterion weights, thereby reducing reliance on subjective expert opinions. HQP effectively harmonized the weights derived from different ML methods, optimizing critical criterion interactions such as seismic risk, environmental factors, and infrastructure efficiency. GIS enabled precise spatial analyses, identifying coastal areas around Anamur and Aydıncık as most suitable (score 5), and inland regions near Gülnar and Silifke as highly suitable (score 4). Notably, the existing location of the Akkuyu NPP precisely matches the area identified as most suitable, validating the robustness and reliability of the proposed approach. Overall, this research significantly advances nuclear site selection methodologies, offering a comprehensive, objective, and practical decision-making tool that substantially contributes to strategic energy planning.

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使用机器学习和半二次规划的核设施选址决策支持框架：来自 rkiye的见解

本研究介绍了梅尔辛省核电厂（NPP）选址的创新方法框架，将七种表现最佳的三种机器学习（ML）算法与半二次规划（HQP）和地理信息系统（GIS）相结合，每种算法都具有独特的优势。使用分类增强（CB）、支持向量机（SVM）和多层感知机（MLP）的研究结果最为理想。SVM （R2 = 0.960）、MLP （R2 = 0.953）和CB （R2 = 0.912）三种算法的融合显著提高了确定标准权重的准确性和客观性，从而减少了对主观专家意见的依赖。HQP有效地协调了来自不同ML方法的权重，优化了关键标准的相互作用，如地震风险、环境因素和基础设施效率。GIS能够进行精确的空间分析，确定阿纳穆尔和Aydıncık附近的沿海地区是最合适的（得分5），而g利纳尔和西里夫克附近的内陆地区是非常合适的（得分4）。值得注意的是，阿库尤核电站的现有位置与确定为最合适的区域精确匹配，验证了所提出方法的稳健性和可靠性。总的来说，这项研究显著地推进了核选址方法，提供了一个全面、客观和实用的决策工具，为战略能源规划做出了重大贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Progress in Nuclear Energy 工程技术-核科学技术

CiteScore

5.30

自引率

14.80%

发文量

331

审稿时长

3.5 months

期刊介绍： Progress in Nuclear Energy is an international review journal covering all aspects of nuclear science and engineering. In keeping with the maturity of nuclear power, articles on safety, siting and environmental problems are encouraged, as are those associated with economics and fuel management. However, basic physics and engineering will remain an important aspect of the editorial policy. Articles published are either of a review nature or present new material in more depth. They are aimed at researchers and technically-oriented managers working in the nuclear energy field. Please note the following: 1) PNE seeks high quality research papers which are medium to long in length. Short research papers should be submitted to the journal Annals in Nuclear Energy. 2) PNE reserves the right to reject papers which are based solely on routine application of computer codes used to produce reactor designs or explain existing reactor phenomena. Such papers, although worthy, are best left as laboratory reports whereas Progress in Nuclear Energy seeks papers of originality, which are archival in nature, in the fields of mathematical and experimental nuclear technology, including fission, fusion (blanket physics, radiation damage), safety, materials aspects, economics, etc. 3) Review papers, which may occasionally be invited, are particularly sought by the journal in these fields.