Green Hydrogen Hub Site Selection using a Hybrid GIS-MCDM and Genetic Algorithm Framework
ハイブリッドGIS-MCDMと遺伝的アルゴリズムを用いたグリーン水素ハブのサイト選定フレームワーク (AI 翻訳)
Dishant Balotra, Banalaxmi Brahma
🤖 gxceed AI 要約
日本語
インドのグジャラート州を対象に、GISと複数基準意思決定法（MCDM）に遺伝的アルゴリズムを組み合わせたハイブリッドフレームワークでグリーン水素ハブの最適立地を選定。従来の主観的手法のバイアスを低減し、再生可能エネルギー潜在力よりインフラ接続性が重要であることを示した。バドダラとアムレリが最適地区と判定され、産業エコシステムとの共立の重要性を強調。
English
This study proposes a hybrid GIS-MCDM and genetic algorithm framework for selecting optimal locations for green hydrogen hubs in Gujarat, India. The approach reduces subjective bias by using evolutionary optimization to balance statistical variance and inter-criteria conflict. Results show infrastructure connectivity is more critical than raw renewable potential, identifying Vadodara and Amreli as top districts, and emphasizing collocation with industrial ecosystems.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも水素ハブの立地計画が進んでおり、本論文のハイブリッドフレームワークは客観性を高める手法として参考になる。特に、再生可能エネルギー賦存量だけでなく既存インフラとの連携を重視する点は、日本の地域特性に応じた計画に応用可能。
In the global GX context
As countries globally develop hydrogen strategies, this paper offers a reproducible framework for site selection that minimizes manual bias and highlights the strategic importance of logistics and existing industrial clusters. The hybrid methodology can be adapted to other regions, contributing to efficient hydrogen infrastructure planning.
👥 読者別の含意
🔬研究者:Demonstrates a novel integration of genetic algorithms with GIS-MCDM for hydrogen hub site selection, offering a data-driven alternative to subjective weighting.
🏢実務担当者:Provides a step-by-step methodology that corporate planning teams can adapt to identify optimal locations for hydrogen production facilities based on multi-criteria analysis.
🏛政策担当者:Highlights the importance of prioritizing infrastructure connectivity over raw renewable potential, informing policy for hydrogen hub siting and industrial cluster development.
📄 Abstract（原文）
In order to achieve India’s net zero emission goal of 2070, the country will have to move towards a Green Hydrogen economy, which requires careful selection of Green Hydrogen Hubs, considering the availability of renewable resource as well as infrastructure preparedness. However, the reliability of traditional site selection is limited because it is based on subjective, expert-driven MCDM techniques. This study does Green Hydrogen Hub site selection in Gujarat using Hybrid GIS-MCDM and Genetic Algorithm Framework. Evolutionary optimization is applied to the objective criterion weights to mathematically balance the statistical variance and inter-criteria conflict in order to minimize manual bias. The framework produces a robust suitability map based on multidimensional geospatial layers covering supply, demand, infrastructure and constraints. Polynomial Stacking Regressor (R2 = 0.922), is used for the statistical verification of the map. According to the results, it is more important to focus on infrastructure connectivity rather than on potential raw renewable. The highest ranking Districts are Vadodara and Amreli. In order to reduce the logistical bottlenecks and provide a reproducible road map for the energy planning in the country, these findings underscore the strategic importance of collocating production hubs to existing industrial ecosystems.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/aiei69164.2026.11497186first seen 2026-05-15 19:51:33
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