用AI预测阿曼绿氢产率与选址，精准识别关键影响因素。
Artificial Intelligence for Green Hydrogen Yield Prediction and Site Suitability using SHAP-Based Composite Index: Focus on Oman
- 基于SHAP值构建多变量融合的选址评估框架。
- 模型准确率达98%，水体距离、海拔和季节变化影响最显著。
- 适合缺乏实测数据的国家进行绿氢规划决策。
随着各国寻求化石燃料的可持续替代方案，绿氢成为减碳战略的重要路径，尤其在太阳能丰富的干旱地区。然而，确定最优制氢地点需综合复杂的环境、气象和基础设施因素，且常面临直接氢产量数据稀缺的问题。本研究提出一种新型人工智能框架，利用均方绝对SHAP（SHapley Additive exPlanations）值计算绿氢产量与选址适宜性指数。该框架包含无监督多变量聚类、有监督机器学习分类器及SHAP算法的多阶段流程，基于集成气象、地形与时间数据训练。结果揭示了显著的空间适宜性分布模式及各变量的相对影响程度。模型预测准确率达98%，结果显示水体距离、海拔和季节变化是阿曼绿氢选址最关键的三个因素，其均方绝对SHAP值分别为2.470891、2.376296和1.273216。鉴于许多具备绿氢潜力国家缺乏或无地面实测产量数据，本研究提供了一种客观、可复现的替代方案，摆脱主观专家赋权，让数据本身发声，并可能发现未预设的潜在分组。该研究为产业界与政策制定者提供了可复制、可扩展的绿氢基础设施规划工具，适用于数据稀缺区域的决策支持。
原文摘要 · Abstract (English)
As nations seek sustainable alternatives to fossil fuels, green hydrogen has emerged as a promising strategic pathway toward decarbonisation, particularly in solar-rich arid regions. However, identifying optimal locations for hydrogen production requires the integration of complex environmental, atmospheric, and infrastructural factors, often compounded by limited availability of direct hydrogen yield data. This study presents a novel Artificial Intelligence (AI) framework for computing green hydrogen yield and site suitability index using mean absolute SHAP (SHapley Additive exPlanations) values. This framework consists of a multi-stage pipeline of unsupervised multi-variable clustering, supervised machine learning classifier and SHAP algorithm. The pipeline trains on an integrated meteorological, topographic and temporal dataset and the results revealed distinct spatial patterns of suitability and relative influence of the variables. With model predictive accuracy of 98%, the result also showed that water proximity, elevation and seasonal variation are the most influential factors determining green hydrogen site suitability in Oman with mean absolute shap values of 2.470891, 2.376296 and 1.273216 respectively. Given limited or absence of ground-truth yield data in many countries that have green hydrogen prospects and ambitions, this study offers an objective and reproducible alternative to subjective expert weightings, thus allowing the data to speak for itself and potentially discover novel latent groupings without pre-imposed assumptions. This study offers industry stakeholders and policymakers a replicable and scalable tool for green hydrogen infrastructure planning and other decision making in data-scarce regions.
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