[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-41824-en":3,"doc-seo-41824-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},41824,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Prioritising Geothermal Resilience Strategies in Hazard-Prone Regions","Climate change intensifies extreme events, making energy resilience a core planning objective for climate-sensitive regions. Geothermal energy can bolster resilience through stable, dispatchable electricity and heat, yet deployment is limited by geological uncertainty, induced seismicity risks, groundwater protection requirements, and variable local acceptance. Managing these constraints requires combining multi-source geoscientific evidence that is hard to integrate with simple tools. This study proposes a hybrid AI–MCDA framework linking geospatial ML with fuzzy AHP and fuzzy VIKOR for uncertainty-aware, map-based ranking.","Geological Journal  \nRESEARCH ARTICLE  \nResources Sustainability and Energy Resilience  \nPrioritising Geothermal Resilience Strategies in Hazard-Prone Regions Using Geospatial Machine Learning and Fuzzy AHP–VIKOR  \nRaza Ali Tunio   \nSchool of Management, Xi'an University of Finance and Economics, Xi'an, Shaanxi, China  \nCorrespondence: Raza Ali Tunio ([razatunio@hotmail.com](razatunio@hotmail.com))  \nReceived: 25 February 2026 | Revised: 24 April 2026 | Accepted: 11 May 2026  \nHanding Editor: Syed Ahsan Ali Shah  \nKeywords: AI–MCDA | energy resilience | fuzzy AHP | fuzzy VIKOR | geohazards | geothermal energy | machine learning  \nABSTRACT  \nClimate change is increasing extreme events and making energy resilience a key planning goal in climate-sensitive regions. Geothermal energy can support resilience because it provides stable and dispatchable power and heat. However, geothermal deployment is often constrained by geological uncertainty, induced seismicity concerns, groundwater protection needs and local acceptance. These constraints are difficult to manage because geothermal decisions depend on multi-source geoscientific evidence, including geophysical, geochemical and geospatial datasets, which are not easy to combine using simple decision tools. This study develops a hybrid AI–MCDA framework that links geospatial machine learning (ML) with fuzzy multi-criteria analysis. ML is used to convert heterogeneous datasets into comparable, map-based indicators, such as geothermal potential signals, landslide susceptibility and hydro-environmental sensitivity indices. These indicators support fuzzy AHP to derive criteria and sub-criteria weights from expert judgements under uncertainty. The study then applies fuzzy VIKOR to rank geothermal resilience strategies through a compromise solution based on overall performance and worst-case regret. The results show that strategies focused on monitoring, evaluation and traffic-light risk governance rank highest, followed by data integration and information-sharing measures. Overall, the framework strengthens transparency, reduces decision uncertainty and supports practical strategy selection for geothermal development aligned with resource sustainability and energy resilience goals.  \n1 | Introduction  \nResource sustainability and energy resilience are now central concerns for climate-sensitive regions. Energy systems face combined stress from extreme events, shifting demand patterns and infrastructure fragility. Recent research shows that climate variability can weaken the planned match between supply and demand for wind and solar systems, creating new operational risks even under long-term decarbonisation goals (Liu et al. 2023) . In parallel, climate extremes can trigger cascading failures and large-scale outages when renewables expand without adequate flexibility and protection measures (Xu  \net al. 2025) . These findings indicate that resilience is not only a technical issue, but also a planning problem that requires robust choices under uncertainty. Accordingly, resilience-oriented energy planning requires integrated decision frameworks that can synthesise spatial risk evidence, expert judgement and transparent ranking procedures within a single assessment process.  \nGeothermal energy has strong potential to support resilience because it can deliver stable electricity and heat (Genetu 2025) . Unlike variable renewables, geothermal output is less sensitive to daily weather conditions and can support grid stability. However, geothermal expansion is not straightforward. Many  \n© 2026 John Wiley & Sons Ltd.  \nGeological Journal, 2026; 0:1–21 1  \n[https://doi.org/10.1002/gj.70345](https://doi.org/10.1002/gj.70345)  \npromising geothermal zones are also geologically complex, and the same geological settings that provide heat and permeability can also increase hazard exposure (Xie et al. 2026; Dalsania and Sircar 2025) . One widely discussed challenge is induced seismicity. Operational experie","cbCaitO5UpKEd5nA","https://ap.wps.com/l/cbCaitO5UpKEd5nA","pdf",2196047,3,1,21,"English","en",105,"# Introduction\n## Energy resilience in climate-sensitive regions\n## Geothermal energy as a resilience resource\n## Key constraints: geohazards, environment, and acceptance\n## Need for integrated decision frameworks with uncertainty handling","[{\"question\":\"Why is energy resilience becoming a central planning goal in climate-sensitive regions?\",\"answer\":\"Climate change increases extreme events and shifts operational conditions, exposing energy systems to cascading failures, infrastructure fragility, and supply–demand mismatches.\"},{\"question\":\"What constraints make geothermal deployment difficult in hazard-prone areas?\",\"answer\":\"Geological uncertainty, induced seismicity concerns, groundwater protection needs, and local social acceptance constraints complicate reliable decision-making.\"},{\"question\":\"How does the study’s hybrid AI–MCDA framework rank geothermal resilience strategies?\",\"answer\":\"Geospatial machine learning converts heterogeneous datasets into comparable map-based indicators, fuzzy AHP derives weighted criteria under uncertainty from expert judgements, and fuzzy VIKOR ranks strategies using a compromise solution based on overall performance and worst-case regret.\"}]",1783334264,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"prioritising-geothermal-resilience-strategies-in-hazard-prone-regions","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/prioritising-geothermal-resilience-strategies-in-hazard-prone-regions/41824/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16","2026-07-06",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is energy resilience becoming a central planning goal in climate-sensitive regions?","Question",{"text":75,"@type":76},"Climate change increases extreme events and shifts operational conditions, exposing energy systems to cascading failures, infrastructure fragility, and supply–demand mismatches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What constraints make geothermal deployment difficult in hazard-prone areas?",{"text":80,"@type":76},"Geological uncertainty, induced seismicity concerns, groundwater protection needs, and local social acceptance constraints complicate reliable decision-making.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study’s hybrid AI–MCDA framework rank geothermal resilience strategies?",{"text":84,"@type":76},"Geospatial machine learning converts heterogeneous datasets into comparable map-based indicators, fuzzy AHP derives weighted criteria under uncertainty from expert judgements, and fuzzy VIKOR ranks strategies using a compromise 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