Bibliometric Analysis of Studies on "Machine Learning in Geothermal Energy"

Year 2025, Volume: 11 Issue: 2, 57 - 68, 25.12.2025

Orkun Teke

Abstract

This study examines the scientific literature developing at the intersection of geothermal energy and machine learning from a bibliometric perspective. 300 academic publications published between 2010 and 2025, obtained from the Web of Science database, were analyzed using the R-based Biblioshiny tool. The study revealed the distribution of publications by year, citation performance, author, institution and country collaborations, the most cited studies and keyword co-occurrence networks. The findings show that there has been a significant acceleration in the field after 2019 and especially from 2022, with production reaching a high level in 2024–2025. While Geothermics was the journal with the most publications, multidisciplinary journals such as Renewable Energy, Energies, and Applied Energy also attracted attention. In the keyword analysis, technical themes such as Organic Rankin Cycle, Enhanced Geothermal System, reservoir, and temperature optimization were central; By 2024, new trends such as hydrogen and advanced geothermal systems have emerged. China leads by far in the number of publications and citations and maintains strong collaborations with the United States and Germany. The study comprehensively summarizes the status of the geothermal energy-machine learning field and provides a guiding framework for future research trends and areas of collaboration.

Keywords

Machine Learning , Geothermal Energy , Bibliometric Analysis , Biblioshiny , Web of Science

Ethical Statement

I declare that there is no ethical problem.

Supporting Institution

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Thanks

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"Jeotermal Enerjide Makine Öğrenmesi" Konulu Çalışmaların Bibliyometrik Analizi

Abstract

Bu çalışma, jeotermal enerji ve makine öğrenmesi kesişiminde gelişen bilimsel literatürü bibliyometrik bir bakış açısıyla incelemektedir. Web of Science veri tabanından elde edilen 2010-2025 yılları arasında yayınlanmış 300 akademik yayın, R tabanlı Biblioshiny aracı kullanılarak analiz edilmiştir. Çalışmada yayınların yıllara göre dağılımı, atıf performansı, yazar, kurum ve ülke iş birlikleri, en çok atıf alan çalışmalar ve anahtar kelime eş-geçiş ağları ortaya konulmuştur. Bulgular, 2019'dan sonra ve özellikle 2022'den itibaren alanda önemli bir ivmelenme olduğunu, üretimin 2024-2025'te yüksek bir seviyeye ulaştığını göstermektedir. En çok yayını olan dergi Geothermics olmakla birlikte, Renewable Energy, Energies ve Applied Energy gibi multidisipliner dergiler de dikkat çekmiştir. Anahtar kelime analizinde Organik Rankin Döngüsü, Geliştirilmiş Jeotermal Sistem, rezervuar ve sıcaklık optimizasyonu gibi teknik temalar merkezi konumdadır; 2024 yılına gelindiğinde, hidrojen ve gelişmiş jeotermal sistemler gibi yeni trendler ortaya çıkmıştır. Çin, yayın ve atıf sayısında açık ara lider konumda olup, Amerika Birleşik Devletleri ve Almanya ile güçlü iş birliklerini sürdürmektedir. Çalışma, jeotermal enerji-makine öğrenmesi alanının durumunu kapsamlı bir şekilde özetlemekte ve gelecekteki araştırma trendleri ve iş birliği alanları için yol gösterici bir çerçeve sunmaktadır.

Keywords

Makine Öğrenmesi , Jeotermal Enerji , Bibliyometrik Analiz , Biblioshiny , Web of Science

Ethical Statement

Herhangi bir etik problem olmadığını beyan ederim

Supporting Institution

-

Thanks

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