Tarımsal Hidrojen Enerjisi ve Verimliliği Alanındaki Araştırma Eğilimlerinin Bibliyometrik ve Makine Öğrenimi Temelli Tahmini

Abstract

Bu çalışma, entegre bir bibliyometrik ve makine öğrenimi çerçevesi aracılığıyla tarımsal kaynaklardan hidrojen üretiminde enerji verimliliğini incelemektedir. 2000 ile 2023 yılları arasında yayınlanmış ve Web of Science’ta indekslenmiş 958 yayından oluşan bir veri kümesi, Bibliometrix kullanılarak analiz edildi; bu analizle yayın dinamikleri, atıf kalıpları, dergi yoğunlaşmaları, uluslararası işbirliği ve tematik gelişim belirlenmeye çalışıldı. Buna paralel olarak, MATLAB'da iki uzun kısa süreli bellek (LSTM) modeli geliştirilmiştir: biri 2021–2034 yılları için yıllık yayın sayılarını tahmin etmek, diğeri ise bibliyometrik metin verilerinden gelecekteki araştırma anahtar kelimelerini oluşturmak içindir. Sonuçlar, bilimsel çıktıda sürdürülebilir bir büyüme olduğunu ve 2015'ten sonra belirgin bir hızlanma olduğunu göstermektedir. En yüksek atıf hacmi Çin ve Amerika Birleşik Devletleri'ne aitken, International Journal of Hydrogen Energy ve Bioresource Technology en etkili yayınlar olarak öne çıkmaktadır. Tekrarlanan temalar arasında biyokütle dönüşümü, hidrojen üretim verimliliği, fotokataliz ve performans optimizasyonu yer almaktadır. Yayın tahmin modeli, 2028 yılına kadar makalelerde keskin bir artış öngörürken, ardından yıllık 500'ün üzerinde bir seviyede istikrarın sağlanacağını öngörmektedir; anahtar kelime modeli ise değerlendirme, gazlaştırma ve katalitik işlemeyi sürekli olarak yeni yönelimler olarak öne çıkarmaktadır. Genel olarak, çalışma bu alana ilişkin veriye dayalı bir harita sunmakta ve enerji-tarım arayüzünde gelecekteki araştırma önceliklerini öngörmek için tekrarlanabilir bir yaklaşım sağlamaktadır.

Keywords

• Bibliyometrik
• enerji verimliliği
• hidrojen enerjisi
• tarımsal biyokütle
• LSTM
• makine öğrenimi
• sürdürülebilirlik

Supporting Institution

NA

Project Number

NA

Ethical Statement

NA

Thanks

NA

Bibliometric and Machine Learning-Based Forecasting of Research Trends in Agricultural Hydrogen Energy and Efficiency

Abstract

This study examines energy efficiency in hydrogen production from agricultural resources through an integrated bibliometric and machine learning framework. A corpus of 958 Web of Science–indexed publications published between 2000 and 2024 was analyzed using Bibliometrix to characterize publication dynamics, citation patterns, journal concentrations, international collaboration, and thematic evolution. In parallel, two long short-term memory (LSTM) models were developed in MATLAB: one to forecast annual publication counts for 2021–2034 and another to generate prospective research keywords from bibliometric text data. The results indicate sustained growth in scientific output, with a marked acceleration after 2015. China and the United States account for the largest citation volumes, whereas International Journal of Hydrogen Energy and Bioresource Technology emerge as the most influential outlets. Recurrent themes include biomass conversion, hydrogen production efficiency, photocatalysis, and performance optimization. The publication-forecast model projects a steep increase in papers through 2028, followed by stabilization above 500 publications per year, while the keyword model consistently highlights evaluation, gasification, and catalytic processing as emerging directions. Overall, the study provides a data-driven map of the field and a replicable approach for anticipating future research priorities at the energy–agriculture interface.

Keywords

• Bibliometrics
• energy efficiency
• hydrogen energy
• agricultural biomass
• LSTM
• machine learning
• research forecasting
• sustainability

Supporting Institution

NA

Project Number

NA

Ethical Statement

NA

Thanks

NA

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