استفاده از ترکیب رویکردهای سنجش از دور و یادگیری ماشین در پیش‌بینی پارامترهای هیدرولوژیکی: یک مطالعه علم‌سنجی

نوع مقاله : مروری

نویسندگان

• معین توسن ¹
• راضیه شمشیرگران ²
• مهدی دستورانی ¹

¹ گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند، بیرجند، ایران.

² گروه مهندسی عمران، دانشکده مهندسی، دانشگاه بیرجند، بیرجند، ایران.

چکیده

کلیدواژه‌ها

• ادغام داده‌ها
• تحلیل علم‌سنجی
• داده‌های چندمنبعی
• روندهای پژوهشی
• هیدرولوژی

موضوعات

• منابع آب

عنوان مقاله [English]

Application of the Combination of Remote Sensing and Machine Learning Approaches in Predicting Hydrological Parameters: A Bibliometric Analysis

نویسندگان [English]

• Moein Tosan ¹
• Raziyeh Shamshirgaran ²
• Mehdi Dastourani ¹

کلیدواژه‌ها [English]

• Data integration
• Scientometric analysis
• Multi-source data
• Research trends
• Hydrology

Introduction:

Hydrology, as a critical discipline in environmental sciences, faces significant challenges in monitoring and forecasting water resources under complex and changing climatic conditions. Traditional hydrological methods, which rely primarily on ground-based data and physical models, are often limited in terms of spatial coverage, temporal resolution, and computational efficiency. In recent years, Remote Sensing (RS) and Machine Learning (ML) have emerged as transformative tools to address these limitations. Remote sensing provides extensive, global coverage data, enabling the monitoring of hydrological variables across diverse landscapes and climates. However, the processing, interpretation, and integration of these vast datasets require advanced computational techniques. Machine learning plays a crucial role in efficiently processing these data, identifying patterns, making predictions, and optimizing hydrological models. The integration of RS and ML offers substantial potential for hydrology, particularly in flood forecasting, drought monitoring, watershed management, and groundwater resource assessment. These technologies can help overcome major challenges faced by hydrologists, such as data gaps, high computational costs, and the complexity of hydrological systems. This study aims to explore the integration of these technologies, identify key research trends, and propose future directions to enhance the joint application of RS and ML in hydrology.

Method:

The methodology adopted for this study involves a bibliometric analysis to assess the application of remote sensing and machine learning in hydrology. Bibliometric analysis examines various scholarly outputs, researchers, and institutions in a specific scientific domain to identify research trends, collaboration networks, and knowledge structures. The analysis was performed in five key stages. Initially, precise research questions were defined to guide the study, followed by the selection of relevant research articles from the Web of Science database. The research used Boolean search strings to extract articles based on keywords related to remote sensing, machine learning, and various hydrological sciences. Data collection involved gathering bibliographic information such as article titles, authors, citations, keywords, and abstracts. This study used the Bibliometrix R package for the analysis, which is equipped with statistical algorithms, numerical methods, and visualization capabilities. Network analysis and keyword clustering were conducted using tools like Bibliophagy within the Bibliometrix package, facilitating the identification of prominent research trends, influential authors, and emerging topics in the field.

Results:

The bibliometric analysis revealed key trends and gaps in the integration of remote sensing and machine learning in hydrological research. A significant portion of the literature focuses on the application of RS data, such as satellite imagery and airborne observations, in hydrological modeling, particularly in flood prediction, river discharge, and precipitation estimation. Machine learning techniques such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Decision Trees have been employed to improve prediction accuracy in these areas. More recent studies have shifted toward deep learning techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), demonstrating superior performance in handling complex hydrological processes. However, challenges remain, particularly in integrating the temporal and spatial dimensions of RS data into ML models. Additionally, data scarcity, especially in developing countries, limits the training of robust ML models. The analysis also highlighted a lack of comprehensive interdisciplinary frameworks that integrate various aspects of hydrological systems, suggesting the need for more holistic approaches in future research.

Conclusions:

The integration of remote sensing and machine learning holds significant promise for advancing hydrological modeling and improving water resource management. This study underscores the need for continued innovation in techniques to address existing challenges, particularly the effective integration of temporal and spatial RS data into ML models. Future research should focus on developing interdisciplinary frameworks that account for the complexity of hydrological systems and address data gaps, especially in regions with limited access to high-quality datasets. The study also emphasizes the importance of collaboration among researchers, institutions, and governments to foster knowledge exchange and promote the development of scalable, efficient, and accurate models for hydrological predictions. By advancing these methodologies, hydrologists can better tackle the global challenges posed by climate change and environmental uncertainty, ensuring more reliable water resource management for the future.

Author Contributions:

For the article titled Integration of Remote Sensing Data and Advanced Machine Learning Techniques for Hydrological Prediction and Analysis: Emerging Approaches and Challenges, the contributions of the authors are as follows:

Conceptualization: Moein Tosan, Raziyeh Shamshirgaran, Abolfazl Akbarpour; Methodology: Moein Tosan; Software: Raziyeh Shamshirgaran; Validation: Moein Tosan, Raziyeh Shamshirgaran, Abolfazl Akbarpour; Formal Analysis: Moein Tosan; Investigation: Moein Tosan; Resources: Raziyeh Shamshirgaran; Data Curation: Raziyeh Shamshirgaran; Writing—Original Draft Preparation: Moein Tosan; Writing—Review and Editing: Moein Tosan, Raziyeh Shamshirgaran, Abolfazl Akbarpour; Visualization: Raziyeh Shamshirgaran; Supervision: Abolfazl Akbarpour; Project Administration: Abolfazl Akbarpour; Funding Acquisition: Abolfazl Akbarpour; All authors have read and agreed to the published version of the manuscript.

Data Availability Statement:

Data available on request from the authors.

Ethical considerations:

This study did not require approval from an ethics committee. The authors declare that no data fabrication, falsification, plagiarism, or any form of misconduct was involved in the research.

Conflict of interest

The author declares no conflict of interest.