Machine Learning Advances Radiation Modeling

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Aug 18 2024

In a paper published in the journal Remote Sensing, researchers examined the relevance of spatial modeling for understanding and predicting spatial patterns and optimizing resources related to solar energy budgets. They systematically reviewed recent advances in machine learning (ML) and deep learning (DL) techniques, applying specific search parameters to databases like Scopus and ScienceDirect.

The study analyzed trends, citations, and methodologies, highlighting a need for more recent research on spatial modeling of solar radiation. Their results emphasized the necessity for further thorough research in this field.

Related Work

The literature review systematically searched major databases like Scopus, ScienceDirect, and ResearchGate with keywords like "machine learning" and "spatial modeling." Boolean operators and refined search parameters were applied to focus on relevant, recent scientific articles and reviews managed through Mendeley reference software.

The refined search results were classified based on citation impact and relevance, revealing the significant role of spatial interpolation techniques and the growing influence of ML and DL in the field. The review highlighted a shift from traditional geostatistical methods to more advanced ML and DL algorithms for modeling solar and terrestrial radiation. This approach underscores the increasing prevalence of these advanced techniques in developing precise and complex spatial models.

Spatial Modeling Advances

The analysis and review of selected articles have led to identifying key results, starting with exploring search procedures and developing four primary classifications related to terrestrial and solar radiation models. These classifications include methods based on interpolation, remote sensors, applications, and ML applications to Earth radiation modeling. The team organized initial results through summary tables, which allowed for a structured presentation of objectives and findings.

The most recent and significant international contributions regarding ML applications in spatial distribution models of terrestrial and solar radiation were reviewed in detail. The search strategy involved multiple iterations across SCOPUS, ResearchGate, and ScienceDirect databases. Keywords, Boolean operators, and advanced filters were employed and refined to obtain precise results related to ML techniques for spatial interpolation in solar radiation, radiative energy budgets, or climate variables.

The first phase of the search produced 31 relevant documents from 2018 to 2023, focusing on ML and radiation. After excluding medical documents and limiting results to final publication stages, the results revealed a diverse range of document types and significant academic contributions from China, the United States, and South Korea.

A refined search on "solar radiation" and limiting results to articles yielded 22 relevant documents, primarily in planetary and earth sciences. This phase highlighted key institutions like Zhejiang University and The Hong Kong Polytechnic University, confirming significant contributions from China, the United States of America (U.S)., and South Korea and underscoring growing interest in ML for analyzing complex climate variables.

In terms of classification, the research was categorized based on interpolation methods, remote sensors, and applications. Detailed summaries of the most relevant studies were provided, focusing on spatial autocorrelation methods, traditional geostatistics for interpolation, and commonly used satellite remote sensors. Applications of spatial models in different fields were also highlighted, demonstrating their impact and development. The section on ML applied to terrestrial radiation modeling was particularly emphasized, reflecting its central role and growing significance in recent research.

Additionally, the review addressed the evolution of techniques and the integration of new technologies, illustrating how advancements in ML are reshaping the field. In addition to showcasing the state of the field, this thorough review points up new directions and prospective study areas. ML has notably advanced in the study of spatial modeling, especially in solar and terrestrial radiation.

Recent studies have employed various ML techniques to enhance the accuracy and efficiency of spatial distribution models. Key studies have explored different ML approaches, including artificial neural networks (ANN), random forests (RF), and deep neural networks (DNN), demonstrating their effectiveness in predicting solar radiation, estimating energy potentials, and interpolating missing data.

The application of ML in these areas has shown considerable improvements in model performance and accuracy, though challenges remain. The final chapter of the review will discuss these advances, limitations, and emerging trends in ML for spatial modeling. Additionally, it will explore the integration of ML with other technologies, such as geographic information systems (GIS), and the potential for future breakthroughs in model innovation and application.

Conclusion

To sum up, this review highlighted significant advancements in the application of ML to spatial modeling of solar and terrestrial radiation. It effectively addressed gaps in comparative studies by showcasing the superior performance of ML algorithms, such as ANN, DNN, and RF, over traditional geostatistical methods. The methodology proved efficient in obtaining relevant results and distinguishing high-quality information.

ML techniques have markedly improved the precision of spatial interpolation models, while geostatistical methods remain useful but are increasingly complemented by ML approaches. This review underscored a shift towards more sophisticated and accurate modeling tools in evolving climate conditions.