An official website of the United States government
Abstract The vadose zone—the variably saturated, near‐surface environment that is critical for ecosystem services such as food and water provisioning, climate regulation, and infrastructure support—faces increasing pressures from both anthropogenic and natural factors, including changing climatic conditions. A more comprehensive understanding of vadose zone processes and interactions is imperative to effectively address these challenges and safeguard water and soil resources. This review outlines selected key issues, knowledge gaps, and research opportunities across six thematic sections. Each section presents a problem statement, a summary of recent innovations, and a compilation of emerging challenges and study opportunities. The selected topics include scaling and modeling of vadose zone properties and processes, soil moisture monitoring initiatives, surface energy balance, interplay between preferential water flow paths and biogeochemical processes, interactions between fires and vadose zone dynamics, and emerging contaminants and their fate in the vadose zone. This overview is intended to serve as a compendium of vadose zone science that encompasses both insights gained from prior research and anticipated needs for the coming years.
more »
« less
Machine learning applications in vadose zone hydrology: A review
Abstract Machine learning (ML) has been broadly applied for vadose zone applications in recent years. This article provides a comprehensive review of such developments. ML applications for variables corresponding to different complex vadose zone processes are summarized mostly in a prediction context. By analyzing and assessing these applications, we discovered extensive usages of classic ML models with relatively limited applications of deep learning (DL) approaches in general. We also recognized a lack of benchmark datasets for soil property research as well as limited integration of physics‐based vadose zone principles into the ML approaches. To facilitate this interdisciplinary research of ML in vadose zone characterization and processes, a paradigm of knowledge‐guided machine learning is suggested along with other data‐driven and ML model‐based research suggestions to advance future research.
more »
« less
- Award ID(s):
- 1934721
- PAR ID:
- 10514970
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Vadose Zone Journal
- Volume:
- 23
- Issue:
- 4
- ISSN:
- 1539-1663
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Machine learning (ML) provides a powerful framework for the analysis of high‐dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non‐linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large‐scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML‐based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large‐scale assessment in education. Context and implicationsRationale for this studyIn 2020, the worldwide SARS‐COV‐2 pandemic forced the educational sciences to perform a rapid paradigm shift with classrooms going online around the world—a hardly novel but now strongly catalysed development. In the context of data‐driven education, this paper demonstrates that the widespread adoption of machine learning techniques is central for the educational sciences and shows how these methods will become crucial tools in the collection and analysis of data and in concrete educational applications. Helping to leverage the opportunities and to avoid the common pitfalls of machine learning, this paper provides educators with the theoretical, conceptual and practical essentials.Why the new findings matterThe process of teaching and learning is complex, multifaceted and dynamic. This paper contributes a seminal resource to highlight the digitisation of the educational sciences by demonstrating how new machine learning methods can be effectively and reliably used in research, education and practical application.Implications for educational researchers and policy makersThe progressing digitisation of societies around the globe and the impact of the SARS‐COV‐2 pandemic have highlighted the vulnerabilities and shortcomings of educational systems. These developments have shown the necessity to provide effective educational processes that can support sometimes overwhelmed teachers to digitally impart knowledge on the plan of many governments and policy makers. Educational scientists, corporate partners and stakeholders can make use of machine learning techniques to develop advanced, scalable educational processes that account for individual needs of learners and that can complement and support existing learning infrastructure. The proper use of machine learning methods can contribute essential applications to the educational sciences, such as (semi‐)automated assessments, algorithmic‐grading, personalised feedback and adaptive learning approaches. However, these promises are strongly tied to an at least basic understanding of the concepts of machine learning and a degree of data literacy, which has to become the standard in education and the educational sciences.Demonstrating both the promises and the challenges that are inherent to the collection and the analysis of large educational data with machine learning, this paper covers the essential topics that their application requires and provides easy‐to‐follow resources and code to facilitate the process of adoption.more » « less
-
Abstract Direct ink writing (DIW) is an extrusion-based additive manufacturing technology. It has gained wide attentions in both industry and research because of its simple design and versatile platform. In electric-field-assisted Direct Ink Writing (eDIW) processes, an external electric field is added between the nozzle and the printing substrate to manipulate the ink-substrate wetting dynamics and therefore optimize the ink printability. eDIW was found effective in printing liquids that are typically difficult to print in the conventional DIW processes. In this paper, an eDIW process modeling system based on machine learning (ML) algorithms is developed. The system is found effective in predicting eDIW printing geometry under varied process parameter settings. Image processing approaches to collect experiment data are developed. Accuracies of different machine learning algorithms for predicting printing results and trace width are compared and discussed. The capabilities, applications and limitations of the presented machine learning-based modeling approach are presented.more » « less
-
Abstract This review paper examines the application and challenges of machine learning (ML) in intelligent welding processes within the automotive industry, focusing on resistance spot welding (RSW) and laser welding. RSW is predominant in body-in-white assembly, while laser welding is critical for electric vehicle battery packs due to its precision and compatibility with dissimilar materials. The paper categorizes ML applications into three key areas: sensing, in-process decision-making, and post-process optimization. It reviews supervised learning models for defect detection and weld quality prediction, unsupervised learning for feature extraction and data clustering, and emerging generalizable ML approaches like transfer learning and federated learning that enhance adaptability across different manufacturing conditions. Additionally, the paper highlights the limitations of current ML models, particularly regarding generalizability when moving from lab environments to real-world production, and discusses the importance of adaptive learning techniques to address dynamically changing conditions. Case studies like virtual sensing, defect detection in RSW, and optimization in laser welding illustrate practical applications. The paper concludes by identifying future research directions to improve ML adaptability and robustness in high-variability manufacturing environments, aiming to bridge the gap between experimental ML models and real-world implementation in automotive welding.more » « less
-
Abstract We present an overview of recent work on using artificial intelligence (AI)/machine learning (ML) techniques for forecasting convective weather and its associated hazards, including tornadoes, hail, wind, and lightning. These high-impact phenomena globally cause both massive property damage and loss of life, yet they are very challenging to forecast. Given the recent explosion in developing ML techniques across the weather spectrum and the fact that the skillful prediction of convective weather has immediate societal benefits, we present a thorough review of the current state of the art in AI and ML techniques for convective hazards. Our review includes both traditional approaches, including support vector machines and decision trees, as well as deep learning approaches. We highlight the challenges in developing ML approaches to forecast these phenomena across a variety of spatial and temporal scales. We end with a discussion of promising areas of future work for ML for convective weather, including a discussion of the need to create trustworthy AI forecasts that can be used for forecasters in real time and the need for active cross-sector collaboration on testbeds to validate ML methods in operational situations. Significance StatementWe provide an overview of recent machine learning research in predicting hazards from thunderstorms, specifically looking at lightning, wind, hail, and tornadoes. These hazards kill people worldwide and also destroy property and livestock. Improving the prediction of these events in both the local space as well as globally can save lives and property. By providing this review, we aim to spur additional research into developing machine learning approaches for convective hazard prediction.more » « less
