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1. Assessment of Spatio-Temporal Empirical Forecasting Performance of Future Shoreline Positions

   https://doi.org/10.3390/rs14246364  

   Islam, Md Sariful; Crawford, Thomas W. (December 2022, Remote Sensing)

   Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Multi-temporal Landsat imagery, from 1988 to 2021, was used to quantify the rates of shoreline movement for different time period. Predictions using the simple extrapolation of the end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR), and the Kalman filter method were used to predict future shoreline positions. Root mean square error (RMSE) was used to assess prediction accuracies. For time depth, our results revealed that the higher the number of shorelines used in calculating and predicting shoreline change rates the better predictive performance was yielded. For the time horizon, prediction accuracies were substantially higher for the immediate future years (138 m/year) compared to the more distant future (152 m/year). Our results also demonstrated that the forecast performance varied temporally and spatially by time period and region. Though the study area is located in coastal Bangladesh, this study has the potential for forecasting applications to other deltas and vulnerable shorelines globally. 

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2. Quantifying the impacts of future shoreline modification on biodiversity in a case study of coastal Georgia, United States

   https://doi.org/10.1111/cobi.14301  

   Coleman, Daniel J; Gittman, Rachel K; Landry, Craig E; Byers, James E; Alexander, Clark R; Coughlin, G Paul; Woodson, C Brock (May 2024, Conservation Biology)

   Abstract People often modify the shoreline to mitigate erosion and protect property from storm impacts. The 2 main approaches to modification are gray infrastructure (e.g., bulkheads and seawalls) and natural or green infrastructure (NI) (e.g., living shorelines). Gray infrastructure is still more often used for coastal protection than NI, despite having more detrimental effects on ecosystem parameters, such as biodiversity. We assessed the impact of gray infrastructure on biodiversity and whether the adoption of NI can mitigate its loss. We examined the literature to quantify the relationship of gray infrastructure and NI to biodiversity and developed a model with temporal geospatial data on ecosystem distribution and shoreline modification to project future shoreline modification for our study location, coastal Georgia (United States). We applied the literature‐derived empirical relationships of infrastructure effects on biodiversity to the shoreline modification projections to predict change in biodiversity under different NI versus gray infrastructure scenarios. For our study area, which is dominated by marshes and use of gray infrastructure, when just under half of all new coastal infrastructure was to be NI, previous losses of biodiversity from gray infrastructure could be mitigated by 2100 (net change of biodiversity of +0.14%, 95% confidence interval −0.10% to +0.39%). As biodiversity continues to decline from human impacts, it is increasingly imperative to minimize negative impacts when possible. We therefore suggest policy and the permitting process be changed to promote the adoption of NI. 

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3. Does deep learning help topic extraction? A kernel k-means clustering method with word embedding,

   Zhang, Y.; Lu, J.; Liu, F.; Liu, Q.; Porter, A.L.; Chen, H.; Zhang, G. (January 2018, Journal of informetrics)

   Topic extraction presents challenges for the bibliometric community, and its performance still depends on human intervention and its practical areas. This paper proposes a novel kernel k-means clustering method incorporated with a word embedding model to create a solution that effectively extracts topics from bibliometric data. The experimental results ofa comparison of this method with four clustering baselines (i.e., k-means, fuzzy c-means, principal component analysis, and topic models) on two bibliometric datasets demonstrate its effectiveness across either a relatively broad range of disciplines or a given domain. An empirical study on bibliometric topic extraction from articles published by three top-tier bibliometric journals between 2000 and 2017, supported by expert knowledge-based evaluations, provides supplemental evidence of the method’s ability on topic extraction. Additionally, this empirical analysis reveals insights into both overlapping and diverse research interests among the three journals that would benefit journal publishers, editorial boards, and research communities. 

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4. Influence of El Niño on the variability of global shoreline position

   https://doi.org/10.1038/s41467-023-38742-9  

   Almar, Rafael; Boucharel, Julien; Graffin, Marcan; Abessolo, Gregoire Ondoa; Thoumyre, Gregoire; Papa, Fabrice; Ranasinghe, Roshanka; Montano, Jennifer; Bergsma, Erwin W.; Baba, Mohamed Wassim; et al (December 2023, Nature Communications)

   Abstract Coastal zones are fragile and complex dynamical systems that are increasingly under threat from the combined effects of anthropogenic pressure and climate change. Using global satellite derived shoreline positions from 1993 to 2019 and a variety of reanalysis products, here we show that shorelines are under the influence of three main drivers: sea-level, ocean waves and river discharge. While sea level directly affects coastal mobility, waves affect both erosion/accretion and total water levels, and rivers affect coastal sediment budgets and salinity-induced water levels. By deriving a conceptual global model that accounts for the influence of dominant modes of climate variability on these drivers, we show that interannual shoreline changes are largely driven by different ENSO regimes and their complex inter-basin teleconnections. Our results provide a new framework for understanding and predicting climate-induced coastal hazards. 

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5. Analysis of pain research literature through keyword Co-occurrence networks

   https://doi.org/10.1371/journal.pdig.0000331  

   Ozek, Burcu; Lu, Zhenyuan; Pouromran, Fatemeh; Radhakrishnan, Srinivasan; Kamarthi, Sagar (September 2023, PLOS Digital Health)

   Feng, Mengling (Ed.)

   Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work reviews and analyzes a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics. 

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