United States Geological Survey Research Articles

Seismic velocity changes from repetitive seismicity at Mauna Loa prior to and during its 2022 eruption

AbstracMauna Loa’s short-lived eruption from late November to early December 2022 marked the culmination of nearly a decade of elevated seismic activity and geodetic inflation. The volcano has been monitored by a network of permanent, short period and broadband seismometers. I used the continuous waveform data from that network starting in 2012 to generate a catalog of seismicity that enhances the US Geological Survey Hawaiian Volcano Observatory’s public seismic catalog with four times the number of earthquakes, which were then grouped by waveform similarity. Analysis of subtle delays in the timing of arrivals of scattered waves between pairs of earthquakes in this catalog yields a history of small changes in the shallow seismic velocity structure of the volcano. Seismic velocities have been shown at other volcanoes to change during unrest and eruption. My results show a decrease in seismic velocity centered on the summit beginning in September 2022, corresponding to the onset of a vigorous precursory swarm of seismic activity and shallow inflation. During the eruption itself, I observe large changes due likely to dike opening along the northeast rift zone and deflation of the summit reservoir. However, seismic velocity changes associated with non-volcanic sources such as ground shaking from large earthquakes and meteorological influences at seasonal and diurnal time scales are also observed, and these dominate the velocity changes prior to the eruption. Proper accounting of these effects will be a requirement for use in real-time monitoring, and this work serves as a starting point in that endeavor for Mauna Loa.

A Numerical Study of the Impact of Topography on the July 2021 Extreme Rainfall Event in Zhengzhou, China

AbstractAn unprecedented extreme rainfall event occurred in Zhengzhou, Henan Province, China, in July 2021. To understand the impact of local topography on this extreme rainfall event, the Weather Research and Forecasting model is configured with 27 and 9 km model grid spacings (MGS), along with United States Geological Survey (USGS) topography data at 8.3 and 0.9 km resolutions, called MGS27_USGS8.3, MGS9_USGS8.3, and MGS9_USGS0.9. Results show that the 9 km MGS, permitting activities with coarse γ scales (∼20 km), successfully reproduces the generation of mesoscale cyclones. However, the finer topography enables a more accurate representation of orographic blocking and lifting effects, thereby adjusting the position of the mesoscale cyclone. It can depict the location of adiabatic processes, local circulation, and vertical pressure gradient forces more accurately, thereby adjusting the position of topography‐induced vertical motions. The turbulence diagnostics show that the topography‐induced lifting motion enhances clouds that block longwave radiation, leading to local environment warming, which in turn enhances turbulence and further amplifies the updrafts, ultimately improving the spatial distribution and temporal variation of precipitation. This study provides insights for an in‐depth understanding of the mechanisms of topography on extreme rainfall.

Niamey: The ages of a city

Over the last few decades, countries in the South have been undergoing rapid urbanization, as if to make up for lost time. Sub-Saharan Africa is characterized by a very low urbanization rate compared to0 the rest of the world. Although the African continent reached its urban transition in 2015, Niger remains by far the least urbanized country, with a rate of 17%. The city of Niamey is the main urban center, with an estimated population of 1,449,801 hbts in 2023, spread over an area of around 33,100 ha. The aim of this study is to analyze the spatial expansion of the city of Niamey from 1984 to 2023. The main data used in this study are raster images from the United States Geological Survey (USGS), vector data from Open Sources Map (OSM) and GoogleEarth, secondary data from the National Institute of Statistics (INS) and field observation. This study enabled us to conclude that between 1984 and 2023, the city of Niamey underwent very strong spatial expansion. The city grew from 4,690 ha to 33,100 ha, i.e. 28,410 ha absorbed in 39 years, with exceptional growth between 2014 and 2023, when the urban area doubled. Its population has risen from 397,437 at the time of the 1988 general population and housing census to an estimated 1,449,801 in 2023 (INS), an increase of 1,052,364 in 35 years. Between these two dates, population density fell from 87.7 to 43.8 inhabitants/km2, i.e. half that of 1984. This spatial expansion has resulted in unprecedented peri-urbanization.

Prioritizing US Geological Survey science on salinization and salinity in candidate and selected priority river basins

The US Geological Survey (USGS) is selecting and prioritizing basins, known as Integrated Water Science basins, for monitoring and intensive study. Previous efforts to aid in this selection process include a scientifically defensible and quantitative assessment of basins facing human-caused water resource challenges (Van Metre et al. in Environmental Monitoring and Assessment, 192(7), 458 2020). In the present work, we explore this ranking process based on water quality considerations, specifically salinity and salinization. We selected top candidate basins to study salinity and salinization issues in 18 hydrologic regions that include 163 candidate basins. Our prioritization is based on quantitative assessment of sources of salinity, drivers of change, and receptors that must respond to those sources and drivers. Source terms represented in the prioritization include geology, depth to brackish groundwater, stream conductivity, chloride in precipitation, urban and agricultural land use, application of road salt as a deicer, and irrigation. Drivers represented in prioritization include changes in chemical weathering as a result of changes in rainwater chemistry. Receptors include measures of water stress, measurements of stream ecological health, and socioeconomic factors. In addition, we present research activities for the USGS on salinity and salinization that can be pursued in these basins including assessment of sources, pathways, and loadings; predicting and understanding changes in sources, peaks, and trends; understanding the components of salinity and mobilization of contaminants; understanding the relationship between salinization and changing ecosystems; and developing knowledge on the causes and distribution of groundwater salinity, brackish water resources, and challenges related to desalination.

Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet

Semantic segmentation algorithms, such as UNet, that rely on convolutional neural network (CNN)-based architectures, due to their ability to capture local textures and spatial context, have shown promise for anthropogenic geomorphic feature extraction when using land surface parameters (LSPs) derived from digital terrain models (DTMs) as input predictor variables. However, the operationalization of these supervised classification methods is limited by a lack of large volumes of quality training data. This study explores the use of transfer learning, where information learned from another, and often much larger, dataset is used to potentially reduce the need for a large, problem-specific training dataset. Two anthropogenic geomorphic feature extraction problems are explored: the extraction of agricultural terraces and the mapping of surface coal mine reclamation-related valley fill faces. Light detection and ranging (LiDAR)-derived DTMs were used to generate LSPs. We developed custom transfer parameters by attempting to predict geomorphon-based landforms using a large dataset of digital terrain data provided by the United States Geological Survey’s 3D Elevation Program (3DEP). We also explored the use of pre-trained ImageNet parameters and initializing models using parameters learned from the other mapping task investigated. The geomorphon-based transfer learning resulted in the poorest performance while the ImageNet-based parameters generally improved performance in comparison to a random parameter initialization, even when the encoder was frozen or not trained. Transfer learning between the different geomorphic datasets offered minimal benefits. We suggest that pre-trained models developed using large, image-based datasets may be of value for anthropogenic geomorphic feature extraction from LSPs even given the data and task disparities. More specifically, ImageNet-based parameters should be considered as an initialization state for the encoder component of semantic segmentation architectures applied to anthropogenic geomorphic feature extraction even when using non-RGB image-based predictor variables, such as LSPs. The value of transfer learning between the different geomorphic mapping tasks may have been limited due to smaller sample sizes, which highlights the need for continued research in using unsupervised and semi-supervised learning methods, especially given the large volume of digital terrain data available, despite the lack of associated labels.

Investigation of Hatay-Defne earthquake (20.02.2023) by using GNSS station

No studies have been conducted on the spatial changes related to the Hatay-Defne earthquake (20 February 2023) up to now. The reason for this situation is that the requested data cannot be accessed as a result of the power and internet interruptions caused by the earthquake. The southern Turkish city of Hatay had a 6.3 magnitude earthquake at 20:04 on February 20, 2023. Three minutes later, a 5.8 magnitude aftershock occurred, and 90 more aftershocks followed. These earthquakes, which were felt across the area, caused individuals who survived the horrific quakes on February 6, 2023, to experience new levels of dread. In the district of Hatay-Defne, the epicenter began. In its first assessment, the United States Geological Survey gave the earthquake a magnitude of 6.3 at a depth of 16 kilometers. In this study, firstly, GNSS data belonging to ARST station in 30 seconds recording interval were downloaded into CSRS-PPP. GNSS data was processed as static and then was processed as epoch to epoch by using kinematic method. The coordinate differences between the obtained coordinates by using the kinematic processing and the obtained coordinates by using the static processing were computed. According to the obtained results, a horizontal movement of approximately 8.30 cm in the south-west direction was observed at the ARST station, which is the closest to the earthquake center.

Methods for the Construction and Editing of an Efficient Control Network for the Photogrammetric Processing of Massive Planetary Remote Sensing Images

Planetary photogrammetry remains an important technical means of producing high-precision planetary maps. High-quality control networks are fundamental to successful bundle adjustment. However, current software tools used by the planetary mapping community to construct and edit control networks exhibit very low efficiency. Moreover, redundant and invalid control points in the control network can further increase the time required for the bundle adjustment process. Due to a lack of targeted algorithm optimization, existing software tools and methods are unable to meet the photogrammetric processing requirements of massive planetary remote sensing images. To address these issues, we first proposed an efficient control network construction framework based on approximate orthoimage matching and hash quick search. Next, to effectively reduce the redundant control points in the control network and decrease the computation time required for bundle adjustment, we then proposed a control network-thinning algorithm based on a K-D tree fast search. Finally, we developed an automatic detection method based on ray tracing for identifying invalid control points in the control network. To validate the proposed methods, we conducted photogrammetric processing experiments using both the Lunar Reconnaissance Orbiter (LRO) narrow-angle camera (NAC) images and the Origins Spectral Interpretation Resource Identification Security Regolith Explorer (OSIRIS-REx) PolyCam images; we then compared the results with those derived from the famous open-source planetary photogrammetric software, the United States Geological Survey (USGS) Integrated Software for Imagers and Spectrometers (ISIS) version 8.0.0. The experimental results demonstrate that the proposed methods significantly improve the efficiency and quality of constructing control networks for large-scale planetary images. For thousands of planetary images, we were able to speed up the generation and editing of the control network by more than two orders of magnitude.

Analysis of Mangrove vegetation health index by implementing NDVI classification method based on Landsat image data: Case study from Zuari Estuary Goa, India

Mangrove ecosystems are among the most productive and biologically complex ecosystems in the world providing many ecosystem services for human wellbeing, hence they have an outstanding relevance ecologically and economically. Mangrove habitats are temporarily inundated and often located in inaccessible regions. In Goa, fringing mangroves are distributed in Zuari and Mandovi estuaries based on topography and environmental conditions. These estuarine regions are used as inland waterways for transportation of iron ore, tourism, infrastructure development like bridges, jetties, highways, resorts and hotels, residential apartments, restaurants, small roads etc. Zuari estuary is located at lat. 15°25'N and long. 73°59'E on the central west coast of India. The images were downloaded from United States Geological Survey website (USGS). The radiometric and geometric corrections were made to reduce the undesirable noise component and changes in the amount of incoming radiance from sensors. Anderson level 1 classification was used for the classification purpose. The mangroves were digitized using hybrid classification method. For mangrove demarcation Google Earth Pro and visual interpretation techniques were used. Normalised Difference Vegetation Index (NDVI) analysis was adapted to perceive the health status of mangroves covers in the study area. The NDVI results were divided into three categories: (0.00-0.15 Stressed), (0.15-0.30 Moderately healthy), (0.30-0.47 Very healthy). Rhizophora and 01sp. each of Aegiceras, Bruguiera, Kandelia, Acanthus and Excoecaria along with mangrove associates like Acrosticum, Clerodendron, Salvadora, Ipomoea and Derris. Sonneretia alba and Avicennia marina were found to be dominating followed by Rhizophora mucronata. Stressed patches of mangroves can be attributed to the human interference from the surrounding regions, changes in water salinity, increase in pollution levels due to mining activities (transportation of iron ore from barges), infrastructure development etc. Moderately healthy and very healthy patches of mangroves may be associated with suitable ecological and natural conditions favouring healthy growth and also lack of anthropogenic interference and awareness about the importance of mangroves.

Shattered ground, shaken minds: Mental health consequences of earthquakes

Despite growing recognition of the importance of mental health status for the achievement of the global development goals, substantial challenges persist in addressing this issue in both developed and developing countries. The literature has pointed to a variety of conditions as triggers for mental health problems, including exposure to unexpected natural disasters. Contributing to the literature, our study quantifies the mental health consequences of the devastating 2006 Yogyakarta earthquake in Indonesia. We combine the Modified Mercalli Intensity from the United States Geological Survey with individual-level data from the Indonesia Family Life Survey to assess the impacts on municipalities with varying earthquake intensities. Employing a difference-in-differences approach, we identify a significant and persistent deterioration in the mental health condition for individuals in municipalities with stronger earthquake severity. To explore the mechanisms underlying this impact, we analyze the roles of family casualties, physical health declines, and socio-economic disruptions, identifying family loss and worsened physical health as particularly influential factors driving the observed mental health outcomes.

A Parametric Study of Mathematical Model for Long-Term Localized Corrosion of Copper in Canadian Deep Geological Repository

The concept of Adaptive Phased Management, approved by Canadian federal authorities and implemented by the Nuclear Waste Management Organization, forms a comprehensive strategy for the long-term safe management of used nuclear fuel in Canada.1 The used nuclear fuel will ultimately be placed within a container in a deep geological repository (DGR). Current container designs employ steel vessels with a protective copper outer layer, strategically selected to reduce corrosion damage. The copper-coated container may be subjected to various corrosion mechanisms over time as the DGR environment evolves. Specific to this work and in the early aerated environment of the DGR, efflorescing salt impurities present on the container surface may lead to the formation of an Evan’s droplet. To understand the behavior of the copper-coated container with respect to localized corrosion and ensure the effectiveness of this protection strategy, a preliminary two-dimensional axisymmetric time-dependent model for the corrosion of copper under an Evans droplet was developed.2, 3 The mathematical model was developed using the finite-element method (COMSOL Multiphysics). Some unique features of the model are inclusion of six heterogeneous and fifteen homogeneous reactions, implicit calculation of nm-scale films, and treatment of the influence of films on surface concentrations and potentials. The model shows the time-dependent localized corrosion rates and depths, calculated for an elapsed time of ten years. It also shows time-dependent radial distributions for current density and surface coverage of films. The influence of oxygen conditions and temperature was included, and the model accounted for the influence of films on reaction rate constants. Preliminary results show that the corrosion of copper in the Evan’s droplet is almost uniform on the electrode surface. Temperature and oxygen concentration was shown to have a strong contribution to copper corrosion.The current version of the mechanical model has many parameters. Some parameters are obtained from PHREEQC thermodynamic software,4 some are selected to yield good results, and some are defined using expert judgement from the literature.While the number of parameters is very large, the impact of each parameter is constrained by the model. A parametric study is necessary to identify reasonable ranges of values and explore different conditions that may lead to localized corrosion of copper. For example, to switch the mechanism from kinetic control to mass-transfer control, the overall rate constants were increased by different orders of magnitude. The influence of both oxide film and salt film on rate constants reduction were also studied. Combining both the blocking effect of films on the copper surface and the control mechanism shift, simulations were completed to study the parameter space. The present work shows that the corrosion rate increased as rate constants were increased, and currents became limited by mass transport of oxygen.References NWMO, “Choosing a Way Forward. The Future Management of Canada’s Used Nuclear Fuel. Final Study,” Nuclear Waste Management Organization, Toronto, Ontario, 2005.C. You, S. Briggs, and M. E. Orazem, “Model Development Methodology for Localized Corrosion of Copper,” Corrosion Science, 222 (2023), 111388.C. You, Y. Chuai, S. Briggs, and M. E. Orazem, “Model for Corrosion of Copper in a Nuclear Waste Repository,” Corrosion Science, 226 (2023), 111658.PHREEQC Version 3, United States Geological Survey, 202, https://www.usgs.gov/software/phreeqc-version-3. AcknowledgementThis work was supported by the Nuclear Waste Management Organization, Canada, under project 2000904.

SEISMIC RATE CHANGES ANALYSIS BASED ON THE SPASIAL DISTRIBUTION SEISMOTECTONICS IN THE SOUTHERN SUMATRA REGION

The southern Sumatra region experiences high seismic activity, often resulting in large earthquakes that cause significant losses. Before such earthquakes, a phenomenon known as seismic quiescence a decrease in seismic activity commonly occurs. This phenomenon can be analyzed through changes in seismic rates using the spatial distribution of Z-values. This study investigates the occurrence of seismic quiescence before major earthquakes and examines changes in seismic rates in southern Sumatra. Used secondary earthquake data from the United States Geological Survey (USGS) for 1973–2023 were analyzed, focusing on the region between 2.050°S–5.885°S and 101.030°E–106.611°E. The study centered on three significant earthquakes: the 2000 (7.9 Mw), 2001 (7.4 Mw), and 2007 (8.4 Mw) events. Seismic rate changes were analyzed using the Z-value method, dividing the region into grids to calculate and spatially distribute Z-values. Results showed seismic quiescence before the 2000 (±16 years), 2001 (±13 years), and 2007 (±17 years) earthquakes in Bengkulu. Additionally, similar phenomena were observed in Bengkulu and Lampung before 2023, suggesting the potential for future significant earthquakes in the region. These findings highlight seismic quiescence as a precursor to major seismic events.

D-InSAR-Based Analysis of Slip Distribution and Coulomb Stress Implications from the 2024 Mw 7.01 Wushi Earthquake

On 23 January 2024, an Mw 7.01 earthquake struck the Wushi County, Xinjiang Uygur Autonomous Region, China. The occurrence of this earthquake provides an opportunity to gain a deeper understanding of the rupture behavior and tectonic activity of the fault system in the Tianshan seismic belt. The coseismic deformation field of the Wushi earthquake was derived from Sentinel-1A ascending and descending track data using Differential Interferometric Synthetic Aperture Radar (D-InSAR) technology. The findings reveal a maximum line-of-sight (LOS) displacement of 81.1 cm in the uplift direction and 16 cm in subsidence. Source parameters were determined using an elastic half-space dislocation model. The slip distribution on the fault plane for the Mw 7.01 Wushi earthquake was further refined through a coseismic slip model, and Coulomb stress changes on nearby faults were calculated to evaluate seismic hazards in surrounding areas. Results indicate that the coseismic rupture in the Mw 7.01 Wushi earthquake sequence was mainly characterized by left-lateral strike-slip motion. The peak fault slip was 3.2 m, with a strike of 228.34° and a dip of 61.80°, concentrated primarily at depths between 5 and 25 km. The focal depth is 13 km. This is consistent with findings reported by organizations like the United States Geological Survey (USGS). The fault rupture extended to the surface, consistent with field investigations by the Xinjiang Uygur Autonomous Region Earthquake Bureau. Coulomb stress results suggest that several fault zones, including the Kuokesale, Dashixia, Piqiang North, Karaitike, southeastern sections of the Wensu, northwestern sections of the Tuoergan, and the Maidan-Sayram Fault Zone, are within regions of stress loading. These areas show an increased risk of future seismic activity and warrant close monitoring.

Source-Scaling Comparison and Validation for Ridgecrest, California: Radiated Energy, Apparent Stress, and Mw Using the Coda Calibration Tool (2.6<Mw<7.1)

ABSTRACT The determination of accurate apparent stress, radiated energy, corner frequency, and their scaling with magnitude remains one of the most difficult seismological endeavors because of complicated 3D Earth structure, complex rupture, and limited broadband recordings. This study focuses on a comparison of four separate state-of-the-art methods that aim to compare and contrast common events using the well-recorded 2019 Ridgecrest, California, sequence, which was motivated in large part by the U.S. Geological Survey (USGS)/Southern California Earthquake Center (SCEC) Community Stress-Drop Validation Study group (Baltay et al., 2024). For this study, we calibrated the Ridgecrest and surrounding region using the Coda Calibration Tool (CCT) and compared them against recent generalized inversion technique (GIT) results of Bindi et al. (2021) (2.6<Mw<7.1) and two other state-of-the-art methods for moderate-sized events in the sequence (3.5<Mw<5.5). We find excellent agreement between the GIT and coda-derived results over a broad range of magnitudes, and for moderate-size events, we find equally good agreement with source estimates from finite-fault inversion method based on Dreger (1997) and a direct S-wave spectral method by Ji et al. (2024). As found in a recent comparative study in central Italy by Morasca et al. (2022) (3.5<Mw<6.3), we find that CCT and GIT results are in excellent agreement for events ranging between 2.6<Mw<7.1, and relative, weak-motion site terms are also in agreement. Although both approaches observe a modest increase in apparent stress with depth, the overall trend in apparent stress increasing with magnitude is supported by our findings. Finally, upon comparison with other regions, we find that the absolute apparent stress values from Ridgecrest are comparable to central Italy but significantly lower than both eastern Canada and the United Kingdom.

Comprehensive water quality indicators modeling by environmental protection view using multi optimized weighted ensemble machine learnings

The environmental protection water quality is a critical subject in dominance of the sustainable water resources management. Accordingly, the essential indicators of water quality were considered, which are not only appropriate indicators for water quality, but they can also serve as crucial indicators for the health of water environment and ecosystems. Therefore, this study uses well-known ensemble machine learning methodologies to investigate and predict the fluctuations of water quality parameters. Optimization procedures used to assemble machine learnings were non-linear programming (NLP), genetic, gradient descent, and least square algorithms, linear programming, particle swarm optimization, Nelder-Mead optimization, and simulating annealing optimization. Using optimization procedures, the basic MLs were assembled and eight new ensemble machine learning were developed. The studied area was the South Platte River basin, USA. The primary dataset was obtained through the online database of the United States Geological Survey, which contained sampling information on river water related to 2023–2024. Then, using clean missing and outlier data preprocessing techniques, the dataset was modified. Finally, using the 10-fold cross-validation technique, the primary data was validated. The results showed that NLP significantly improved the accuracy and performance of models, achieving the best performance with R2 of 0.9836 and 0.9031 across DO and pH modelings. The modeling results indicated that the pH parameter fluctuated within the safe range. While the DO seems that tolerated in unsafe domain for aquatic ecosystems. The findings of this research could help a wide range of decision-makers.

Seismic Activity Analysis in California: Patterns, Trends, and Predictive Modeling

For decades, California has been considered one of the most seismically active areas in the United States, if not the most, due to the frequent occurrence of earthquakes from tectonic activity, notably along the San Andreas Fault. Understanding seismic activity is a matter not only of safety but also of major importance for urban planning. In the recent past, Machine learning algorithms (ML) have emerged as a promising invention for advancing earthquake prediction by facilitating the pinpointing of patterns in large datasets that may be hard to detect through traditional techniques. The primary objective of this research project is to assess historical seismic data to identify trends and patterns in California’s seismic activity. Equally important, the goals of this research focused on developing predictive models that can provide insight into the possibility of seismic events in the future. To assess seismic activity in California, data were gathered from a credible and reputable source, most notably, the United States Geological Survey (USGS) Earthquake Database, which provides detailed records of earthquakes spanning over six decades. This dataset included all recorded seismic events in California, capturing the key details about the place of occurrence in latitude and longitude, the magnitude of the seismic event, the depth at which it happened, and the time when the seismic activity took place. To devise and curate a reliable earthquake prediction algorithm for California, distinct machine learning models were considered, comprising, Random Forest, XG-Boost, and Logistic Regression. Overall, the Random Forest algorithm exemplified high accuracy. Optimizing this algorithm will lead to more reliable predictions, potentially aiding disaster preparedness and risk mitigation in California's earthquake-prone areas. The findings from seismic activity analysis have deep implications for urban planning and disaster preparedness in California. Knowledge of the pattern, place, and magnitude of earthquakes over time will help urban planners and policymakers structure communities that can remain resilient during such calamities. For instance, higher earthquake frequencies would automatically call for increased stringency in building codes, especially along fault lines, to ensure that houses situated on such lines are designed to withstand major seismic activity. This operation may even limit or reallocate urban development out of high-risk zones into less risky zones.

Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery Data

The accurate and prompt mapping of flood-affected regions is important for effective disaster management, including damage assessment and relief efforts. While high-resolution optical imagery from satellites during disasters presents an opportunity for automated flood inundation mapping, existing segmentation models face challenges due to noises like cloud cover and tree canopies. Thanks to the digital elevation model (DEM) data readily available from sources such as United States Geological Survey (USGS), terrain guidance was utilized by recent graphical models such as hidden Markov trees (HMTs) to improve segmentation quality. Unfortunately, these methods either can only handle a small area where water levels at different locations are assumed to be consistent, or require restricted assumptions such as there is only one river channel. This paper presents an algorithm for flood extent mapping on large-scale Earth imagery, applicable to a large geographic area with multiple river channels. Since water level can vary a lot from upstream to downstream, we propose to detect river pixels in order to partition the remaining pixels into localized zones, each with a unique water level. In each zone, water at all locations flow to the same river entry point. Pixels in each zone are organized by an HMT to capture water flow directions guided by elevations. Moreover, a novel regularization scheme is designed to enforce inter-zone consistency by penalizing pixel-pairs of adjacent zones that violate terrain guidance. Efficient parallelization is made possible by coloring the zone adjacency graph to identify zones and zone-pairs that have no dependency and hence can be processed in parallel, and incremental one-pass terrain-guided scanning is conducted wherever applicable to reuse computations. Experiments demonstrate that our solution is more accurate than existing solutions, and can efficiently and accurately map out flooding pixels in a giant area of size 24,805 × 40,129. Despite the imbalanced workloads caused by a few large zonal HMTs dominating the serial computing time, our parallelization approach is effective and manages to achieve up to 14.3 × speedup on a machine with Intel Xeon Gold 6126 CPU @ 2.60GHz (24 cores, 48 threads) using 32 threads.

Optimizing transient monitoring of river streamflow by a highly predictive model utilizing Ensemble learning models and Multi algorithms

Global warming and population growth have significantly intensified the challenges in securing drinking water supplies. This study investigates transient instabilities of streamflow using ensemble machine learning (EML) and machine learning (ML) methodologies on the South Platte river in the United States. The United States Geological Survey’s online database was utilized to obtain the primary dataset. Several technical approaches were employed for preprocessing the initial dataset: cleaning outlier data, clean missing data, and 10 fold cross-validation. Nonlinear programming, genetic algorithms, least squares, linear programming, gradient descent, particle swarm optimization, Nelder Mead, and simulated annealing were employed algorithms to develop eight-weighted EML models. The results showed that the ensemble learning approach and the aggregation of weak learners by mentioned algorithms have been significantly successful. Particularly, the nonlinear programming-EML (NLP-EML) outperformed others, achieving the highest prediction accuracy with an R2 coefficient equal to 0.97. The probability density function showed that NLP-EML was the most reliable model. Overall, the findings highlight the superior performance and reliability of EML approaches in hydrological modeling, offering practical guidance to experts on the creation of robust ensemble models for improved prediction accuracy.

A Geospatial Analysis of the Association between Access to Tanning Bed Facilities and Melanoma in the United States New England Region.

Substantial geographic disparities in melanoma incidence and access to tanning bed facilities exist. In this spatial ecological study, we evaluate the relationship between access to tanning bed facilities and melanoma incidence. New England county-level melanoma incidence rates (2014-2018), tanning bed facilities location, demographic data, socioeconomic data, and geographic data were obtained from the National Cancer Institute State Cancer Profiles, Data Axle, American Community Survey, and United States Geological Survey. Spatial regression models were utilized to determine the association between melanoma incidence and access to tanning beds, adjusting for confounding factors. Adjusted spatial regression identified that for every 1-minute increase in average travel time to tanning facilities within 30 minutes travel time threshold, the melanoma incidence rate decreased by 3.46% in the same county and by 1.92% across New England. We found a negative association between average travel time to tanning facilities and county-level melanoma incidence rate, suggesting that policy aimed at decreasing access to indoor tanning facilities has the potential to reduce melanoma rates. High-risk clusters, with respect to county-level melanoma rate, were also identified. Targeting of interventions and resources to high-risk clusters may reduce geographic melanoma rates disparities.

Assessing visibility at highway-rail grade crossings using light detection and ranging (LiDAR) technology

In 2022, 2034 incidents occurred at highway-rail grade crossings (HRGCs) in the United States, posing significant risks such as fatalities, injuries, and property damage. These incidents underscore the need for effective prevention and mitigation strategies. With over 212,000 public and private HRGCs nationwide, safety monitoring is challenging, as traditional inspections primarily rely on manual assessments. Obstructed sightlines at HRGCs further increase safety risks by limiting road users' ability to see approaching trains. Although previous studies have addressed behavioral and safety issues, the literature currently lacks quantitative data analysis of sightlines at HRGCs. This study aims to address this gap by utilizing remote sensing techniques to identify and quantify sightline visibility. We studied 12 HRGCs using geospatial analysis in ArcGIS Pro through Viewshed and Observer Points analysis, integrating United States Geological Survey (USGS) Light Detection and Ranging (LiDAR) data with United States Federal Railroad Administration (FRA) crossing reports. Our findings indicate that sightline issues in the case studies reviewed are primarily linked to traffic control devices, environmental factors, and geometric conditions. Additionally, the results suggest that USGS LiDAR data and geospatial analysis offer potentially rapid and cost-effective methods for identifying sightline safety issues at HRGCs.

Development of a 3D Geologic Model Used in the Seismic Hazard and Liquefaction Hazard Analysis of Madison County, Tennessee

ABSTRACT Madison County of western Tennessee is approximately 100 km southeast of the New Madrid seismic zone and, thus, is subject to potentially dangerous seismic shaking and liquefaction events. Holocene floodplain alluvium and associated Pliocene/Pleistocene terraces of the Forked Deer and Hatchie Rivers cover a large part of Madison County. Underlying these unconsolidated sediments are approximately ∼650 m of poorly consolidated Paleogene and Cretaceous terrestrial and marine sediments, which are underlain by Paleozoic limestone bedrock. Uniform regional geology used in the 2014 United States Geological Survey seismic hazard maps assumes a homogenous seismic velocity structure above bedrock. Three-dimensional geologic mapping of the ∼700 m of Quaternary, Neogene, Paleogene, and Cretaceous sediments conducted in this study provides a more detailed velocity structure, resulting in a more accurate and detailed map of the expected ground motions in Madison County during future earthquakes. In addition, the surface geologic map reveals regions that are susceptible to liquefaction. This paper discusses the methods used to construct the surface geology map, subsurface structure contour maps, isopach maps, and 3D geologic model used in the seismic hazard and liquefaction hazard analyses of Madison County, Tennessee, and summaries of the results of these analyses are presented.