Map Of Area Research Articles

Forecasting Dropout Pulmonary TB Control Through the Swot Program and Mapping of at-Risk Areas in Batang District

Forecasting Dropout Pulmonary TB Control Through the Swot Program and Mapping of at-Risk Areas in Batang District

Machine learning approach to burned area mapping for Southern California

ABSTRACT Accurate representation of the location and amount of burned areas is vital to the understanding of patterns and impacts of fires. Some extant burned area maps appear to have high commission errors, which lead to an overrepresentation of burned area. The primary research objective of this study was to assess whether region-specific training data used for machine learning routines improve accuracy of burned area products for western San Diego County. We used training data derived from fine-scale aerial orthoimagery to create and compare three training sets, each with a different Landsat scale sub-pixel burn threshold: 20%, 50%, or 80%. Meaning either 20%, 50%, or 80% of a 30 m × 30 m pixel had to burn for the entire pixel to be classified as burned. High-resolution orthoimagery was used for the creation of the training/testing data as well as determining which sub-pixel threshold leads to more accurate burned area representation. These training data were input into a gradient-boosted regression model. We compared the burned area product from the region-specific gradient boosted model (L-GBRM) to the three products: Monitoring Trends in Burn Severity, Fire and Resource Assessment Program, and the Landsat Burned Area product. We found >20% sub-pixel burn threshold of a Landsat pixel yielded the most accurate classification results. We used a 50% sub-pixel burn threshold for the reference data to compare the results to since the burned area associated with it is closely aligned with the high-resolution orthoimagery determined burn area. The L-GBRM was the most accurate product while also mapping the smallest area burned, suggesting that the extant products have relatively high commission errors. Using region-specific training data achieved a higher accuracy than nationwide training data. Looking at sub-pixel burn thresholds for creating a burned area map could prove to make a more accurate map in terms of area burned represented.

A GIS-based suitability analysis for siting a waste-to-energy power plant in Kaduna, Nigeria

The success of any waste management system requires accurate and up-to-date data on quantity, and composition of waste. Waste generation data from households are needed to address issues relating to municipal solid wastes such as management methods, performance improvement, policy, regional and local planning, administration, cost accounting, design and operation of landfill facilities, and environmental quality. This study carried out a suitability analysis for siting a waste-to-energy power plant in Kaduna using GIS and remote sensing techniques with the specific objectives of identifying existing waste dumpsite in the study area, identifying other factors needed for waste-to-energy plant in the area, and identifying the most suitability site of waste-to-energy in Kaduna. The slop map of the study area was created using the Shuttle Radar Topographic Mission (SRTM). In the ArcGIS environment, Euclidean distance analysis was performed on institutions, rivers, dumpsites, roads, markets, transmission lines, and electricity substations. The waste-to-energy power plant suitability map was created using a multi-criteria technique by overlaying the datasets. The waste-to-energy-power-plant suitability map was made by combining the criteria. Dogon Dawa, Sabon Birinin Jaji, Ganga, Buruku, chikun, Dokwa, ikara, and Kuban are the best sites. The results of the site suitability assessment further revealed that the Makafia local government, located in the city's northern outskirts, was the most suitable area for locating the waste-to-energy power plant, covering 73686 hectares, or 26% of the total area. In addition, the map lays the groundwork for decision-makers to locate a waste-to-energy generating plant. This study shows how decision-making about solid waste management may benefit from the use of geospatial technology. Additionally, it is advised that before approving the placement of a waste-to-energy power plant, decision-makers in the study region refer to it as a reference.

Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change

Landslides pose a noteworthy threat in urban settlements globally, especially in areas experiencing extreme climate and rapid engineering. However, researches focusing on the long-term uninterrupted land use and land cover change (LULCC) impacted on landslide susceptibility mapping (LSM) in rapid urban expansion areas remains limited, let alone different temporal scenarios adjacency thresholds. This work aims to refine the temporal LSM considering spatiotemporal land use and land cover (LULC) and to provide decision makers with governing factors in landslides control during urbanization in mountainous areas. Herein, annual LULC data and landslide inventory spanning from 1992 to 2022 were utilized to map dynamic landslide susceptibility in Wanzhou District of the Three Gorges Reservoir Area, China. Initially, the landslide-related factors were filtered as input features of random forest (RF) model before diagnosis via multicollinearity test and Pearson Correlation Coefficient (PCC). The advanced patch-generating land use simulation (PLUS) model was then invited to fuel temporal susceptibility prediction powered by LULCC projections. Finally, the performance of various scenarios was evaluated using Receiver Characteristic Curve (ROC) curves and Shapley Additive exPlanation (SHAP) technique, with discussions on LULCC temporal adjacency thresholds and mutual feedback mechanism between territorial exploitation and landslide occurrences. The results indicate that the precision of LSM is positively correlated with the time horizon, acted by incorporating the latest LULC and LULCC achieving an area under the curve (AUC) of 0.920. The transition of land from forest to cropland and impervious areas should be avoided to minimize the increase in landslide susceptibility. Moreover, a one-year adjacency threshold of LULCC is recommended for optimal model accuracy in future LSM. This dynamic LSM framework can serve as a reference for decision makers in future landslide susceptibility mitigation and land resources utilization in rapid urban expansion areas worldwide.

Advancements in mapping areas suitable for wetland habitats across the conterminous United States

Wetland habitats provide critical ecosystem services to the surrounding landscape, including nutrient and pollutant retention, flood mitigation, and carbon storage. Wetland connectivity to water bodies and related ecosystems is critical in habitat sustainability, but there are limited resources for landscape-level wetland planning. Considering the network connectivity of an ecosystem type can derive different benefits to the natural and built environment, as well as human health. The value that wetlands provide, along with incentive programs and conservation goals mandated by the government require new and improved wetland spatial data. Utilizing high quality, publicly available data, this study finds that the amount of land in the United States that could support built or restored wetlands is more than double the area of mapped existing wetlands.This study uses 17 input variables (i.e., features extracted from remotely sensed data and auxiliary datasets) at the 10-m resolution and the National Wetlands Inventory to train a random forest model to identify areas that may support a wetland habitat, or potential wetland areas. Models were calculated for each of 18 two-digit hydrologic units that encompass the conterminous United States, and model overall accuracy ranged from 78.0 % to 89.8 %. The models predicted that 21.1 % of the conterminous United States can be categorized as potential wetland area.Selecting input variables to predict areas with wetland potential, rather than to identify existing wetlands, using the random forest algorithm can be transferred to other locations, scales, and ecosystem types. Visualizing potential wetland areas using input data at the 10-m resolution and enhanced methodology improves previous work, as even slight changes in topography, soils, and landscape features can determine ecosystem connections. This product can be used to better place wetland restoration projects to serve ecosystem- and community-wide health by ensuring ecosystem success and targeting areas that face increased climate change impacts.

Predicting seawater intrusion in coastal areas using machine learning: A case study of arid coastal aquifers, Saudi Arabia

Groundwater is a vital resource for communities in arid regions, such as Saudi Arabia, where seawater intrusion poses a significant threat. To determine groundwater quality with traditional methods is time-consuming and costly process. In this study, we developed a machine learning method as a tool for predicting seawater intrusion in coastal areas using groundwater chemical parameters. We analysed 99 water sample data from the Red Sea and 42 from eastern coast of Saudi Arabia for 13 chemical parameters and conducted two experiments with and without chloride parameter. Unlike previous studies that developed complex machine learning methods, we focus on lightweight machine learning algorithms to model seawater intrusion for possibility of sensor deployment in future. The machine learning algorithms assessed and compared are namely decision trees, ensemble machine learning methods (Random Forest and Gradient Boosting), support vector machines, multilayer perceptrons, and ridge regression. With the chloride parameter included, ridge regression emerged as the best model, with an R2 of 99.8% and the lowest Akaike and Bayesian information criteria values. Without chloride data, multilayer perceptron achieved a marginally higher R2 of 98.7% but required longer training times. Notably, a high accuracy was maintained without chloride parameter, demonstrating the capacity of machine learning for augment monitoring. This research underscores the effectiveness of ridge regression and neural networks in predicting seawater intrusion, enabling the accurate mapping of vulnerable areas to inform adaptive management strategies for groundwater preservation. The regional scope of this study offers valuable insights for other arid coastal regions that rely on groundwater and threatened by seawater intrusion.

A map of collective pedagogical content knowledge as a basis for studying the development of biology teachers’ personal PCK of evolution

ABSTRACT In the past few decades, a large body of research has been conducted on pedagogical content knowledge (PCK), but further empirical and theoretical studies are needed to enhance our understanding of PCK development. This study aimed to describe the development of PCK of evolution in a group of biology teachers using a modified version of the refined consensus model (Mixed Model), including the five components of PCK from the model elaborated by Magnusson et al. (1999). The qualitative research design involved multiple study cases, including semi-structured interviews and lesson plan meetings. A collective PCK (cPCK) map was generated via content representation interviews with six expert biology teachers and scholars. The ideas expressed by three or more experts were then included in the cPCK map, which was used to assess the personal PCK (pPCK) development of a different group of seven biology schoolteachers using three traits: map area, map shape, and the identity of the concepts included in the map. The findings show that the development of teachers’ pPCK was mainly due to the increase in the map area resulting from the inclusion of new concepts belonging to knowledge of students learning and to the knowledge associated with strategies.

Remote Sensing for Mapping Natura 2000 Habitats in the Brière Marshes: Setting Up a Long-Term Monitoring Strategy to Understand Changes

On a global scale, wetlands are suffering from a steady decline in surface area and environmental quality. Protecting them is essential and requires a careful spatialisation of their natural habitats. Traditionally, in our study area, species discrimination for floristic mapping has been achieved through on-site field inventories, but this approach is very time-consuming in these difficult-to-access environments. Usually, the resulting maps are also not spatially exhaustive and are not frequently updated. In this paper, we propose to establish a complete map of the study area using remote sensors and set up a long-term and regular observatory of environmental changes to monitor the evolution of a major French wetland. This methodology combines three dataset acquisition technologies, airborne hyperspectral and WorldView-3 multispectral images, supplemented by LiDAR images, which we compared to evaluate the difference in performances. To do so, we applied the Random Forest supervised classification methods using ground reference areas and compared the out-of-bag score (OOB score) as well as the matrix of confusion resulting from each dataset. Thirteen habitats were discriminated at level 4 of the European Nature Information System (EUNIS) typology, at a spatial resolution of around 1.2 m. We first show that a multispectral image with 19 variables produces results which are almost as good as those produced by a hyperspectral image with 58 variables. The experiment with different features also demonstrates that the use of four bands derived from LiDAR datasets can improve the quality of the classification. Invasive alien species Ludwigia grandiflora and Crassula helmsii were also detected without error which is very interesting when applied to these endangered environments. Therefore, since WV-3 images provide very good results and are easier to acquire than airborne hyperspectral data, we propose to use them going forward for the regular observation of the Brière marshes habitat we initiated.

A Combination of Remote Sensing Datasets for Coastal Marine Habitat Mapping Using Random Forest Algorithm in Pistolet Bay, Canada

Marine ecosystems serve as vital indicators of biodiversity, providing habitats for diverse flora and fauna. Canada’s extensive coastal regions encompass a rich range of marine habitats, necessitating accurate mapping techniques utilizing advanced technologies, such as remote sensing (RS). This study focused on a study area in Pistolet Bay in Newfoundland and Labrador (NL), Canada, with an area of approximately 170 km2 and depths varying between 0 and −28 m. Considering the relatively large coverage and shallow depths of water of the study area, it was decided to use airborne bathymetric Light Detection and Ranging (LiDAR) data, which used green laser pulses, to map the marine habitats in this region. Along with this LiDAR data, Remotely Operated Vehicle (ROV) footage, high-resolution multispectral drone imagery, true color Google Earth (GE) imagery, and shoreline survey data were also collected. These datasets were preprocessed and categorized into five classes of Eelgrass, Rockweed, Kelp, Other vegetation, and Non-Vegetation. A marine habitat map of the study area was generated using the features extracted from LiDAR data, such as intensity, depth, slope, and canopy height, using an object-based Random Forest (RF) algorithm. Despite multiple challenges, the resulting habitat map exhibited a commendable classification accuracy of 89%. This underscores the efficacy of the developed Artificial Intelligence (AI) model for future marine habitat mapping endeavors across the country.

Carrying Capacity Assessment of the Aik Nyet Natural Tourism Area in Buwun Sejati Village, West Lombok Regency

The increase in the number of tourists in 2023, based on data from KPH Rinjani Barat, is 36,788 visits per year. Large and continuous tourist visits to a certain area can have negative impacts on the environment and the visitors themselves. This study aims to assess the carrying capacity of the Aik Nyet natural tourist area to ensure it can support ongoing visitor activities. The tools used in this study include writing materials, GPS, questionnaires, raffia ropes, and tally sheets. The materials used in this research include area maps, bio-physical conditions, visitor information, and vegetation data in the Aik Nyet tourist area. Both qualitative and quantitative methods were employed in this study. The findings show that the physical carrying capacity (PCC) is 25,232 people. The real carrying capacity (RCC) can accommodate 24,663 people, and the effective carrying capacity (ECC) can support 9,865 people. Monitoring of the water quality in the area is recommended.

A Robust AR-DSNet Tracking Registration Method in Complex Scenarios

A robust AR-DSNet (Augmented Reality method based on DSST and SiamFC networks) tracking registration method in complex scenarios is proposed to improve the ability of AR (Augmented Reality) tracking registration to distinguish target foreground and semantic interference background, and to address the issue of registration failure caused by similar target drift when obtaining scale information based on predicted target positions. Firstly, the pre-trained network in SiamFC (Siamese Fully-Convolutional) is utilized to obtain the response map of a larger search area and set a threshold to filter out the initial possible positions of the target; Then, combining the advantage of the DSST (Discriminative Scale Space Tracking) filter tracker to update the template online, a new scale filter is trained after collecting multi-scale images at the initial possible position of target to reason the target scale change. And linear interpolation is used to update the correlation coefficient to determine the final position of target tracking based on the difference between two frames. Finally, ORB (Oriented FAST and Rotated BRIEF) feature detection and matching are performed on the accurate target position image, and the registration matrix is calculated through matching relationships to overlay the virtual model onto the real scene, achieving enhancement of the real world. Simulation experiments show that in complex scenarios such as similar interference, target occlusion, and local deformation, the proposed AR-DSNet method can complete the registration of the target in AR 3D tracking, ensuring real-time performance while improving the robustness of the AR tracking registration algorithm.

Using Biosensors to Detect and Map Language Areas in the Brain for Individuals with Traumatic Brain Injury.

The application of biosensors in neurolinguistics has significantly advanced the detection and mapping of language areas in the brain, particularly for individuals with brain trauma. This study explores the role of biosensors in this domain and proposes a conceptual model to guide their use in research and clinical practice. The researchers explored the integration of biosensors in language and brain function studies, identified trends in research, and developed a conceptual model based on cluster and thematic analyses. Using a mixed-methods approach, we conducted cluster and thematic analyses on data curated from Web of Science, Scopus, and SciSpace, encompassing 392 articles. This dual analysis facilitated the identification of research trends and thematic insights within the field. The cluster analysis highlighted Functional Magnetic Resonance Imaging (fMRI) dominance and the importance of neuroplasticity in language recovery. Biosensors such as the Magnes 2500 watt-hour (WH) neuromagnetometer and microwire-based sensors are reliable for real-time monitoring, despite methodological challenges. The proposed model synthesizes these findings, emphasizing biosensors' potential in preoperative assessments and therapeutic customization. Biosensors are vital for non-invasive, precise mapping of language areas, with fMRI and repetitive Transcranial Magnetic Stimulation (rTMS) playing pivotal roles. The conceptual model serves as a strategic framework for employing biosensors and improving neurolinguistic interventions. This research may enhance surgical planning, optimize recovery therapies, and encourage technological advancements in biosensor precision and application protocols.

Combining environmental-socio-economic data with volunteer geographic information for mapping flood risk zones in Zhengzhou, Henan Province, China

Due to the occurrence of extreme weather phenomena, flooding caused by precipitation has become a frequent, widespread, and highly destructive natural disaster. For densely populated urban areas, efficient identification of people's emergency rescue needs and mapping of high flood risk areas are particularly important for decision makers to formulate disaster relief strategies. In this study, we employed a machine learning approach along with volunteer geographic information (VGI)1 data and environmental-socio-economic indicators to map the flood risk area in Zhengzhou. Firstly, the sentiment analysis tool based on artificial intelligence platform initially identified the public's emergency rescue needs. Secondly, the crowd-sourcing approach and geographical approach were used to evaluate the VGI data quality and the variable importance measures (VIMs)2 method to optimize the evaluation indicators. Lastly, the flood risk area was classified in four categories: flood environmental susceptibility, flood-induced risk, flood exposure risk, and flood mitigation capacity, subsequently resulting in a comprehensive risk map of Zhengzhou. The results showed that the accuracy of flood risk assessment increased from 85.7 % to 92.4 % after VGI quality evaluation. The most important indicators affecting flood risk assessment were population density, river system and nighttime light intensity. The comprehensive flood risk map better reflected the spatial distribution of high-risk areas in Zhengzhou. It allows emergency responders to quickly identify areas that require urgent attention.

SiamDCFF: Dynamic Cascade Feature Fusion for Vision Tracking.

Establishing an accurate and robust feature fusion mechanism is key to enhancing the tracking performance of single-object trackers based on a Siamese network. However, the output features of the depth-wise cross-correlation feature fusion module in fully convolutional trackers based on Siamese networks cannot establish global dependencies on the feature maps of a search area. This paper proposes a dynamic cascade feature fusion (DCFF) module by introducing a local feature guidance (LFG) module and dynamic attention modules (DAMs) after the depth-wise cross-correlation module to enhance the global dependency modeling capability during the feature fusion process. In this paper, a set of verification experiments is designed to investigate whether establishing global dependencies for the features output by the depth-wise cross-correlation operation can significantly improve the performance of fully convolutional trackers based on a Siamese network, providing experimental support for rational design of the structure of a dynamic cascade feature fusion module. Secondly, we integrate the dynamic cascade feature fusion module into the tracking framework based on a Siamese network, propose SiamDCFF, and evaluate it using public datasets. Compared with the baseline model, SiamDCFF demonstrated significant improvements.

Multi-spectral remote sensing bathymetry based on the variation of substrate spectra and empirical linear relationship

The high spatial resolution and wide applicability of multi-spectral remote sensing make it significant for estimating shallow water depth. In areas with mixed substrates, determining the contribution of substrate reflection to the sensor’s received signal can effectively enhance the precision of multi-spectral remote sensing bathymetry. Therefore, this paper proposes a Two Factors constrained Quasi-Analytical Algorithm (TF-QAA). Firstly, the algorithm considers the spectral variation of the mixed substrates. Secondly, a robust correlation between inherent optical parameters and water depth is established in this study, which is used to enforce a linear relationship constraint on the initially estimated water depth. Sentinel-2 image data was used to verify the algorithm in the Antelope Reef area of the South China Sea. The best result from images taken at different times indicates that the TF-QAA method has the average absolute error (MAE) of 0.92 m, the root mean square error (RMSE) of 1.20 m, the average relative error (MRE) of 10.6%, and the correlation coefficient (R2) of 0.97. The results suggest that the algorithm is effective for Case 1 water, providing a novel approach for extensive bathymetric mapping in shallow water areas of islands, with or without LiDAR-measured depth data.

Enhancement of Comparative Assessment Approaches for Synthetic Aperture Radar (SAR) Vegetation Indices for Crop Monitoring and Identification—Khabarovsk Territory (Russia) Case Study

Crop identification at the field level using remote sensing data is a very important task. However, the use of multispectral data for the construction of vegetation indices is sometimes impossible or limited. For such situations, solutions based on the use of time series of synthetic aperture radar (SAR) indices are promising, eliminating the problems associated with cloudiness and providing an assessment of crop development characteristics during the growing season. We evaluated the use of time series of synthetic aperture radar (SAR) indices to characterize crop development during the growing season. The use of SAR imagery for crop identification addresses issues related to cloudiness. Therefore, it is important to choose the SAR index that is the most stable and has the lowest spatial variability throughout the growing season while being comparable to the normalized difference vegetation index (NDVI). The presented work is devoted to the study of these issues. In this study, the spatial variabilities of different SAR indices time series were compared for a single region for the first time to identify the most stable index for use in precision agriculture, including the in-field heterogeneity of crop sites, crop rotation control, mapping, and other tasks in various agricultural areas. Seventeen Sentinel-1B images of the southern part of the Khabarovsk Territory in the Russian Far East at a spatial resolution of 20 m and temporal resolution of 12 days for the period between 14 April 2021 and 1 November 2021 were obtained and processed to generate vertical–horizontal/vertical–vertical polarization (VH/VV), radar vegetation index (RVI), and dual polarimetric radar vegetation index (DpRVI) time series. NDVI time series were constructed from multispectral Sentinel-2 images using a cloud cover mask. The characteristics of time series maximums were calculated for different types of crops: soybean, oat, buckwheat, and timothy grass. The DpRVI index exhibited the highest stability, with coefficients of variation of the time series that were significantly lower than those for RVI and VH/VV. The main characteristics of the SAR and NDVI time series—the maximum values, the dates of the maximum values, and the variability of these indices—were compared. The variabilities of the maximum values and dates of maximum values for DpRVI were lower than for RVI and VH/VV, whereas the variabilities of the maximum values and the dates of maximum values were comparable for DpRVI and NDVI. On the basis of the DpRVI index, classifications were carried out using seven machine learning methods (fine tree, quadratic discriminant, Gaussian naïve Bayes, fine k nearest neighbors or KNN, random under-sampling boosting or RUSBoost, random forest, and support vector machine) for experimental sites covering a total area of 1009.8 ha. The quadratic discriminant method yielded the best results, with a pixel classification accuracy of approximately 82% and a kappa value of 0.67. Overall, 90% of soybean, 74.1% of oat, 68.9% of buckwheat, and 57.6% of timothy grass pixels were correctly classified. At the field level, 94% of the fields included in the test dataset were correctly classified. The paper results show that the DpRVI can be used in cases where the NDVI is limited, allowing for the monitoring of phenological development and crop mapping. The research results can be used in the south of Khabarovsk Territory and in neighboring territories.

Harnessing photocatalytic activity of mesoporous graphitic carbon nitride decorated by copper single-atom catalysts for oxidative dehydrogenation of N-heterocycles

This work describes the application of Cu single-atom catalysts (SACs) for photocatalytic oxidative dehydrogenation of N-heterocyclic amines to the respective N-heteroaromatics through environmentally benign and sustainable pathways. The mesoporous graphitic carbon nitride (mpg-C3N4), prepared by the one-step pyrolysis method, possesses a lightweight material with a high surface area (95 m2 g−1) and an average pore diameter (3.6 nm). A simple microwave-assisted preparation method was employed to decorate Cu single-atom over mpg-C3N4 support. The Cu single-atom decorated on mpg-C3N4 support (Cu@mpg-C3N4) is characterized by various characterization techniques, including XRD, UV–visible spectrophotometry, HRTEM, HAADF-STEM with elemental mapping, AC-STEM, ICP-OES, XANES, EXAFS, and BET surface area. These characterization studies confirmed that the Cu@mpg-C3N4 catalyst exhibited high surface area, mesoporous nature, medium band gap, and low metal loading. The as-synthesized and well-characterized Cu@mpg-C3N4 single-atom photocatalyst is then evaluated for its efficacy in converting N-heterocycles into corresponding N-heteroaromatic compounds with excellent conversion and selectivity (>99 %). This transformation is achieved using water as a green solvent and a 30 W white light as a visible light source, demonstrating the catalyst's potential for sustainable and environmentally benign reactions.

Web-Based Mapping of Crime-Prone Areas in Samarinda Seberang and Loa Janan Ilir Districts, Samarinda Citys

Web-Based Mapping of Crime-Prone Areas in Samarinda Seberang and Loa Janan Ilir Districts, Samarinda Citys

Management of Coastline Variability in an Endangered Island Environment: The Case of Noirmoutier Island (France)

This article presents a diachronic study of evolution along the coastline of Noirmoutier Island in France, a sandy shore particularly susceptible to erosion and submersion risks, which are exacerbated by climate change due to two-thirds of its territory being below sea level. The study is based on an analysis of aerial images covering a period of 72 years, divided into five distinct periods: 1950–1974, 1974–1992, 1992–2000, 2000–2010, and 2010–2022. The methodology used combines two complementary approaches: the Digital Shoreline Analysis System (DSAS) for taking linear measurements of the erosion and accretion that have taken place along various shorelines, and the surface method to evaluate the amount of surface lost or gained between different shorelines while calculating the uncertainties associated with the obtained results. The overall trend observed between 1950 and 2022 indicates that the Noirmoutier coastline studied has gained surface area (81 hectares) at an average rate of +0.57 ± 0.06 m per year. The article then presents an application of the method developed by Durand and Heurtefeux in 2006 to estimate the future position of the shoreline. A map of the local area is also provided, identifying the areas susceptible to coastal erosion by 2052 and by 2122, in accordance with the provisions of the Climate and Resilience Law adopted in France on 22 August 2021. The results reveal that there are many sources of uncertainty in predicting the future evolution of the shoreline using this methodology. Therefore, it is crucial to consider these uncertainties when planning future coastal management actions and adopting appropriate adaptation methods to counteract unforeseen developments.

Construction of compositional geological models using the variational grid method of geomapping and the object-hierarchical approach

The variational grid method of geomapping is based on approximation by bicubic splines, the permissible grid size of which is limited by the amount of computer RAM. The paper describes an approach to solving problems of constructing maps of geological parameters, using this method, when computer resources are insufficient. The approach is based on the sequential mapping for a set of fragments of the entire mapping area. The implementation of the approach is considered for two options. The first, if the data density is sufficiently high, consists of sequential calculations over partially overlapping “bands”. If the data distribution is significantly uneven, the second option is implemented, which consists in calculating a detailed grid for local areas and smoothly pasting it into the general map. Smoothness at the boundaries of bands and insets is ensured through the condition of equality of spline coefficients for coinciding nodes, which is set by adjusting the system of linear algebraic equations when solving the mapping problem. Based on these approaches, a multiscale structural-geological model of the sedimentary cover over the territory of Western Siberia was implemented. The formation of a compositional model is carried out on the basis of an object-hierarchical approach, which ensures consistency in the calculations of all its elements, as well as automation of constructions.