Spatial Interpolation Algorithm Research Articles

Thermal Earth model for the conterminous United States using an interpolative physics-informed graph neural network

This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop a thermal Earth model for the conterminous United States. The model was trained to approximately satisfy Fourier’s Law of conductive heat transfer by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other spatial and physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, electrical conductivity, and proximity to faults and volcanoes. With a spatial resolution of 18km2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$18 \\ km^2$$\\end{document} per grid cell, we predicted heat flow at surface as well as temperature and rock thermal conductivity across depths of 0-7km\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0-7 \\ km$$\\end{document} at an interval of 1km\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1 \\ km$$\\end{document}. Our model showed temperature, surface heat flow and thermal conductivity mean absolute errors of 6.4∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6.4^\\circ C$$\\end{document}, 6.9mW/m2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6.9 \\ mW/m^2$$\\end{document} and 0.04W/m-K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.04 \\ W/m-K$$\\end{document}, respectively. This thorough modeling of the Earth’s thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources. Our thermal Earth model is available as web application at https://stm.stanford.edu, feature layers on ArcGIS at https://arcg.is/nLzzT0, and tabulated data on the Geothermal Data Repository at https://gdr.openei.org/submissions/1592.

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory.

The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.

Ocean sound speed field reconstruction with graph regularized deep matrix factorization

To reconstruct the ocean sound speed field (SSF), a matrix in horizontal could be formed by gridding the sea area, where some sparse entries were filled by the observation sound speed profiles (SSPs). The reconstruction of the SSF can be modeled as a matrix completion problem, using limited and noisy observation values to reconstruct the entire matrix. Furthermore, the spatial correlation of the SSF could be modeled by graph space model, utilizing with the low-rank property of the SSF matrix, the problem of graph regularization low-rank matrix completion was developed. The observed average SSPs in six cross section were estimated from the four underwater acoustic tomography moorings, and nine local SSPs were obtained from pressure inverted echo sounders (PIESs) inversion. By combining the method of deep matrix factorization and the graph regularization, the Graph Regularized Deep Matrix Factorization (GRDMF) method was proposed. The simulation results showed that the GRDMF performs better than the spatial interpolation algorithm in SSF reconstruction. Considering the application scenarios of deep-sea acoustic tomography, an average constraint (AC) item was designed additionally, and then a customized algorithm named GRDMF-AC is proposed. Finally, data of the experiment at the South China Sea in 2021 is processed to verify GRDMF-AC, the results showed that the reconstructed SSF captures mesoscale eddy in the experiment.

DEVELOPMENT OF METHOD TO OPTIMIZE RAIN GAUGE STATION LOCATION IN DA RANG RIVER BASIN USING SPATIAL INTERPOLATION TECHNIQUE

Rainfall is one of the crucial data types in meteorological research. In the context of increasingly complex climate change, meteorological forecast data is essential for modeling and predicting natural disasters, especially floods and landslides. To ensure the accuracy of rainfall prediction and the cost of building and operating rain gauge stations, determining the number of stations is crucial. Therefore, this study developed an optimization design for the rain gauge station network in the Da Rang River focusing on the quantity and spatial distribution of rain gauge stations. The geostatistical method combined with spatial interpolation algorithms was applied using rainfall data from 2015-2020. The choice of an appropriate spatial interpolation method influenced the accuracy of the optimization process. The spatial interpolation analysis showed the spatial variation of rainfall types across the entire study area by season, as well as the geographical distribution of rainfall. After determining the optimal number of stations, an approach based on geographic information systems produced a spatial distribution map of the optimal rain gauge station locations. The results indicated that an additional 14 rain gauge stations should be installed, randomly located within the rainy area with a distance of 10 km between stations. These additional stations, along with the existing ones in the area, will provide comprehensive information about rainfall, enabling accurate predictions of rainfall distribution in the research area.

SOC Algorithm-Based Framework for Spectrum Occupancy Completion

This paper investigates the completion of continuous spectrum occupancy in space-frequency domain. Most of the existing studies adopt the traditional scheme, namely field strength completion followed by feature extraction. However, the existence of non-ideal sampling data challenges the recovery of spectrum occupancy array in accuracy. In this paper, we first develop a spectrum occupancy completion framework that implements feature extraction and spectrum occupancy completion sequentially, which reduces the impact of non-ideal data on completion. Then, to further improve the completion accuracy, a semantic-optional-based completion (SOC) algorithm is proposed, which divides the continuous spectrum occupancy data into discrete and multi-class spectrum semantic data according to the spectrum occupancy level. Specifically, the completion of spectrum occupancy is realized by specific semantic extraction, spectrum semantic completion and spectrum semantic recovery. Further, the real-world measured data are simulated to demonstrate the effectiveness of our proposed framework and SOC. The experimental results show that, compared with the low-rank completion algorithm and spatial interpolation algorithm, the proposed SOC has higher completion accuracy at more than 93% frequency points.

Accurate Anomaly Detection With Energy Efficiency in IoT–Edge–Cloud Collaborative Networks

With the applicability of edge intelligence in various domains, anomaly detection, which aims to identify unusual and infrequent circumstances, is regarded as a regularly performed task to guarantee the health of the Internet of Things (IoT) applications. Generally, sensory data are gathered at the network edge and completely transmitted to the cloud, where computational-heavy algorithms are mostly adopted to determine the locations of anomaly. Considering the occurrence infrequency of anomalies, this strategy may transmit relatively huge-volume of sensory data, which may reflect a healthy situation indeed, to the cloud. To mitigate this problem, this paper proposes an accurate anomaly detection mechanism with energy efficiency in three-Tier IoT-Edge-Cloud collaborative networks. Specifically, after gathering sensory data provided by IoT nodes in certain edge networks, the edge node applies the Marching Squares algorithm to generate isopleths, where an isopleth may capture the boundary of anomaly. A sensory data filtering mechanism is conducted at the edge tier, such that anomaly-relevant sensory data are transmitted to the cloud, and thus, the network traffic is decreased significantly. Thereafter, the boundary of anomaly is obtained, and the locations of candidate boundary nodes are determined by adopting the Kriging spatial interpolation algorithm at the cloud tier. These locations are traversed by mobile sensing nodes at edge networks, and their sensory data are gathered for boundary refinement. Extensive experiments are conducted on an air quality hazardous gas dataset from Towards Data Science, and evaluation results show that our technique outperforms the state-of-the-art counterparts in boundary accuracy and energy consumption.

IMPROVING THE ACCURACY OF SATELLITE-BASED NEAR SURFACE AIR TEMPERATURE AND PRECIPITATION PRODUCTS

Abstract. In this study, we evaluate the performance of several reanalyses and satellite-based products of near-surface air temperature and precipitation to determine the best product in estimating daily and monthly variables across the complex terrain of Turkey. Each product’s performance was evaluated using 1120 ground-based gauge stations from 2015 to 2019, covering a range of complex topography with different climate classes according to the Köppen-Geiger classification scheme and land surface types according to the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, various traditional and more advanced machine learning downscaling algorithms were applied to improve the spatial resolution of the products. We used distance-based interpolation, classical Random Forest, and more innovative Random Forest Spatial Interpolation (RFSI) algorithms. We also investigated several satellite-based covariates as a proxy to downscale the precipitation and near-surface air temperature, including MODIS Land Surface Temperature, Vegetation Index (NDVI and EVI), Cloud Properties (Cloud Optical Properties, Cloud Effective Radius, Cloud Water Path), and topography-related features. The agreement between the ground observations and the different products, as well as the downscaled temperature products, was examined using a range of commonly employed measures. The results showed that AgERA5 was the best-performing product for air temperature estimation, while MSWEP V2.2 was superior for precipitation estimation. Spatial downscaling using bicubic interpolation improved air temperature product performance, and the Random Forest (RF) machine learning algorithm outperformed all other methods in certain seasons. The study suggests that combining ground-based measurements, precipitation products, and features related to topography can substantially improve the representation of spatiotemporal precipitation distribution in data-scarce regions.

Impact of Different Irrigation Methods on the Main Chemical Characteristics of Typical Mediterranean Fluvisols in Portugal

The sustainable management of Mediterranean agricultural soils, characterized by salinization and low organic matter content, requires a thorough understanding of their temporal and spatial evolution. The focal point of this investigation encompasses an area of 6769 ha within the Portuguese Mediterranean basin, from which as many as 686 topsoil specimens were acquired during the periods 2001/2002 and 2011/2012 for the purpose of scrutinizing soil organic matter (SOM) content, pH measured in water, and electrical conductivity (EC). The methodology employed both classical and geostatistical techniques, and the terrestrial samples were classified in accordance with the irrigation mechanisms in use (namely, drip and sprinkler systems), subsequently juxtaposed with their counterparts in rainfed systems. Predictive maps were generated using the Ordinary Kriging algorithm for spatial interpolation. The findings demonstrate that irrigated Fluvisols displayed lower SOM content compared to rainfed soils, with sprinkler-irrigated soils experiencing a 16.1% decrease and drip-irrigated soils showing a more pronounced 26.6% decrease. Moreover, drip-irrigated soils contained 12.5% less SOM compared to sprinkler-irrigated soils. The pH levels stabilized at around 6.6 in both rainfed and irrigated soils, with no significant differences observed between the irrigation methods. Furthermore, irrigated Fluvisols exhibited higher EC values compared to rainfed soils, with both sprinkler and drip-irrigated soils showing values that were 35.2% higher. These results underscore the impact of irrigation practices on soil properties, including elevated EC values due to increased soil salt accumulation. The study highlights the necessity of considering specific irrigation systems and associated practices to ensure sustainable soil health and productivity. Adopting management approaches that account for these factors is crucial for preserving optimal soil conditions in Mediterranean agricultural systems.

Image-Like Situational Difference Mapping (ILSDM)-Based Method for Detecting Anomalies in Electromagnetic Environment

Electromagnetic environment anomaly radiation source detection (EMEARD) can provide a basis for electromagnetic (EM) equipment behavior cognition and battlefield threat assessment. Existing methods mainly focus on generating EM space situations but ignore the research on anomaly signal detection. In this paper, the connections between information geometry and performance of sensor networks for anomaly detection are explored. Nonlinear estimation methods based on information geometry can be directly used for anomaly detection. However, Riemann distance as a metric function faces the disadvantages of extensive computation and high complexity. To tackle this issue here, we propose a method based on image-like situation difference mapping, namely ILSDM. which takes the data-driven strategy to obtain the analytic expression of situation difference mapping distance function (DMDF), and anomaly detection is equivalent to the mapping reconstruction of information geometry. Firstly, the EM space situation is acquired through the distributed sensor network. In addition, to solve the perceptual blind area problem, the sensors’ data is estimated by spatial autocovariance optimal interpolation algorithm (Kriging). Secondly, we apply the Gaussian mixture model (GMM) to fit the statistical features of each sensing node into probability density function (PDF), and adjacent sensors with similar PDF are fused by the neighbor average fusion method (NAF). Then, based on the information geometry, the fused PDFs are mapped to the Riemannian manifold space as a series of coordinates, and we further propose to use image-like situation (ILS) to represented the EM space situation. Finally, DMDF measures the information distance between different ILSs to achieve anomaly detection. Numerical simulations and real experiments were conducted to prove the superiority and feasibility of our method for anomaly detection in EM space.

Prediction of distortion in a thin-plate structure with intermittent fillet joints using a local-to-global approach

Fillet welds by intermittent welding are widely used in the welding of centrifugal fans, such as attaching a fan blade to the rear plate. The intermittent welding method transforms the full-length continuous weld into multiple short welds to minimize distortion by reducing the total heat input for a welded joint to control welding distortion. An infinite number of short welds are created during this process. However, depending on the complexity of the model, the computational cost of predicting welding distortion based on full-scale modeling is considerable. These numerous short welds undoubtedly complicate modeling and computations, limiting rapid distortion prediction for large and complex structures. This study develops an equivalent deformation calculation approach based on a local-to-global technique and applies it to predict distortion of a fillet joint welded specimen. The approach consists of two steps. First, the welding-induced structural deformation is calculated for a two-dimensional (2D) weld and a 2D heat-affected zone (local calculation). Second, the 2D structural deformation is applied to the full-scale welded structure using a nearest-point spatial interpolation algorithm to acquire the three-dimensional (3D) equivalent deformation (global calculation). Note that the weld shape was considered in both local and global calculations. In addition, welding tests were performed, and the coordinates of the key points were measured. The geometric parameters of the welds used in the numerical simulations were obtained from experimental observations. Finally, the key point coordinates obtained by the proposed approach were compared with experimental measurements. Furthermore, the distribution of out-of-plane distortion caused by the new approach was compared with the simulation results of the thermo-elastic-plastic method performed on a full-scale welded structure. The proposed approach can be verified by both comparisons.

EVALUATION OF ACCURACY OF DIGITAL TERRAIN MODELING METHODS BASED ON SPATIAL INTERPOLATION (Part 2)

Digital terrain model (DTM) is an integral part of many geodetic works. It allows accurately reproducing the earth's surface in a digital form with varying levels of detail. One of the most significant problems in creating a DTM is the accuracy of reflecting the relief. In the first part of the work, an analysis of methods that can more detailed reflect the relief was already conducted. For these purposes, the Surfer geoinformation package was selected, which offers a considerable number of methods based on the spatial interpolation algorithm. Although these methods have represented the same area with high detail, there is still a need to assess the accuracy of each method. The purpose of this article is to assess the accuracy of methods for constructing a digital terrain model based on spatial interpolation. Since model accuracy is a crucial factor for many geodetic works, it is necessary to determine which methods are the most accurate and meet the requirements of different purposes of use. The accuracy assessment of methods will improve the efficiency of geodetic works and avoid errors and additional costs. In the Surfer program, after constructing the DTM with each method, a report can be obtained containing the input values of points and the values obtained after interpolation with each method. This report is very useful for assessing the accuracy of each method since it allows understanding how well the model reflects the real relief. To assess the accuracy of each method, control points of the spatial variable Z were taken, and the values in these points were interpolated for each method. This assessment makes it possible to obtain a numerical characteristic of accuracy for each method and compare them with each other. Based on the analysis conducted, it can be concluded that problematic areas were identified on a fragment of a topographic map, where there were insufficient input data for accurate interpolation. The result is not accurate and could be better with input data obtained from geodetic instruments and technologies such as GPS, lidar, etc. The "Natural Neighbor" method performed the best in reproducing the digital elevation model on this hilly terrain. The application of the "Kriging," "Triangulation with Linear Interpolation," "Modified Shepard's Method," and "Radial Basic Function" methods provided fairly accurate interpolation. However, the "Minimum Curvature" method was found to be ineffective in hilly terrain as it did not provide accurate interpolation. After conducting an accuracy assessment, it can be concluded that high-quality input data is an extremely important element in achieving more accurate results when creating a digital elevation model. Keywords: digital elevation model, construction methods, spatial interpolation, geographic information system, Surfer.

ANALYSIS OF METHODS FOR DIGITAL TERRAIN MODELING BASED ON SPATIAL INTERPOLATION (Part 1)

Digital Elevation Model (DEM) is an important component of geodetic works, which allows reproducing the earth's surface in digital form with a certain level of detail. Various methods are used to construct DEMs, among which it is worth noting the methods based on spatial interpolation. One advantage of spatial interpolation is the ability to take into account the uneven distribution of points on the earth's surface, which provides a more accurate DEM. With the spread of high-precision geodetic instruments and technologies such as GPS and LiDAR, the methods of constructing DEMs have significantly improved. Modern methods include interpolation of curved surfaces, smoothing methods that reduce noise and random errors, and adaptive filtering methods that can detect and correct data anomalies. In the field of geodesy, digital elevation models are an essential element in performing measurements and creating project documentation for construction. The purpose of this article is to analyze existing methods for constructing digital elevation models and to compare them to choose the best one. For the analysis, the widely known geoinformation system package Surfer was selected, which contains a sufficiently large number of deterministic methods and a geostatistical method based on spatial interpolation. The Golden Software Surfer geoinformation system is currently the industry standard for constructing graphical representations of two-variable functions. An unbeatable advantage of the program is its built-in spatial interpolation algorithms, which allow creating digital surface models with high quality for spatially unevenly distributed data. The Surfer program provides 12 different methods of spatial interpolation. For the analysis, a random fragment of a topographic map was taken, which was previously digitized and exported in ASCII format with ready-made coordinates for constructing a DEM. However, only 6 out of the 12 methods that could more accurately reflect the real relief situation were further analyzed based on these topographic data, namely Kriging, Triangulation with Linear Interpolation, Radial Basic Function, Natural Neighbor, Modified Shepard`s Method and Minimum Curvature. Keywords: digital elevation model, construction methods, spatial interpolation, geographic information system, Surfer.

The relative roles of different land-use types in bike-sharing demand: A machine learning-based multiple interpolation fusion method

Land use plays a crucial role in promoting the bike-sharing demand. Traditionally, studies on bike-sharing demand (BSD) are mainly focused on its prediction through regression methods, but the influence of MAUP (modifiable areal unit problem) in modeling is ignored. This paper aims to model spatial BSD distribution and prove the driving forces of different land use types to BSD through a machine-learning-based multiple interpolation fusion method. The hotspot detection model is employed to establish sample points covering different land use types in urban areas. In order to capture the differences in adaptations among different urban regions and for different data sizes, six machine learning methods are applied and evaluated to improve BSD estimation by fusing five spatial interpolation algorithms, including Inverse Distance Weight, Spline, Kriging, Natural Neighborhood and Trend. The methodological verification of Beijing City shows that the fusion models improve the estimation performance compared with individual interpolation algorithms, and that GRNN (generalized regression neural network) method is superior to all the others. According to fitting results of all POIs based on the GRNN fusion model, we identify which types of facilities correspond to customers that will have a stronger preference for bike-sharing and demonstrate which facility names are more prominent in each land use type. The conclusions presented here enrich our understanding relationships between land-use and BSD, which provide a valuable foundation for the bike-sharing development. Compared with implementing regression in an analysis zone or a square grid, troubles caused by the MAUP are effectively solved through this method.

Application of Multi-Temporal InSAR (MT-InSAR) for structural monitoring: the case study of Scrovegni Chapel in Padova

In the last years, the use of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique for structural health monitoring (SHM) of buildings and infrastructures has spread. Compared to traditional monitoring methods, MT-InSAR technique involves a number of advantages, e.g. perform back-analysis and extend the monitoring to the surrounding area, that makes it a very cost-effective tool. On the other hand, some drawbacks still have to be fully overcome, such as the issues related to the geocoding of the measurement points and the noisiness in the time series. In this article, the application of MT-InSAR monitoring to a case study is presented. The structure of interest is the Scrovegni Chapel in Padova, recently declared a world heritage site by UNESCO as it contains numerous frescos executed by Giotto. Using images acquired by COSMO-SkyMed constellation, the deformation of the area surrounding the Chapel was estimated, by means of spatial interpolation algorithms. In addition, time series referred to measurement points located on the rooftop were analyzed and compared to the environmental data (temperature and humidity) acquired by on-site physical sensors already installed.

Prediction of Rainfall Classification of Java Island with ANN-Feature Expansion and Ordinary Kriging

Precipitation is one of the most important climatic variables in many aspects of our daily lives. High rainfall intensity can cause floods, landslides, and other natural disasters. Therefore, rainfall prediction is important for predicting natural disasters, assisting farmers in production decisions, and crop harvesting. In this research, a system is built to create a rainfall prediction map using a machine learning approach and spatial interpolation algorithms in Java, Indonesia. In the field of weather prediction, the artificial neural network approach is a popular machine learning method. The artificial neural network (ANN) method is a method that has the advantage of studying connections in the previously unknown hidden layer between input data and output data through training procedures. By using the ANN method, historical weather and climate data can be applied to create a classification model and predict rainfall classes. The classification of data is determined based on the attributes of historical weather and climate data, namely temperature, humidity, air pressure, evaporation, sunlight, and the level of rainfall in the time range per day and month. From the results of the ANN modeling, it was found that the 5C month model with an accuracy value of 89% as the best monthly ANN model, and the 6C day model with an accuracy value of 81% as the best daily ANN model. After going through ANN modeling, there is a spatial interpolation algorithm that is often used to estimate rainfall, namely Ordinary Kriging. The Ordinary Kriging approach is used to reduce the estimated variance and estimate the rainfall value in the case study area. After going through Ordinary Kriging modeling, a rainfall prediction map for the next six months and seven days is made based on the coordinates as a result of the research. The results of this research are rainfall prediction maps for the next six months and the next seven days on Java Island.

Digital twin-enabled built environment sensing and monitoring through semantic enrichment of BIM with SensorML

Effective environmental condition monitoring provides constant surveillance of the built environment and reveals deteriorations that could impact the daily operation of facilities, especially amid COVID situations. However, the current Industry Foundation Classes (IFC) data schema for Building Information Modelling (BIM) provided limited support to represent full semantics related to environmental sensing and monitoring. How to semantically enrich the IFC schema with enhanced data description capability for informed decision-making in smart facilities management (FM) amid COVID situations remains an open question. This paper develops a semi-automatic extension and integration of IFC data schema with Sensor Model Language (SensorML) specification in order to support automated built environment sensing and monitoring. Referring to SensorML, an extended IFC model view definition for a comprehensive description of required sensor metadata and sensing entities is presented. An Internet of Things (IoT) sensor network is then established to realise continuous data collection from a variety of wireless sensing devices. The spatial-temporal data captured by the IoT sensor network are extracted by a regular expression-based data distillation algorithm and integrated with the digital twin, in which spatial interpolation algorithms further analyse, compute, and visualise the state of the environment. The proposed method is demonstrated via an experimental study which supports real-time environmental monitoring and delivers more actionable insights to facility managers to sustain the daily operation of buildings. This study contributes new methods and models to semantically enrich the digital twin from the data perspective for environmental condition monitoring during the pandemic time which fosters the development of holistic building facility management.

Tomographic images of tree trunks generated using ultrasound and post-processed images: Influence of the number of measurement points

Tomography is a technique increasingly used in tree inspections. This technique can be performed in two stages: field testing and postimage processing. To feed the tomographic image construction software, it is necessary to adopt a measurement grid composed of points positioned on the perimeter of the stem. The images are generated through spatial interpolation algorithms. From a theoretical point of view, more measurement points taken in the perimeter of the stem result in better interpolation results. However, an increase in the number of mesh points causes a substantial increase in field work and image processing time. The general objective of this study was to verify the influence of the number of measurement points of the diffraction mesh on the ultrasound tomography results. For this purpose, ten simulated discs were used, all 500 mm in diameter and with different defects in terms of size, position, and geometry. In each of the discs, diffraction grids were simulated with 6, 8, 10, 12, and 14 measurement points in the contour. The results showed that a favorable combination of accuracy and a minimization of effort can be achieved with diffraction mesh with number of measurement points calculated as five times the perimeter of the trunk in meters.

Actively heated fiber optics method to monitor grout diffusion range in goaf

The invisibility of grouting has always been one of the most challenging problems related to the research of grouting theory, but it has not been solved. An actively heated fiber optics (AHFO) method is proposed in this paper to realize the visualization of slurry diffusion morphology. The grid installation method of AHFO is designed, and the calculation formula of grid size is deduced. Based on the self-developed spatial positioning algorithm and Kriging spatial interpolation algorithm, the temperature anomaly in the AHFO temperature profile is used to locate and quantify the range of grouting diffusion in goaf. To verify the applicability of this method, a large-scale three-dimensional grouting model test was carried out. The test results show that the grouting diffusion mode is elliptical, consistent with the previous research results. Compared with the theoretical calculation results, the maximum relative error of the monitored grouting diffusion radius is 18 %. Therefore, the monitoring method can monitor the grouting diffusion.

Climate zoning of asphalt pavement based on spatial interpolation and Fuzzy C-Means algorithm

ABSTRACT Natural conditions tend to affect the performance of the asphalt pavement. Climatic factors must be taken into account in the structural design of the asphalt pavement. Thus it is important to classify asphalt scientifically and reasonably based on the local climatic conditions. Based on 56-year meteorological data from 747 representative meteorological stations in China, temperature and precipitation were divided into eight meteorological indices. Three spatial interpolation methods are used for interpolation, namely Inverse Distance Weight, Ordinary Kriging and Collaborative Kriging considering Digital Elevation Model (DEM). After the comparison, the Collaborative Kriging considering DEM interpolation results of temperature and the Inverse Distance Weight interpolation results of precipitation were used to establish the standard climate zoning system in China. The classification of asphalt pavement is introduced on the basis of performance grading. According to the pavement high and low temperature design formula obtained by comparison, climatic suitability zoning of road asphalt is performed, and the classification table with performance grading of road asphalt in China is presented. The research results provide a theoretical basis for the selection of road asphalt and provide guidance for scientific and reasonable design of asphalt pavement.

Application of a semivariogram based on a deep neural network to Ordinary Kriging interpolation of elevation data.

The Ordinary Kriging method is a common spatial interpolation algorithm in geostatistics. Because the semivariogram required for kriging interpolation greatly influences this process, optimal fitting of the semivariogram is of major significance for improving the theoretical accuracy of spatial interpolation. A deep neural network is a machine learning algorithm that can, in principle, be applied to any function, including a semivariogram. Accordingly, a novel spatial interpolation method based on a deep neural network and Ordinary Kriging was proposed in this research, and elevation data were used as a case study. Compared with the semivariogram fitted by the traditional exponential model, spherical model, and Gaussian model, the kriging variance in the proposed method is smaller, which means that the interpolation results are closer to the theoretical results of Ordinary Kriging interpolation. At the same time, this research can simplify processes for a variety of semivariogram analyses.