Triangulated Irregular Network Research Articles

Integration of Digital Elevation Model (DEM) and HEC-RAS Hydrodynamic Model for flood routing

The development of computer technology, especially the hydrodynamic modeling package, provides convenience in many things including flood modeling in the river. One of these modeling packages is HEC-RAS Hydrodynamic Model which can be used to simulate both steady flow and unsteady flow. On the other side, the development of Geographic Information System (GIS), is now rapidly evolving for a variety of purposes with a wider range of fields and scope, including the preparation of river geometry data based on Digital Elevation Model (DEM) in Triangulated Irregular Network (TIN) format as the input of the model. The aim of this study is to perform flood routing for determining the river capacity and for estimating the factors that cause floods by integrating TIN data into HEC-RAS Hydrodynamic Model, using Lantikadigo River in Central Sulawesi, Indonesia as a model. In this river, almost every year flooding occurs with fluctuating intensity of inundation. Integrating data is the process of synthesizing geometry data that is processed in the GIS environment as input for the HEC-RAS Model. Data integration provides the effectiveness of the use of simulation time due to input geometry data is done using import data facility when compared manually input geometry data. The results of the study show that the maximum water level of the 1-year return period has exceeded the river bank elevation both on the left and on the right side of the entire segment. The peak discharge of hydrograph for 1-year return period is 55.3 m3/s at the outlet of Lantikadigo Watershed. This means that the average channel capacity is far below the peak discharge. Based on simulation results it can be predicted that the cause of flooding in Lantikadigo River is due to morphological change of river geometry.

Application of the Triangulated Irregular Network (TIN) Method in the Creation of Models of Historical Buildings

Renovation or modernization of a historical building involves creating its model. An accurate 3D representation of a building can be used e.g. to analyse the possibilities of renovating or recovering its damaged elements. This paper discusses examples of how helpful 3D laser scanning can be in creating models of historical buildings. This technology is of particular importance when it comes to representing decorative architectural details. To create a 3D image of a building using reverse engineering one must first gather on-site data by means of terrestrial laser scanning in the form of a point cloud. This paper describes the process of creating a model of a historical building by means of the TIN method using Leica’s Cyclone programme. The on-site measurements were taken using a ScanStation C10 scanner, also by Leica.

Watershed Management using Remote Sensing and Gis of Maheshwaram Watershed

Conservation of integrated watershed management essential resources plays an active role in reduction of unfavorable effects like soil erosion, production of food determining the exact water potential zones etc. Land potential is the main basis of managing watershed. Watershed management studies demand is increasing in the recent years. The study is not limited to drought prone areas, but also in humid and semi humid areas in order to maintain the survival of the crops in critical stages. The main objective of the current study area is management of Maheshwaram Watershed based on the rainfall data. In the study area the rainfall data is collected for the 10 years span (2006-2015) from ARI (Agricultural Research Institute) from Rajendra Nagar Hyderabad. Topographic maps are collected from Survey of India in order to prepare base map, digital elevation model (DEM), contour map, drainage map. The Second main objective of the study area is approximation on annual and monthly run-off by using rainfall data. Further Base map, DEM(Digital Elevation Model), contour map, drainage map, slope map, land use and Land cover map, TIN(Triangulated Irregular Network) maps were generated by Survey of India toposheet maps to prepare maps by using ArcGIS Software. Analysis of the study helps the decision makers to provide sustainable planning of the environment and economy in the future.

Assessment of flood hazard mapping in urban areas using entropy weighting method: a case study in Hamadan city, Iran

Flood is one of the major natural disasters which cause enormous casualties and damages particularly in urban areas. In urban areas, studies on flood hazards have been accompanied by tensions for various reasons, including complexity of urban levels, different spatial modeling indices, lack of accurate hydrological data, and precise modeling of land surface simulations. This paper used a Constrained Delaunay Triangular Irregular Network to model fine urban surfaces (based on the detailed ground sampling data), and subsequently discusses five indicators regarding the dangers of flood, namely (1) elevation, (2) slope, (3) distance to discharge channels, (4) index of development and persistence of the drainage network (IDPR), and (5) infiltration rate. In the next step for flood hazard mapping, the combination of geographical information systems and the entropy weight method as the multi-criteria decision analysis was used to combine the indicators. The proposed methodology was used for Hamadan city that is located in the central part of Hamadan Province in Iran where several floods occur annually. The flood hazard mapping indicates that approximately 15.83% of the total study area is classified as very highly hazardous, 31.72% as hazardous, 20.11% as moderate, 16.02% as minor, and 16.32% as the least hazardous. Finally, superimposition and receiver operating characteristic (ROC) curve methods were used to verify the accuracy of the obtained flood hazard map. In terms of superimposition and ROC curve, the accuracy of the model was approximately 70% and 73%, respectively.

Flood Inundation Mapping of Babai Basin using HEC-RAS & GIS

The main objective of this research is to prepare inundation map of a section of Babai Basin and to find out the inundated areas for different return periods. In order to achieve the objectives, Geographic Information System (GIS) and HEC-RAS along with HEC-GeoRAS extension were used as tools. Coordinates were used as primary parameter and were obtained from survey to generate geometric data such as Triangular Irregular Networks (TIN), streamline, river bank and flow paths. After importing the geometry data into HEC-RAS, hydraulic modelling was applied by using flow data as main input. As a result, the watershed area of the Babai basin has been successfully modelled and map showing the flooded areas along the Babai basin has been delineated. The flooded area was geometrically overlaid on the topographic map to outline the affected areas. The area under the influence of the flood were assessed which shows that about 49% of total inundated areas comprises farming land. Also a sensitivity analysis was performed to predict the change in percentage of flooded areas when Maning’s parameter was increased by 10% and when kept same in both banks and channel. However, differences in figure of inundated areas are insignificant to variations made in Maning’s n. 

Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point Clouds

Digital Elevation Models (DEMs) are 3D representations of the Earth’s surface and have numerous applications in geomorphology, hydrology and ecology. Structure-from-Motion (SfM) photogrammetry using photographs obtained by unmanned aerial vehicles (UAVs) have been increasingly used for obtaining high resolution DEMs. These DEMs are interpolated from point clouds representing entire landscapes, including points of terrain, vegetation and infrastructure. Up to date, there has not been any study clearly comparing different algorithms for filtering of vegetation. The objective in this study was, therefore, to assess the performance of various vegetation filter algorithms for SfM-obtained point clouds. The comparison was done for a Mediterranean area in Murcia, Spain with heterogeneous vegetation cover. The filter methods that were compared were: color-based filtering using an excessive greenness vegetation index (VI), Triangulated Irregular Networks (TIN) densification from LAStools, the standard method in Agisoft Photoscan (PS), iterative surface lowering (ISL), and a combination of iterative surface lowering and the VI method (ISL_VI). Results showed that for bare areas there was little to no difference between the filtering methods, which is to be expected because there is little to no vegetation present to filter. For areas with shrubs and trees, the ISL_VI and TIN method performed best. These results show that different filtering techniques have various degrees of success in different use cases. A default filter in commercial software such as Photoscan may not always be the best way to remove unwanted vegetation from a point cloud, but instead alternative methods such as a TIN densification algorithm should be used to obtain a vegetation-less Digital Terrain Model (DTM).

Bernstain-search differential evolution algorithm for numerical function optimization

The standard Differential Evolution Algorithm (sDE) is a stochactic search method commonly used in evolutionary computing. The problem solving success of sDE is highly sensitive to the genetic operators used and the initial values of the parameters of these operators. Since a universal Differential Evolution Algorithm (uDE) is not sensitive to the structure and parameter values of the genetic operators used, it is parameter-free in practice and easier to control than sDE. uDE does not need a trial-and-error process when selecting the genetic operators and initial values of intrinsic parameter of related genetic operators to solve the problem, unlike the sDE. Therefore, the use and adaptation of a uDE to solve different types of numerical engineering problems is easy and time-consuming compared to sDE. In this paper, a new uDE, Bernstain-Search Differential Evolution Algorithm (BSD), is introduced. BSD is new and easily controllable, simple structured, non-recursive, highly efficient, fast and practically parameter-free uDE. BSD have a too feasible random crossover and mutation process and does not have a control-parameter setting process, contrary to sDE and its improved variants. In this paper, 30 benchmark problems of CEC’2014, 60 classic benchmark problems, image evolution problems for 12 test images and one Triangulated Irregular Network (TIN) refinement problem were used in the experiments performed to investigate the problem solving success of BSD, statistically. Four tested methods (i.e., ABC, JADE, CUCKOO, WDE) were used in the conducted experiments. Problem solving successes of BSD and tested methods were statistically compared by using Wilcoxon Signed Rank Test piecewisely. Results obtained from the performed tests showed that in general, problem solving success of BSD is statistically better than the tested methods that have been used in this paper.

3D EARTHWORK BIM DESIGN AND ITS APPLICATION IN AN ADVANCED CONSTRUCTION EQUIPMENT OPERATION

Introduction: Advent of new technologies leads the construction industry in utilizing the modern techniques, especially in the earthwork process. The machine guidance system is one of such examples which allows the operator to work more efficiently by using the design on the display screen. Methods: This paper outlines a new Building Information Modeling (BIM) approach to earthwork design with the development of module system which calculates the actual earth volume and generates a TIN surface for machine guidance. This method has importance in generating Triangular Irregular Network (TIN) surface which can be used for visualization of the 3D model as well as for the machine guidance. Results and discussion: The integration of IT based system with the earthwork site results in higher efficiency and productivity. In earthwork process, the proper flow of information is essential as it can increase the transparency and lead to process improvement. This module provides an easy approach for the model generation with its user-friendly interface.

Application of 3D GIS Flood Simulation to Investigate the Source of Inundation at Pekan Sub-District

Flood is usually caused by heavy precipitation that eventually lead to river spillage. In some rare cases, inundation could happen due to the accumulation of water on land cover resulting in stagnant flooding. Pekan sub-district is chosen as the ideal study area to determine the type of flood in this location. Using Geographic Information System (GIS) approach, 3-Dimensional inundation simulation procedure is applied to discover the real source of flooding at the study area. This study is performed to observe flood pattern over the study area based on the river surface level readings of nearby Department of Irrigation and Drainage Malaysia (DID) station. Flood simulation used SRTM (Shuttle Radar Topographic Mission) dataset to produce Pekan sub-district topography into 3D TIN (triangulated irregular network) model. Additionally, water body layer represents specific Pahang River gauge level offsets for overland inundation and river expansion inspection. Historical datasets of Pahang River hydrograph at station 3434401 published by DID were used to find the peak level. Results from inundation model instances show that very few overtopping incidences observed along Pahang River within the study boundary. Still, signs of the growing number of water spots on land cover positively indicate that inundation is again happening. This situation concludes that Pekan sub-district flood occurrences are caused by stagnant flooding. Justification is done through photographic evidence and historical DID hydrograph datasets of the year 2007 to the 1st quarter of 2018.

Modelling of buildings from aerial LiDAR point clouds using TINs and label maps

This paper presents a new framework for automatically creating compact building models from aerial LiDAR point clouds, where each point is known to belong to the class building. The approach addresses the issues of non-uniform point density and outlier detection to extract and refine semantic roof structures by a sequence of operations on a label map. We first partition the points into some coarse regions based on a region growing method over the Triangulated Irregular Network (TIN) model. The region label IDs are then projected to a 2D grid map, which is used to refine the roof regions and their boundaries. We design an energy optimization approach on the label map to optimize the region labels. In order to regularize the contours of roof regions extracted from the label map, we propose a new method for refining contour segment vertices, which iteratively filters the normals of contour segments and uses them to guide the update of contour vertices. The effectiveness of this method is evaluated on LiDAR point clouds from different scenes, and its performance is validated by extensive comparisons to state-of-the-art techniques.

Simulation of extreme temperatures using a new method: TIN‐copula

AbstractIn climatology, there is a clear need for more reliable data, especially in regions where no meteorological stations exist. Different statistical methods as well as regional climate models are usually used for covering areas with limited data. However, important biases between real and simulated climate parameters are observed, especially with respect to extremes. The present study introduces a new statistical method that combines triangular irregular networks and copulas for the simulation of extreme maximum and minimum temperatures. According to the new method, a studied region can be divided into triangles, and a data series can be simulated for every unknown x‐point in each triangle. The simulation of new data series is based on both the distances between the unknown point and the triangle vertices and the observational data available at each vertex. The statistical evaluation of this new method was successful and further demonstrated that the size of the triangle as well as the climatic characteristics of the stations at the triangle vertices can significantly affect the final results. The present investigation proposes the triangular irregular network‐copula method for the simulation of extreme temperatures, as the projections provided by this method are shown to approach the observed extremes values for individual meteorological stations.

Surfaces of Revolution (SORs) Reconstruction Using a Self-Adaptive Generatrix Line Extraction Method from Point Clouds

This paper presents an automatic reconstruction algorithm of surfaces of revolution (SORs) with a self-adaptive method for generatrix line extraction from point clouds. The proposed method does not need to calculate the normal of point clouds, which can greatly improve the efficiency and accuracy of SORs reconstruction. Firstly, the rotation axis of a SOR is automatically extracted by a minimum relative deviation among the three axial directions for both tall-thin and short-wide SORs. Secondly, the projection profile of a SOR is extracted by the triangulated irregular network (TIN) model and random sample consensus (RANSAC) algorithm. Thirdly, the point set of a generatrix line of a SOR is determined by searching for the extremum of coordinate Z, together with overflow points processing, and further determines the type of generatrix line by the smaller RMS errors between linear fitting and quadratic curve fitting. In order to validate the efficiency and accuracy of the proposed method, two kinds of SORs, simple SORs with a straight generatrix line and complex SORs with a curved generatrix line are selected for comparison analysis in the paper. The results demonstrate that the proposed method is robust and can reconstruct SORs with a higher accuracy and efficiency based on the point clouds.

Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters

Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

A Random Features-Based Method for Interpolating Digital Terrain Models with High Efficiency

Airborne light detection and ranging (lidar) is becoming a widely adopted technique for capturing elevation data, which are mainly used for creating digital terrain models (DTMs). However, the large size of lidar datasets poses a serious computational challenge to the promising radial basis function (RBF) interpolation method. In this work, to reduce the huge computational cost and improve the interpolation accuracy, random Fourier features are first introduced to approximate the Gaussian kernel of RBFs in feature space, then a random features-based weighted RBF interpolation method is developed. Based on randomized Fourier features, the nonlinear kernel-based training and evaluation of the RBF method is transformed into simple linear operations in feature space, and with the help of weighted ridge regression, the negative effect of the non-Gaussian distribution of lidar datasets on DTM production is reduced. In other words, the combination of randomized Fourier features and weighted ridge regression improves the efficiency and accuracy of the RBF interpolation method. Experiments on simulated datasets indicate that the proposed method performs better than the classical or random features-based RBF method for dealing with non-Gaussian distributed samples, with the former being slightly less accurate than the iterative RBF method due to the low-dimensional random features. However, the computational cost of the new method is much lower compared with the classical or iterative RBFs. Interpolation of airborne lidar-derived points demonstrates that the new method has a computational cost similar to the inverse distance weighting and triangulated irregular network (TIN) approaches, and is significantly faster than the ordinary kriging (OK) or thin plate spline (TPS) methods. Quantitatively, for interpolation of 644,433 points, the proposed method is approximately 833 and 21 times faster than OK and TPS, respectively. Moreover, the new method avoids the surface discontinuity artifacts presented by the OK, TPS, and TIN methods.

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular network (TIN), which was directly built from structure from motion (SfM) point clouds. Growing degree days (GDD) was used as the meteorological feature. Three models were used to estimate rice AGB, including the simple linear regression (SLR) model, simple exponential regression (SER) model, and machine learning model (random forest). Compared to models that do not use structural and meteorological features (NDRE, R2 = 0.64, RMSE = 286.79 g/m2, MAE = 236.49 g/m2), models that include such features obtained better estimation accuracy (NDRE*Hcv/GDD, R2 = 0.86, RMSE = 178.37 g/m2, MAE = 127.34 g/m2). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features.

Parallelization of the flow-path network model using a particle-set strategy

ABSTRACTHigh-performance simulation of flow dynamics remains a major challenge in the use of physical-based, fully distributed hydrologic models. Parallel computing has been widely used to overcome efficiency limitation by partitioning a basin into sub-basins and executing calculations among multiple processors. However, existing partition-based parallelization strategies are still hampered by the dependency between inter-connected sub-basins. This study proposed a particle-set strategy to parallelize the flow-path network (FPN) model for achieving higher performance in the simulation of flow dynamics. The FPN model replaced the hydrological calculations on sub-basins with the movements of water packages along the upstream and downstream flow paths. Unlike previous partition-based task decomposition approaches, the proposed particle-set strategy decomposes the computational workload by randomly allocating runoff particles to concurrent computing processors. Simulation experiments of the flow routing process were undertaken to validate the developed particle-set FPN model. The outcomes of hourly outlet discharges were compared with field gauged records, and up to 128 computing processors were tested to explore its speedup capability in parallel computing. The experimental results showed that the proposed framework can achieve similar prediction accuracy and parallel efficiency to that of the Triangulated Irregular Network (TIN)-based Real-Time Integrated Basin Simulator (tRIBS).

TIN-based Tag Map Layout

ABSTRACTWith the widespread use of tag clouds, multiple map-based variations have been proposed. Like standard tag clouds (also called word clouds), these ‘tag maps’ all share the basic strategy of displaying words within a ‘geographic space’ and scaling the word size to depict frequency (or importance) of those words within some dataset. While some tag maps simply plot a standard tag cloud on top of a map, the subset of tag maps we focus on here are those in which the collection of words are displayed within bounded geographic regions (often of irregular shape) that the words are relevant for. For this form of tag map, map scale and polygon shape add constraints to word size and position that have not been considered in most prior approaches to tag map word layout. In this paper, we present a layout strategy for tag map generation that includes consideration of the shape and size of the geographical regions acting as containers for the tags. The method introduced here uses a triangulated irregular network (TIN) to subdivide the geographical region into many triangle subareas, with the centroid of each triangle being a potential location to centre a tag on. All the triangles are sorted by their area and all the tags are sorted by their weight value (e.g. frequency, importance or popularity). Positioning of tags is undertaken sequentially from most important (or frequent or popular) with potential locations being the TIN triangle centroids (tried from largest to smallest triangle). After each tag placement, the TIN is recalculated to integrate the tag centroid and bounding corners into the TIN creation. The limited whitespace in the geographical region, at any specific scale, is used fully by dynamically adjusting the font size along with the number and the direction of tags. The method can be applied to add tags within geographic polygons that are convex, concave and other more complex regions containing holes or islands.

Use of Drones for the Topo-Bathymetric Monitoring of the Reservoirs of the Segura River Basin

The Segura River Basin (SRB), located in the South East of Spain, has the lowest percentage of renewable water resources of all the Spanish basins. Therefore, knowledge of the annual rate of water reservoir sedimentation is an important issue to be resolved in one of the most water-stressed regions in the western Mediterranean basin. This paper describes the sensors developed in collaboration with technology-based enterprises (aerial drone, floating drone, and underwater drone), and the methodology for integration of the different types of data acquired to monitor the reservoirs of the SRB. The proposed solution was applied to 21 reservoirs of the SRB. The proposed methodology is based on the use of unmanned aerial vehicles (UAV) for photogrammetry of the reservoir surface area. For each reservoir, two flights were completed, with 20 cm and 5 cm resolution, respectively. Then, a triangular irregular network mesh was generated by GIS techniques. Surface water vehicles (USV) and underwater remote-operated vehicles (ROV) were used to undertake bathymetric surveys. In addition, water quality measurements were made with an ROV device. The main results consist of topographic and bathymetric measurements for each reservoir, obtained by using equipment based on OpenSource technology. According to the results, the annual rate of storage capacity loss of water resources in the SRB´s reservoirs is 0.33%.

A Modified Methodology for Generating Indoor Navigation Models

Automatic methods for constructing navigation routes do not fully meet all requirements. The aim of this study was to modify the methodology for generating indoor navigation models based on the Medial Axis Transformation (MAT) algorithm. The simplified method for generating corridor axes relies on the Node-Relation Structure (NRS) methodology. The axis of the modeled structure (corridor) is then determined based on the points of the middle lines intersecting the structure (polygon). The proposed solution involves a modified approach to the segmentation of corridor space. Traditional approaches rely on algorithms for generating Triangulated Irregular Networks (TINs) by Delaunay triangulation or algorithms for generating Thiessen polygons known as Voronoi diagrams (VDs). In this study, both algorithms were used in the segmentation process. The edges of TINs intersected structures. Selected midpoints on TIN edges, which were located in the central part of the structure, were used to generate VDs. Corridor structures were segmented by polygon VDs. The identifiers or structure nodes were the midpoints on the TIN edges rather than the calculated centroids. The generated routes were not zigzag lines, and they approximated natural paths. The main advantage of the proposed solution is its simplicity, which can be attributed to the use of standard tools for processing spatial data in a geographic information system.

Establishment of digital 3D map based on discrete elevation point data measured in the field

This study established a survey zone digital elevation model (DEM) using highly precise and regularly updated three-dimensional coordinates of the surface of barchan dunes using CASS9.0 software. Two contour plots were drawn, one for the entire survey zone drawn with a contour interval of 0.005 m and one for an individual sand dune drawn with a contour interval of 0.001 m. ArcMap10 was used to define the projection of the created contour lines in DWG format and export the data into shape files to enable the creation of a triangulated irregular network (TIN). Next, TIN data was converted into raster data to facilitate our analysis and data processing. We created the final TIN by using an additional smoothing processing on the desired area that had been clipped out. We added the TIN data to the ArcScene module to establish the final 3D numerical model of barchan dunes. This model extracts and analyzes the changes in the characteristic parameters of the barchan dune as its topography changes in real time so researchers can visualize dune migration and evolution. Their understanding is enhanced by comparing and superposing the various 3D dune models on different days.