Reduced Data Set Research Articles

Developing a questionnaire to assess the health effects of bioaerosols.

Bioaerosol exposure occurs in several industries including green waste recycling, poultry farming, grain, animal feed and cotton production. Whilst several studies have investigated exposure and health effects in compost workers, the best questions to ask about health are uncertain. This study aimed to develop a questionnaire to assess health symptoms in compost workers. A 46-item questionnaire to ascertain possible symptoms of occupational illness related to bioaerosol exposure in a cohort of UK industrial compost workers and delivery in an interviewer-led format. The reliability of the questionnaire was assessed using Cronbach's α. A principal component analysis (PCA) was conducted to condense the questionnaire for practical purposes. One hundred and eleven (89%) workers completed the questionnaire. All items showed very good reliability (Cronbach's α = 0.83). After removing perfectly correlated questions from the data set, the PCA was conducted on a reduced data set of 28 items to explore underlying themes. Nine components were identified that explained 77% of the total variation. Nine of the questions removed prior to PCA due to perfect correlation were reintroduced because they added clinical value. The final questionnaire therefore consisted of 37 items and retained very adequate reliability (Cronbach's α = 0.76). Our health questionnaire has demonstrated adequate reliability when used within this industrial composting workforce. Further applications may include health surveillance, investigating outbreaks of occupational disease or research. Future work should examine the predictive validity of the questionnaire in these settings.

Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning

In this paper, the focus is on the application of prototype selection to multi-label data sets as a preliminary stage in the learning process. There are two general strategies when designing Machine Learning algorithms that are capable of dealing with multi-label problems: data transformation and method adaptation. These strategies have been successfully applied in obtaining classifiers and regressors for multi-label learning. Here we investigate the feasibility of data transformation in obtaining prototype selection algorithms for multi-label data sets from three prototype selection algorithms for single-label. The data transformation methods used were: binary relevance, dependent binary relevance, label powerset, and random k-labelsets. The general conclusion is that the methods of prototype selection obtained using data transformation are not better than those obtained through method adaptation. Moreover, prototype selection algorithms designed for multi-label do not do an entirely satisfactory job, because, although they reduce the size of the data set, without affecting significantly the accuracy, the classifier trained with the reduced data set does not improve the accuracy of the classifier when it is trained with the whole data set.

Ship performance and navigation data compression and communication under autoencoder system architecture

Modern vessels are designed to collect, store and communicate large quantities of ship performance and navigation information through complex onboard data handling processes. That data should be transferred to shore based data centers for further analysis and storage. However, the associated transfer cost in large-scale data sets is a major challenge for the shipping industry, today. The same cost relates to the amount of data that are transferring through various communication networks (i.e. satellites and wireless networks), i.e. between vessels and shore based data centers. Hence, this study proposes to use an autoencoder system architecture (i.e. a deep learning approach) to compress ship performance and navigation parameters (i.e. reduce the number of parameters) and transfer through the respective communication networks as reduced data sets. The data compression is done under the linear version of an autoencoder that consists of principal component analysis (PCA), where the respective principal components (PCs) represent the structure of the data set. The compressed data set is expanded by the same data structure (i.e. an autoencoder system architecture) at the respective data center requiring further analyses and storage. A data set of ship performance and navigation parameters in a selected vessel is analyzed (i.e. data compression and expansion) through an autoencoder system architecture and the results are presented in this study. Furthermore, the respective input and output values of the autoencoder are also compared as statistical distributions and sample number series to evaluate its performance.

First global next-to-leading order determination of diffractive parton distribution functions and their uncertainties within the xFitter framework

We present GKG18-DPDFs, a next-to-leading order (NLO) QCD analysis of diffractive parton distribution functions (diffractive PDFs) and their uncertainties. This is the first global set of diffractive PDFs determined within the xFitter framework. This analysis is motivated by all available and most up-to-date data on inclusive diffractive deep inelastic scattering (diffractive DIS). Heavy quark contributions are considered within the framework of the Thorne–Roberts (TR) general mass variable flavor number scheme (GM-VFNS). We form a mutually consistent set of diffractive PDFs due to the inclusion of high-precision data from H1/ZEUS combined inclusive diffractive cross sections measurements. We study the impact of the H1/ZEUS combined data by producing a variety of determinations based on reduced data sets. We find that these data sets have a significant impact on the diffractive PDFs with some substantial reductions in uncertainties. The predictions based on the extracted diffractive PDFs are compared to the analyzed diffractive DIS data and with other determinations of the diffractive PDFs.

Does feedback on daily activity level from a Smart watch during inpatient stroke rehabilitation increase physical activity levels? Study protocol for a randomized controlled trial

BackgroundPracticing activities improves recovery after stroke, but many people in hospital do little activity. Feedback on activity using an accelerometer is a potential method to increase activity in hospital inpatients. This study’s goal is to investigate the effect of feedback, enabled by a Smart watch, on daily physical activity levels during inpatient stroke rehabilitation and the short-term effects on simple functional activities, primarily mobility.Methods/designA randomized controlled trial will be undertaken within the stroke rehabilitation wards of the Second Affiliated hospital of Anhui University of Traditional Chinese Medicine, Hefei, China. The study participants will be stroke survivors who meet inclusion criteria for the study, primarily: able to participate, no more than 4 months after stroke and walking independently before stroke. Participants will all receive standard local rehabilitation and will be randomly assigned either to receive regular feedback about activity levels, relative to a daily goal tailored by the smart watch over five time periods throughout a working day, or to no feedback, but still wearing the Smart watch. The intervention will last up to 3 weeks, ending sooner if discharged. The data to be collected in all participants include measures of daily activity (Smart watch measure); mobility (Rivermead Mobility Index and 10-metre walking time); independence in personal care (Barthel Activities of Daily Living (ADL) Index); overall activities (the World Health Organization (WHO) Disability Assessment Scale, 12-item version); and quality of life (the Euro-Qol 5L5D). Data will be collected by assessors blinded to allocation of the intervention at baseline, 3 weeks or at discharge (whichever is the sooner); and a reduced data set will be collected at 12 weeks by telephone interview. The primary outcome will be change in daily accelerometer activity scores. Secondary outcomes are compliance and adherence to wearing the watch, and changes in mobility, independence in personal care activities, and health-related quality of life.DiscussionThis project is being implemented in a large city hospital with limited resources and limited research experience. There has been a pilot feasibility study using the Smart watch, which highlighted some areas needing change and these are incorporated in this protocol.Trial registrationClinicalTrials.gov, NCT02587585. Registered on 30 September 2015. Chinese Clinical Trial Registry, ChiCTR-IOR-15007179. Registered on 8 August 2015.

Technology Focus: Seismic Applications (March 2018)

Technology Focus Many innovative advances in the seismic method have been introduced over recent years. In this discussion, I will focus on the topic of sampling. A key example is the azimuthal sampling in full-azimuth 3D surveys—surveys that are needed, for instance, to characterize fractures. Full-azimuth geometries typically call for an expensive explosion in the amount of data needing to be acquired. A way to reduce the acquisition cost is to rely instead on interpolation, but aliasing issues limit the spatial frequencies that can be recovered. In the marine world, an innovation to address this has been to collect crossline-oriented, particle-acceleration measurements in addition to the usual pressure measurements (made by hydrophones). Particle acceleration is related directly to the spatial gradient of the pressure. Knowing both the pressure and the pressure derivative extends the ability for successful interpolation. Another innovation in use today is to abandon the traditional inline/crossline field geometry by shooting in circular tracks instead. Regardless of whether straight lines or circular tracks are used, platforms and other obstructions block access to towed streamers. Placing receiver nodes on the seabed is a very popular way around this problem when corals are not present. Otherwise, wave- and solar-powered unmanned surface vehicles provide a new, tantalizing alternative innovation. Each of these bathtub-sized crafts independently tows a hydrophone array. In the onshore world, one of the big advances for addressing increased sampling requirements has been to go to 24-hour shooting with continuous recording of simultaneously sweeping vibrators. This has been enabled by innovations in both acquisition and processing. In acquisition, the introduction of vibrator command-and-control systems means that the vibrator drivers no longer must wait for the start signal from the recording truck. In processing, the development of deblending algorithms enables the overlapping field records to be separated. These advances dramatically increase the number of records that can be acquired each day—especially in desert regions. An innovation for improving this efficiency even further is to skip over some of the source and receiver positions in a carefully specified, random-looking fashion adopted from the science of compressive sampling. (By relying on sparsity in an appropriate transform domain, data processing can reconstruct the seismic signal adequately from the reduced data set.) Finally, yet another form of sampling of interest today is the time interval between consecutive monitor surveys in 4D programs. Recent innovations enable frequent, lower-cost monitor surveys to see small, rapid changes in deepwater reservoirs. For more information, see the featured papers. Recommended additional reading at OnePetro: www.onepetro.org. SPE 187203 Look-Ahead Geosteering By Means of Real-Time Integration of Logging-While-Drilling Measurements With Surface Seismic by F. Arata, Eni, et al. SPE 184029 Seismic Airborne TEM Joint Inversion and Surface Consistent Refraction Analysis: New Technologies for Complex Near-Surface Corrections by Daniele Colombo, Saudi Aramco, et al. URTeC 2670158 The Use of Time-Lapse Seismic Attributes for Characterizing Hydraulic Fractures in a Tight Siltstone Reservoir by N. Riazi, University of Calgary, et al.

Slepton pair production at the LHC in NLO+NLL with resummation-improved parton densities

Novel PDFs taking into account resummation-improved matrix elements, albeit only in the fit of a reduced data set, allow for consistent NLO+NLL calculations of slepton pair production at the LHC. We apply a factorisation method to this process that minimises the effect of the data set reduction, avoids the problem of outlier replicas in the NNPDF method for PDF uncertainties and preserves the reduction of the scale uncertainty. For Run II of the LHC, left-handed selectron/smuon, right-handed and maximally mixed stau production, we confirm that the consistent use of threshold-improved PDFs partially compensates the resummation contributions in the matrix elements. Together with the reduction of the scale uncertainty at NLO+NLL, the described method further increases the reliability of slepton pair production cross sections at the LHC.

Ensemble Data Reduction Techniques and Multi-RSMOTE via Fuzzy Integral for Bug Report Classification

Due to the unavoidable bugs appearing in the most of the software systems, bug resolution has become one of the most important activities in software maintenance. To decrease the time cost in manual work, text classification techniques are applied to automatically identify severity of bug reports. In this paper, we address the problem of low-quality and class imbalance for identifying the severity of bug reports. First, we combine feature selection with instance selection to simultaneously reduce the bug report dimension and the word dimension, which could get small-scale and high-quality reduced data set. Then, an improve random oversampling technique, named, RSMOTE, which is presented to weaken the imbalancedness degree of class distribution. Finally, to avoid the random over-sampling uncertainty of RSMOTE, we develop an ensemble learning algorithm, which is based on Choquet fuzzy integral, to combine multiple RSMOTE. We empirically investigate the performance of data reduction on ten data sets of three large open source projects, namely, Eclipse, Mozilla, and GNOME. The results show that our approach can effectively reduce the data scale and improve the performance of identifying the severity of bug reports.

Anomalous waves propagating at very high frequency in the atmosphere and their disturbances due to changes in refractivity profiles

Abstract Anomalous waves propagation is severely affected due to almost always present variations in refractivity under various environmental conditions at different time, location and frequency. These conditions, representing different state of the atmosphere including e.g. foggy, rainy and cloudy etc., not only degrade the quality of the signal but sometimes completely eradicate the communication link. Such severe impact on propagation cannot be ignored by the designers of communication systems. The aim of this research is to present correlation between experimental and modelled link losses for variations in refractivity values recommended by International Telecommunication Union-Recommendations (ITU-R) as well as that of standard profiles. To do so, a communication setup of 50 km over the Sea operating experimentally over a period of a year at 240 MHz is analyzed for different refractivity profiles and their impact on propagation. A median value is taken for every set of 6000 values taken from the recorded data set of more than 48 million experimental link losses. This reduces the huge data set of the experimental link losses to 8000 values only. This reduced data set of experimental and modelled link losses were correlated and investigated for different evaporation duct heights throughout the year. For the considered link, the ITU-R refractivity profile was found to perform better than the standard refractivity profile. However, the new findings as observed in this research, which may be helpful for the recommendations authorities, is the existing of evaporation duct up to 10 m height.

Sparse multi-criteria optimization classifier for credit risk evaluation

Over the past few decades, many classifier methods are suggested for credit risk evaluation. With ever-increasing amounts of data, for multi-criteria optimization classifier (MCOC) and other traditional classification methods, owing to the correlation among different features in data these classifiers often give the poor predictive performance. Thus, some dimensionality reduction techniques are firstly used to find important features; then, these classifier models are built on the reduced data set. However, because feature selection and classification are carried out in different feature spaces, the purpose of increasing predictive accuracy and interpretability is difficult to achieve truly. It is therefore important to research the new classifier methods with simultaneous classification and feature selection so as to improve the predictive accuracy and obtain the interpretable results. In this paper, we propose a novel sparse multi-criteria optimization classifier (SMCOC) based on one-norm regularization, linear and nonlinear programming, respectively, and construct the corresponding algorithm. The experimental results of credit risk evaluation and the comparison with linear and quadratic MCOCs, logistic regression and support vector machines have shown that the proposed SMCOC can enhance the separation of different credit applicants, the efficiency of credit scoring, the interpretability of risk evaluation model and the generalization power of risk prediction for new credit applicants.

Daily reference crop evapotranspiration with reduced data sets in the humid environments of Azores islands using estimates of actual vapor pressure, solar radiation, and wind speed

Reference crop evapotranspiration (ETo) estimations using the FAO Penman-Monteith equation (PM-ETo) require a set of weather data including maximum and minimum air temperatures (T max, T min), actual vapor pressure (e a), solar radiation (R s), and wind speed (u 2). However, those data are often not available, or data sets are incomplete due to missing values. A set of procedures were proposed in FAO56 (Allen et al. 1998) to overcome these limitations, and which accuracy for estimating daily ETo in the humid climate of Azores islands is assessed in this study. Results show that after locally and seasonally calibrating the temperature adjustment factor a d used for dew point temperature (T dew) computation from mean temperature, ETo estimations shown small bias and small RMSE ranging from 0.15 to 0.53 mm day−1. When R s data are missing, their estimation from the temperature difference (T max−T min), using a locally and seasonal calibrated radiation adjustment coefficient (k Rs), yielded highly accurate ETo estimates, with RMSE averaging 0.41 mm day−1 and ranging from 0.33 to 0.58 mm day−1. If wind speed observations are missing, the use of the default u 2 = 2 m s−1, or 3 m s−1 in case of weather measurements over clipped grass in airports, revealed appropriated even for the windy locations (u 2 > 4 m s−1), with RMSE < 0.36 mm day−1. The appropriateness of procedure to estimating the missing values of e a, R s, and u 2 was confirmed.

Daily reference crop evapotranspiration in the humid environments of Azores islands using reduced data sets: accuracy of FAO-PM temperature and Hargreaves-Samani methods

Reference crop evapotranspiration (ETo) estimations using the FAO Penman-Monteith equation (PM-ETo) require several weather variables that are often not available. Then, ETo may be computed with procedures proposed in FAO56, either using the PM-ETo equation with temperature estimates of actual vapor pressure (e a) and solar radiation (R s), and default wind speed values (u 2), the PMT method, or using the Hargreaves-Samani equation (HS). The accuracy of estimates of daily e a, R s, and u 2 is provided in a companion paper (Paredes et al. 2017) applied to data of 20 locations distributed through eight islands of Azores, thus focusing on humid environments. Both estimation procedures using the PMT method (ETo PMT) and the HS equation (ETo HS) were assessed by statistically comparing their results with those obtained for the PM-ETo with data of the same 20 locations. Results show that both approaches provide for accurate ETo estimations, with RMSE for PMT ranging 0.48–0.73 mm day−1 and for HS varying 0.47–0.86 mm day−1. It was observed that ETo PMT is linearly related with PM-ETo, while non-linearity was observed for ETo HS in weather stations located at high elevation. Impacts of wind were not important for HS but required proper adjustments in the case of PMT. Results show that the PMT approach is more accurate than HS. Moreover, PMT allows the use of observed variables together with estimators of the missing ones, which improves the accuracy of the PMT approach. The preference for the PMT method, fully based upon the PM-ETo equation, is therefore obvious.

Ananke: temporal clustering reveals ecological dynamics of microbial communities.

Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of clustering based on sequence identity, marker-gene data sets collected over time can be clustered based on temporal correlation to reveal ecologically meaningful associations. We present Ananke, a free and open-source algorithm and software package that complements existing sequence-identity-based clustering approaches by clustering marker-gene data based on time-series profiles and provides interactive visualization of clusters, including highlighting of internal OTU inconsistencies. Ananke is able to cluster distinct temporal patterns from simulations of multiple ecological patterns, such as periodic seasonal dynamics and organism appearances/disappearances. We apply our algorithm to two longitudinal marker gene data sets: faecal communities from the human gut of an individual sampled over one year, and communities from a freshwater lake sampled over eleven years. Within the gut, the segregation of the bacterial community around a food-poisoning event was immediately clear. In the freshwater lake, we found that high sequence identity between marker genes does not guarantee similar temporal dynamics, and Ananke time-series clusters revealed patterns obscured by clustering based on sequence identity or taxonomy. Ananke is free and open-source software available at https://github.com/beiko-lab/ananke.

Novel Spectrophotometric Protocol for the Long-Term Characterization of the Hue of Artwork

ABSTRACTA method for the highly precise, long-term documentation of the color of artwork is described. Gaussian analysis of several ultraviolet–visible absorption spectra allowed the generation of a reduced data set of three to seven Gaussian bands. High-resolution absorption spectra were characterized as 21 or fewer numerical data. The spectral approach enables chemical identification independent from the illumination and concentration of the sample. Applications including documentation and authentication are discussed.

Image reconstruction for cone-beam computed tomography using total p-variation plus Kullback–Leibler data divergence**Project supported by the National Natural Science Foundation of China (Grant Nos. 61372172 and 61601518).

Accurate reconstruction from a reduced data set is highly essential for computed tomography in fast and/or low dose imaging applications. Conventional total variation (TV)-based algorithms apply the L1 norm-based penalties, which are not as efficient as Lp () quasi-norm-based penalties. TV with a p-th power-based norm can serve as a feasible alternative of the conventional TV, which is referred to as total p-variation (TpV). This paper proposes a TpV-based reconstruction model and develops an efficient algorithm. The total p-variation and Kullback–Leibler (KL) data divergence, which has better noise suppression capability compared with the often-used quadratic term, are combined to build the reconstruction model. The proposed algorithm is derived by the alternating direction method (ADM) which offers a stable, efficient, and easily coded implementation. We apply the proposed method in the reconstructions from very few views of projections (7 views evenly acquired within 180°). The images reconstructed by the new method show clearer edges and higher numerical accuracy than the conventional TV method. Both the simulations and real CT data experiments indicate that the proposed method may be promising for practical applications.

LOMA: A local outlier mining algorithm based on attribute relevance analysis

Abstract In this study, we propose a novel local outlier detection approach - called LOMA - to mining local outliers in high-dimensional data sets. To improve the efficiency of outlier detection, LOMA prunes irrelevance attributes and objects in the data set by analyzing attribute relevance with a sparse factor threshold. Such a pruning technique substantially reduce the size of data sets. The core of LOMA is searching sparse subspace, which implements the particle swarm optimization method in reduced data sets. In the process of searching sparse subspace, we introduce the sparse coefficient threshold to represent sparse degrees of data objects in a subspace, where the data objects are considered as local outliers. The attribute relevance analysis provides a guidance for experts and users to identify useless attributes for detecting outliers. In addition, our sparse-subspace-based outlier algorithm is a novel technique for local-outlier detection in a wide variety of applications. Experimental results driven by both synthetic and UCI data sets validate the effectiveness and accuracy of our LOMA. In particular, LOMA achieves high mining efficiency and accuracy when the sparse factor threshold is set to a small value.

Precision Near-Field Reconstruction in the Time Domain via Minimum Entropy for Ultra-High Resolution Radar Imaging

Ultra-high resolution (UHR) radar imaging is used to analyze the internal structure of objects and to identify and classify their shapes based on ultra-wideband (UWB) signals using a vector network analyzer (VNA). However, radar-based imaging is limited by microwave propagation effects, wave scattering, and transmit power, thus the received signals are inevitably weak and noisy. To overcome this problem, the radar may be operated in the near-field. The focusing of UHR radar signals over a close distance requires precise geometry in order to accommodate the spherical waves. In this paper, a geometric estimation and compensation method that is based on the minimum entropy of radar images with sub-centimeter resolution is proposed and implemented. Inverse synthetic aperture radar (ISAR) imaging is used because it is applicable to several fields, including medical- and security-related applications, and high quality images of various targets have been produced to verify the proposed method. For ISAR in the near-field, the compensation for the time delay depends on the distance from the center of rotation and the internal RF circuits and cables. Required parameters for the delay compensation algorithm that can be used to minimize the entropy of the radar images are determined so that acceptable results can be achieved. The processing speed can be enhanced by performing the calculations in the time domain without the phase values, which are removed after upsampling. For comparison, the parameters are also estimated by performing random sampling in the data set. Although the reduced data set contained only 5% of the observed angles, the parameter optimization method is shown to operate correctly.

Parallel Distance-Based Instance Selection Algorithm for Feed-Forward Neural Network

AbstractInstance selection endeavors to decide which instances from the data set should be maintained for further use during the learning process. It can result in increased generalization of the learning model, shorter time of the learning process, or scaling up to large data sources. This paper presents a parallel distance-based instance selection approach for a feed-forward neural network (FFNN), which can utilize all available processing power to reduce the data set while obtaining similar levels of classification accuracy as when the original data set is used. The algorithm identifies the instances at the decision boundary between consecutive classes of data, which are essential for placing hyperplane decision surfaces, and retains these instances in the reduced data set (subset). Each identified instance, called a prototype, is one of the representatives of the decision boundary of its class that constitutes the shape or distribution model of the data set. No feature or dimension is sacrificed in the reduction process. Regarding reduction capability, the algorithm obtains approximately 85% reduction power on non-overlapping two-class synthetic data sets, 70% reduction power on highly overlapping two-class synthetic data sets, and 77% reduction power on multiclass real-world data sets. Regarding generalization, the reduced data sets obtain similar levels of classification accuracy as when the original data set is used on both FFNN and support vector machine. Regarding execution time requirement, the speedup of the parallel algorithm over the serial algorithm is proportional to the number of threads the processor can run concurrently.

Application of √area-concept to assess fatigue strength of AlSi7Cu0.5Mg casted components

In this paper casted aluminium alloy AlSi7Cu0.5Mg T6W is examined by fatigue strength testing and fractographic analysis of the fractured surfaces to characterise the failure origin. The specimens extracted from cylinder heads differ in their eutectic modification between strontium (Sr) and sodium (Na). Three specimen locations are Sr-modified and one is Na-modified. The fractographic analysis enables the characterisation of the occurred micropore sizes within the different specimen positions. The determination of the micropore sizes is also supported by computed tomography (CT) scans for better understanding of the micropore shapes. Murakami’s √area-concept is applied on the four data sets to investigate the dependency of the fatigue strength on the micropore size. For the purpose of increasing the number of data points at the run-out region, two projection methods are presented to consider data points from the finite-life regime. Considering the scatter band of the S/N-curves, a method based on a power equation is selected. The √area-concept is additionally modified to enabling a more precise adjustment on the respective data set. The fractographic analyses show mixed occurrence of slip bands and micropores. To evaluate the application of such mixed cause of failure in the applied approach, a reduced data set without such mixed failure types is compared with the results over the whole data base. The √area-concept over all data points exhibit valid results proofing that the consideration of the mixed causes of failure including slip bands and micropores is acceptable.Comparing the stress levels of fatigue tests with the results from the applied model, on average a conservative behaviour can be noted, whereas almost all assessed data points are within a scatter band of 10% deviation to the mean value of the experiments.

COMPARING UNIFORM AND RANDOM DATA REDUCTION METHODS FOR DTM ACCURACY

The digital cartographic representation of the elevation of the earth's surface created from discrete elevation points is defined as a digital terrain model (DTM). DTMs have been used in a wide range of applications, such as civil planning, flood control, transportation design, navigation, natural hazard risk assessment, hydraulic simulation, visibility analysis of the terrain, topographic change quantification, and forest characterization. Remote sensing, laser scanning, and radar interferometry become efficient sources for constructing high-accuracy DTMs by the developments in data processing technologies. The accuracy, the density, and the spatial distribution of elevation points, the terrain surface characteristics, and the interpolation methods have an influence on the accuracy of DTMs. In this study, uniform and random data reduction methods are compared for DTMs generated from airborne Light Detection and Ranging (LiDAR) data. The airborne LiDAR data set is reduced to subsets by using uniform and random methods, representing the 75%, 50%, and 25% of the original data set. Over the Mount St. Helens in southwest Washington State as the test area, DTM constructed from the original airborne LiDAR data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that uniform data reduction method can be used to reduce the LiDAR datasets to 50% density level while still maintaining the quality of DTM.