Open Access. Powered by Scholars. Published by Universities.®

Civil and Environmental Engineering Commons™

Open Access. Powered by Scholars. Published by Universities.®

Machine learning

Discipline
Institution
Publication Year
Publication
Publication Type
File Type

Articles 91 - 120 of 157

Full-Text Articles in Civil and Environmental Engineering

Predictions Of The Dynamic Complex Modulus Of Non-Conventional Asphalt Concrete Using Machine Learning Techniques, Annie Benson Jan 2023

Predictions Of The Dynamic Complex Modulus Of Non-Conventional Asphalt Concrete Using Machine Learning Techniques, Annie Benson

College of Graduate Studies: Theses & Dissertations

The complex dynamic modulus (|E*|) is a characterization property that defines the stiffness of an asphalt mixture. The dynamic modulus can be found through lab testing or predictions. Since lab testing can be time-consuming and expensive, the prediction method can be used as an alternative method. While a statistical method has been traditionally used for the |E*| prediction such as the Witczak’s predictive equations, machine learning (ML) is recently emerging as an alternative way that |E*| predictions can be made. This research attempted to predict the |E*| using several ML techniques including linear regression, support vector machines (SVM), decision trees, …

Go to article

A Computer Vision-Based Method For Tack Coat Coverage Inspection Using Drone-Collected Images, Aida Da Silva Jan 2023

A Computer Vision-Based Method For Tack Coat Coverage Inspection Using Drone-Collected Images, Aida Da Silva

Graduate Theses, Dissertations, and Problem Reports (ETD)

Tack coat is a thin asphalt applied between the existing surface and asphalt overlay during road rehabilitation. The uniformity of tack coat coverage plays a vital role in providing adhesive bonding between the two layers in the pavement structures. To ensure tack coat uniformity, the current practice primarily relies on manual inspection during construction by field experts. This process is time-consuming and tedious, and the results can be subjective and error-prone. Drones have emerged as a non-destructive sensing technology in the construction industry for many inspection practices. Unlike other non-destructive inspection technologies, drones offer benefits ranging from accelerating data collection …

Go to article

Automated Approach For The Enhancement Of Scaffolding Structure Monitoring With Strain Sensor Data, Sayan Sakhakarmi Dec 2022

Automated Approach For The Enhancement Of Scaffolding Structure Monitoring With Strain Sensor Data, Sayan Sakhakarmi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Construction researchers have made a significant effort to improve the safety of scaffolding structures, as a large proportion of workers are involved in construction activities requiring scaffolds. However, most past studies focused on design and planning aspects of scaffolds. While limited studies investigated scaffolding safety during construction, they are limited to simple cases only with limited failure modes and simple scaffolds. In response to this limitation, this study aims to develop an automated scaffold monitoring approach capable of monitoring large scaffolds. Accordingly, this study developed an automated scaffold safety monitoring framework that leverages sensor data collected from a scaffold, scaffold …

Go to article

Artificial Intelligence In Civil Infrastructure Health Monitoring—Historical Perspectives, Current Trends, And Future Visions, Tarutal Ghosh Mondal, Genda Chen Sep 2022

Artificial Intelligence In Civil Infrastructure Health Monitoring—Historical Perspectives, Current Trends, And Future Visions, Tarutal Ghosh Mondal, Genda Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the …

Go to article

Ag-Iot For Crop And Environment Monitoring: Past, Present, And Future, Nipuna Chamara, Md Didarul Islam, Geng Bai, Yeyin Shi, Yufeng Ge Sep 2022

Ag-Iot For Crop And Environment Monitoring: Past, Present, And Future, Nipuna Chamara, Md Didarul Islam, Geng Bai, Yeyin Shi, Yufeng Ge

Department of Agricultural and Biological Systems Engineering: Faculty Publications

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction …

Go to article

Multimodal Imaging Of Structural Concrete Using Image Fusion And Deep Learning, Sina Mehdinia Aug 2022

Multimodal Imaging Of Structural Concrete Using Image Fusion And Deep Learning, Sina Mehdinia

Dissertations and Theses

Concrete structures may be exposed to a variety of loads and environments during their service life. Non-destructive testing (NDT) techniques can be helpful in evaluating the condition of a structure. Imaging provides a visual representation of the interior of concrete and its condition non-destructively. Ground penetrating radar (GPR) and ultrasonic echo array (UEA) using electromagnetic and stress waves, respectively, provide the data that can be used to reconstruct an image. In this PhD dissertation, image reconstruction and fusion algorithms, simulation, and a deep learning model were investigated with the goal to lay the foundation for enhanced imaging applications for concrete. …

Go to article

Data Fusion For Non-Motorized Volume Estimation: A Machine Learning Approach, Md. Mintu Miah Aug 2022

Data Fusion For Non-Motorized Volume Estimation: A Machine Learning Approach, Md. Mintu Miah

Civil Engineering Dissertations - Archive

ABSTRACT: Despite increased awareness at the state and federal levels of the design and policy concerns associated with nonmotorized infrastructure, communities typically develop strategies to estimate network-wide nonmotorized traffic. However, past research fails to provide the same level of estimation sophistication as motor vehicles due to a lack of investment, which limits increased understanding and capability among communities in network-wide nonmotorized traffic volume estimation. Although previous studies introduce machine learning, Neural Networks, and feature engineering to motorized traffic volume estimation, nonmotorized traffic estimation experiences limited attempts to use these advanced techniques. This dissertation addresses this deficiency in bicycle volume estimation …

Go to article

Evaluation Of Decision-Making Prediction Models For Sewer Pipes Asset Management, Salar Shirkhanloo Aug 2022

Evaluation Of Decision-Making Prediction Models For Sewer Pipes Asset Management, Salar Shirkhanloo

Civil Engineering Dissertations - Archive

Wastewater collection systems deteriorate over time, requiring continuous adjustments and the development of asset management frameworks on the part of utility owners to maintain the performance of their assets. Any asset management framework should emphasize the importance of asset inspection and condition evaluation for efficient system operation and maintenance. Closed-circuit television (CCTV) is the most widely used tool in the United States for inspecting the interior of sewer pipes, which is a somewhat expensive and time-consuming process given the extensive inventory of pipes in a city. Due to their vast inventory of these pipes, no municipality can inspect every individual …

Go to article

Machine Learning-Enabled Model-Based Condition Assessment Of Water Pipelines By Leveraging Hydraulic Monitoring Data, Ahmad Momeni Aug 2022

Machine Learning-Enabled Model-Based Condition Assessment Of Water Pipelines By Leveraging Hydraulic Monitoring Data, Ahmad Momeni

All Dissertations

Overpopulation and climate change have direly challenged the freshwater resources, specifically potable water supplied by water distribution networks (WDNs). One aggravating issue associated with the WDNs is associated with the pipeline leakage, which accounts for almost 20% of freshwater loss in WDNs throughout the US. Leakage detection and severity measurement are of top
asset management priorities in water utilities to minimize and mitigate complicated risks attributed to background and burst leakage. Accordingly, decline in other pipe condition parameters such as effective hydraulic diameters and roughness coefficients, which are complex and uncertain in nature, abets leakage by worsening the WDN status …

Go to article

Learning From Machines: Insights In Forest Transpiration Using Machine Learning Methods, Morgan Thornwell Jul 2022

Learning From Machines: Insights In Forest Transpiration Using Machine Learning Methods, Morgan Thornwell

Dissertations and Theses

Machine learning has been used as a tool to model transpiration for individual sites, but few models are capable of generalizing to new locations without calibration to site data. Using the global SAPFLUXNET database, 95 tree sap flow data sites were grouped using three clustering strategies: by biome, by tree functional type, and through use of a k-means unsupervised clustering algorithm. Two supervised machine learning algorithms, a random forest algorithm and a neural network algorithm, were used to build machine learning models that predicted transpiration for each cluster. The performance and feature importance in each model were analyzed and compared …

Go to article

Coevolution Of Machine Learning And Process-Based Modelling To Revolutionize Earth And Environmental Sciences: A Perspective, Mojtaba Sadegh Jun 2022

Coevolution Of Machine Learning And Process-Based Modelling To Revolutionize Earth And Environmental Sciences: A Perspective, Mojtaba Sadegh

Civil Engineering Faculty Publications and Presentations

Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in …

Go to article

Automated Quality Control For In-Situ Water Temperature Sensors, Leah S. Richardson May 2022

Automated Quality Control For In-Situ Water Temperature Sensors, Leah S. Richardson

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The identification of data not representative of the target subject for outdoor (in-situ) environmental sensors (bad data) is a topic that has been explored in the past. Many tools (such as data filters and computer models) have succeeded in providing an end user with properly identified incorrect data over 95% of the time. However, with the continuous increase in the use of automated data collection, a simple indication of the bad data may no longer provide the end user with enough information to reduce the amount of time that must be spent for manual quality control. The purpose of this …

Go to article

Data-Driven Framework For Understanding & Modeling Ride-Sourcing Transportation Systems, Bishoy Kelleny May 2022

Data-Driven Framework For Understanding & Modeling Ride-Sourcing Transportation Systems, Bishoy Kelleny

Civil & Environmental Engineering Theses & Dissertations

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for …

Go to article

Using Safety Performance Models, Autonomous Vehicle Data, And Machine Learning To Develop Contextual Complexity Criteria To Establish A Standardized Process For On-Road Evaluation Of Medically At-Risk Drivers Considering Static And Dynamic Factors Of The Roadway Environment, Vijay Bendigeri May 2022

Using Safety Performance Models, Autonomous Vehicle Data, And Machine Learning To Develop Contextual Complexity Criteria To Establish A Standardized Process For On-Road Evaluation Of Medically At-Risk Drivers Considering Static And Dynamic Factors Of The Roadway Environment, Vijay Bendigeri

All Dissertations

The field of transportation engineering has an opportunity to positively impact the medical community, specifically the clinicians who evaluate, train, and rehabilitate at-risk drivers. Driving Rehabilitation Specialists (DRSs) have an essential role in making roads safer for medically-at-risk drivers, their passengers, and other road users. DRSs conduct on-road driving evaluations, which are considered the gold standard to make fitness-to-drive decisions due to their high face validity. Most DRSs use a fixed route, meaning the exact same route is used to evaluate each client. When a DRS develops a fixed route, that clinician identifies characteristics of the roadway they think are …

Go to article

Digitalization Of Construction Project Requirements Using Natural Language Processing (Nlp) Techniques, Fahad Ul Hassan May 2022

Digitalization Of Construction Project Requirements Using Natural Language Processing (Nlp) Techniques, Fahad Ul Hassan

All Dissertations

Contract documents are a critical legal component of a construction project that specify all wishes and expectations of the owner toward the design, construction, and handover of a project. A single contract package, especially of a design-build (DB) project, comprises hundreds of documents including thousands of requirements. Precise comprehension and management of the requirements are critical to ensure that all important explicit and implicit requirements of the project scope are captured, managed, and completed. Since requirements are mainly written in a natural human language, the current manual methods impose a significant burden on practitioners to process and restructure them into …

Go to article

Quantitative Evaluation Of Steel Corrosion Induced Deterioration In Rubber Concrete By Integrating Ultrasonic Testing, Machine Learning And Mesoscale Simulation, Jinrui Zhang, Mengxi Zhang, Biqin Dong, Hongyan Ma Apr 2022

Quantitative Evaluation Of Steel Corrosion Induced Deterioration In Rubber Concrete By Integrating Ultrasonic Testing, Machine Learning And Mesoscale Simulation, Jinrui Zhang, Mengxi Zhang, Biqin Dong, Hongyan Ma

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Chloride-induced steel corrosion seriously affects the durability of reinforced concrete structures. Rubber concrete, an environmentally friendly construction material in which waste rubber is recycled as a concrete component, has demonstrated superior resistance to chloride-induced steel corrosion and the subsequent concrete deterioration. However, quantitative evaluation of the degree of deterioration in rubber concrete based on nondestructive detection is challenging due to the complexity of the material. In this paper, reinforced concrete specimens with rubber contents of 0, 10% and 20% are subjected to the electrochemically accelerated corrosion experiments and monitored by ultrasonic testing. Six machine learning models are trained by the …

Go to article

Application Of Machine Learning To Predict The Performance Of An Emipg Reactor Using Data From Numerical Simulations, Owen Sedej, Eric G. Mbonimpa, Trevor Sleight, Jeremy Slagley Mar 2022

Application Of Machine Learning To Predict The Performance Of An Emipg Reactor Using Data From Numerical Simulations, Owen Sedej, Eric G. Mbonimpa, Trevor Sleight, Jeremy Slagley

Faculty Publications

Microwave-driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A two-dimensional …

Go to article

Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej Mar 2022

Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej

Theses and Dissertations

This thesis aims to contribute to the future development of this technology by providing an in-depth literature review of how this technology physically operates and can be numerically modeled. Additionally, this thesis reviews literature of machine learning models that have been applied to gasification to make accurate predictions regarding the system. Finally, this thesis provides a framework of how to numerically model an experimental plasma gasification reactor in order to inform a variety of machine learning models.

Go to article

Application Of Nonlinear Time Series And Machine Learning Algorithms For Forecasting Groundwater Flooding In A Lowland Karst Area, Bidroha Basu, Patrick Morrissey, Laurence Gill Feb 2022

Application Of Nonlinear Time Series And Machine Learning Algorithms For Forecasting Groundwater Flooding In A Lowland Karst Area, Bidroha Basu, Patrick Morrissey, Laurence Gill

Publications

In karst limestone areas interactions between ground and surface waters can be frequent, particularly in low lying areas, linked to the unique hydrogeological dynamics of that bedrock aquifer. In extreme hydrological conditions, however, this can lead to wide-spread, long-duration flooding, resulting in significant cost and disruption. This study develops and compares a nonlinear time-series analysis based nonlinear autoregressive model with exogenous variables (NARX), machine learning based near support vector regression as well as a linear time-series ARX model in terms of their performance to predict groundwater flooding in a lowland karst area of Ireland. The models have been developed upon …

Go to article

Supervised Machine Learning Techniques Applied To Low-Cost Air Quality Sensor Suites, Peter Wahman Jan 2022

Supervised Machine Learning Techniques Applied To Low-Cost Air Quality Sensor Suites, Peter Wahman

All Undergraduate Theses and Capstone Projects

Low-cost PM sensors have garnered interest for their ability to reduce the cost of investigating PM concentrations in both indoor and outdoor spaces. They perform well in high concentration lab testing with correlation coefficients greater than 0.9. In real-world applications, the correlation coefficients drop significantly because of sensing floors and adverse ambient conditions. There are plenty of supervised machine learning techniques that aim to correct the measurements ranging from linear regression to more advanced neural networks and random forests. This work aims to use those more complicated techniques to adjust the measurements using other data sets gathered by a sensor …

Go to article

Assessing Machine Learning Utility In Predicting Hydrologic And Nitrate Dynamics In Karst Agroecosystems, Timothy Mcgill Jan 2022

Assessing Machine Learning Utility In Predicting Hydrologic And Nitrate Dynamics In Karst Agroecosystems, Timothy Mcgill

Theses and Dissertations--Biosystems and Agricultural Engineering

Seasonal hypoxia in the Gulf of Mexico and harmful algal blooms experienced in many inland freshwater bodies is partially driven due to excessive nitrogen loading seen from agricultural watersheds. Within the Mississippi/Atchafalaya River Basin, many areas are underlain with karst features, and efforts to reduce nitrogen contributions from these areas have had varying success, due to lacking a complete understanding of nutrient dynamics in karst agricultural systems. To improve the understanding of nitrogen cycling in these systems, 35 months of high resolution in situ water quality and atmospheric data were collected and fed into a two-hidden layer extreme learning machine …

Go to article

A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir Jan 2022

A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir

Dissertations and Theses

Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches toward the comprehension of urban flood risk using data science techniques on crowd-sourced data. This is accomplished by developing a series of data-driven models to identify flood factors of significance and localized areas of flood vulnerability in New York City (NYC). First, the infrastructural (catch basin clogs, …

Go to article

An Intelligent Approach For Predicting The Strength Of Geosynthetic-Reinforced Subgrade Soil, Muhammad Nouman Amjad Raja, Sanjay K. Shukla, Muhammad Umer Arif Khan Jan 2022

An Intelligent Approach For Predicting The Strength Of Geosynthetic-Reinforced Subgrade Soil, Muhammad Nouman Amjad Raja, Sanjay K. Shukla, Muhammad Umer Arif Khan

Research outputs 2014 to 2021

In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model …

Go to article

Similitude, Scaling, And Prediction Of Stream Dissolved Oxygen Across The Contiguous U.S., Aron Gebreslase Jan 2022

Similitude, Scaling, And Prediction Of Stream Dissolved Oxygen Across The Contiguous U.S., Aron Gebreslase

Graduate Theses, Dissertations, and Problem Reports (ETD)

Dissolved oxygen (DO) is a general indicator of stream water quality and ecosystem health. However, the concentration of in-stream DO is controlled by various climatic, land use/cover, hydrologic, biochemical, and ecological drivers. Observational data for stream DO are often unavailable at the desired temporal and spatial scales. Accurate and reliable prediction of stream DO concentration based on a small set of environmental drivers is important to guide the water managers and policymakers to achieve and maintain healthy streams. This dissertation focused on identifying the dominant controls of stream DO and understanding the mechanisms and environmental regimes by employing machine learning, …

Go to article

Machine-Learning Algorithms For Forecast-Informed Reservoir Operation (Firo) To Reduce Flood Damages, Manizhe Zarei, Omid Bozorg-Haddad, Sahar Baghban, Mohammad Delpasand, Erfan Goharian, Hugo A. Loaiciga Dec 2021

Machine-Learning Algorithms For Forecast-Informed Reservoir Operation (Firo) To Reduce Flood Damages, Manizhe Zarei, Omid Bozorg-Haddad, Sahar Baghban, Mohammad Delpasand, Erfan Goharian, Hugo A. Loaiciga

Faculty Publications

Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by …

Go to article

Predicting Pavement Structural Condition Using Machine Learning Methods, Nazmus Sakib Ahmed Oct 2021

Predicting Pavement Structural Condition Using Machine Learning Methods, Nazmus Sakib Ahmed

Theses and Dissertations

State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement’s structural condition. The aim is to be able to predict whether a pavement section’s structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, …

Go to article

Short-Term Crash Risk Prediction Considering Proactive, Reactive, And Driver Behavior Factors, Sina Darban Khales Aug 2021

Short-Term Crash Risk Prediction Considering Proactive, Reactive, And Driver Behavior Factors, Sina Darban Khales

Dissertations

Providing a safe and efficient transportation system is the primary goal of transportation engineering and planning. Highway crashes are among the most significant challenges to achieving this goal. They result in significant societal toll reflected in numerous fatalities, personal injuries, property damage, and traffic congestion. To that end, much attention has been given to predictive models of crash occurrence and severity. Most of these models are reactive: they use the data about crashes that have occurred in the past to identify the significant crash factors, crash hot-spots and crash-prone roadway locations, analyze and select the most effective countermeasures for reducing …

Go to article

Signal Processing And Data Analysis For Real-Time Intermodal Freight Classification Through A Multimodal Sensor System., Enrique J. Sanchez Headley Jul 2021

Signal Processing And Data Analysis For Real-Time Intermodal Freight Classification Through A Multimodal Sensor System., Enrique J. Sanchez Headley

Graduate Theses and Dissertations

Identifying freight patterns in transit is a common need among commercial and municipal entities. For example, the allocation of resources among Departments of Transportation is often predicated on an understanding of freight patterns along major highways. There exist multiple sensor systems to detect and count vehicles at areas of interest. Many of these sensors are limited in their ability to detect more specific features of vehicles in traffic or are unable to perform well in adverse weather conditions. Despite this limitation, to date there is little comparative analysis among Laser Imaging and Detection and Ranging (LIDAR) sensors for freight detection …

Go to article

Data Mining Of Unstructured Textual Information In Transportation Safety Domain: Exploring Methods, Opportunities And Limitations, Keneth Morgan Kwayu Jun 2021

Data Mining Of Unstructured Textual Information In Transportation Safety Domain: Exploring Methods, Opportunities And Limitations, Keneth Morgan Kwayu

Dissertations

The unprecedented increase in volume and influx of structured and unstructured data has overwhelmed conventional data management system capabilities in organizing, analyzing, and procuring useful information in a timely fashion. Structured data sources have a pre-defined pattern that makes data preprocessing and information retrieval tasks relatively easy for the current technologies that have been designed to handle structured and repeatable data. Unlike structured data, unstructured data usually exists in an unorganized format that offers no or little insight unless indexed and stored in an organized fashion. The inherent format of unstructured data exacerbates difficulties in data preprocessing and information extraction. …

Go to article

Uavs For Vegetation Monitoring: Overview And Recent Scientific Contributions, Ana I. De Castro, Yeyin Shi, Joe Mari Maja, Jose M. Peña May 2021

Uavs For Vegetation Monitoring: Overview And Recent Scientific Contributions, Ana I. De Castro, Yeyin Shi, Joe Mari Maja, Jose M. Peña

Department of Agricultural and Biological Systems Engineering: Faculty Publications

This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural and forestry scenarios. Three general categories were considered: (1) sensors and vegetation indices used, (2) technological goals pursued, and (3) agroforestry applications. Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing relevant vegetation features (mainly canopy cover and crop height) or for detecting different plant/crop stressors, such as nutrient content/deficiencies, water …

Go to article

First Prev Next Last