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(1 - 14 of 14)
- Title
- IMPROVING LONG RANGE FORECAST ERRORS FOR BETTER CAPACITY DECISION MAKING.
- Creator
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Nizam, Anisulrahman, Leon, Steven, University of Central Florida
- Abstract / Description
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Long-range demand planning and capacity management play an important role for policy makers and airline managers alike. Each makes decisions regarding allocating appropriate levels of funds to align capacity with forecasted demand. Decisions today can have long lasting effects. Reducing forecast errors for long-range range demand forecasting will improve resource allocation decision making. This research paper will focus on improving long-range demand planning and forecasting errors of...
Show moreLong-range demand planning and capacity management play an important role for policy makers and airline managers alike. Each makes decisions regarding allocating appropriate levels of funds to align capacity with forecasted demand. Decisions today can have long lasting effects. Reducing forecast errors for long-range range demand forecasting will improve resource allocation decision making. This research paper will focus on improving long-range demand planning and forecasting errors of passenger traffic in the U.S. domestic airline industry. This paper will look to build upon current forecasting models being used for U.S. domestic airline passenger traffic with the aim of improving forecast errors published by Federal Aviation Administration (FAA). Using historical data, this study will retroactively forecast U.S. domestic passenger traffic and then compare it to actual passenger traffic, then comparing forecast errors. Forecasting methods will be tested extensively in order to identify new trends and causal factors that will enhance forecast accuracy thus increasing the likelihood of better capacity management and funding decisions.
Show less - Date Issued
- 2013
- Identifier
- CFH0004425, ucf:45115
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH0004425
- Title
- FORECASTING THE ONSET OF CLOUD-GROUND LIGHTNING USING S-POL AND NLDN DATA.
- Creator
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Ramakrishnan, Kartik, Kasparis, Takis, University of Central Florida
- Abstract / Description
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The maximum number of thunderstorms in the United States occur in Central Florida. The cloud-ground lightning from these storms is responsible for extensive damage to life and property. The lightning from these storms is also responsible for delays and cancellations of space shuttle launch attempts at the Kennedy Space Center (KSC) and the 45th Space Wing unmanned launches at Cape Canaveral launch facilities. For these and other reasons accurate forecasting of cloud-ground lightning is of...
Show moreThe maximum number of thunderstorms in the United States occur in Central Florida. The cloud-ground lightning from these storms is responsible for extensive damage to life and property. The lightning from these storms is also responsible for delays and cancellations of space shuttle launch attempts at the Kennedy Space Center (KSC) and the 45th Space Wing unmanned launches at Cape Canaveral launch facilities. For these and other reasons accurate forecasting of cloud-ground lightning is of crucial importance.The second phase of NASA's Tropical Rainfall Measuring Mission Texas and Florida Underflights project (TEFLUN-B) was conducted between 1st August and 30th September, 1998. The S-band dual-polarization radar (S-Pol) belonging to the National Center for Atmospheric Research (NCAR) was part of the surface based facilities during this project, and was located at Melbourne, Florida. This provided an excellent opportunity to observe Florida thunderstorms with the help of a dual-polarization radar.This project aims at developing cloud-ground lightning forecasting signatures by analyzing S-Pol data for 10 thunderstorms that occurred over the Kennedy Space Center. Time-height trends of reflectivity, ice and graupel-hail as well as electric potential trends for these storms are taken into consideration while developing the forecasting signatures. This thesis proposes that a 35dBZ echo at the -5oC temperature level is the best indicator of imminent CG lightning with a POD of 90%, an FAR of 10% and a CSI of 81.8%. An electric potential level of approximately 1000 V/m also indicates the onset of cloud-ground lightning. An analysis of the microphysical structure of the thunderstorms reveals that the presence of graupel-hail at the -10oC temperature level is necessary in order for cloud-ground lightning to occur.
Show less - Date Issued
- 2004
- Identifier
- CFE0000143, ucf:46168
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000143
- Title
- MULTISENSOR FUSION REMOTE SENSING TECHNOLOGY FOR ASSESSING MULTITEMPORAL RESPONSES IN ECOHYDROLOGICAL SYSTEMS.
- Creator
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Makkeasorn, Ammarin, Chang, Ni-Bin, University of Central Florida
- Abstract / Description
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Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements...
Show moreEarth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing–based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction.
Show less - Date Issued
- 2007
- Identifier
- CFE0001767, ucf:47267
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001767
- Title
- CLIMATE MODELING, OUTGOING LONGWAVE RADIATION, AND TROPICAL CYCLONE FORECASTING.
- Creator
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Rechtman, Thomas, Mohapatra, Ram N., University of Central Florida
- Abstract / Description
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Climate modeling and tropical cyclone forecasting are two significant issues that are continuously being improved upon for more accurate weather forecasting and preparedness. In this thesis, we have studied three climate models and formulated a new model with a view to determine the outgoing longwave radiation (OLR) budget at the top of the atmosphere (TOA) as observed by the National Oceanic and Atmospheric Administration's (NOAA) satellite based Advanced Very High Resolution Radiometer ...
Show moreClimate modeling and tropical cyclone forecasting are two significant issues that are continuously being improved upon for more accurate weather forecasting and preparedness. In this thesis, we have studied three climate models and formulated a new model with a view to determine the outgoing longwave radiation (OLR) budget at the top of the atmosphere (TOA) as observed by the National Oceanic and Atmospheric Administration's (NOAA) satellite based Advanced Very High Resolution Radiometer (AVHRR). In 2006, Karnauskas proposed the African meridional OLR as an Atlantic hurricane predictor, the relation was further proven in 2016 by Karnauskas and Li. Here we have considered a similar study for all other tropical cyclone basins.
Show less - Date Issued
- 2018
- Identifier
- CFH2000403, ucf:45775
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000403
- Title
- Superforecasting or SNAFU: The Forecasting Ability of the US Military Officer.
- Creator
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Raugh, David, Handberg, Roger, Dolan, Thomas, Powell, Jonathan, Gannon, Barbara, University of Central Florida
- Abstract / Description
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What is the impact of military institutional tendencies and habits on U.S. Army senior officer forecasting accuracy and how does this forecasting ability shape success in battle? Military leaders plan operations based on the forecasted strengths and vulnerabilities of their adversary. Negative habits, such as limited option development, confirmation bias, doctrinal overreliance, and over-consideration of sunk costs, inhibit effective forecasting. The tempo of the modern battlefield,...
Show moreWhat is the impact of military institutional tendencies and habits on U.S. Army senior officer forecasting accuracy and how does this forecasting ability shape success in battle? Military leaders plan operations based on the forecasted strengths and vulnerabilities of their adversary. Negative habits, such as limited option development, confirmation bias, doctrinal overreliance, and over-consideration of sunk costs, inhibit effective forecasting. The tempo of the modern battlefield, hierarchical culture, and institutional tendencies of the US Army may promote and reinforce these habits. I surveyed Colonels in US Army War College programs to measure their individual tendencies, levels of education, and accuracy in forecasting events during a three to twelve-month future. Quantitative analysis of the resulting data shows that these habits are present and negatively affect forecasting ability; additionally, higher levels of education positively affect forecast accuracy, possibly counteracting the effects of negative institutional tendencies and habits. Extending the research using historical and contemporary case studies of senior US Army Generals, including interviews of General David Petraeus and other high-ranking officials, I find that rejection of these institutional habits and tendencies enabled superior forecasting, leading to battlefield success. I conclude by examining how educational levels of commanding generals in the Iraq War affected military success. Exploratory quantitative analysis of data collected from the US Army historical archives shows that higher levels of education positively affected significant activities within the general's assigned areas.
Show less - Date Issued
- 2019
- Identifier
- CFE0007519, ucf:52609
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007519
- Title
- Forecasting Volcanic Activity Using An Event Tree Analysis System and Logistic Regression.
- Creator
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Junek, William, Jones, W, Simaan, Marwan, Foroosh, Hassan, Woods, Mark, University of Central Florida
- Abstract / Description
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Forecasts of short term volcanic activity are generated using an event tree process that is driven by a set of empirical statistical models derived through logistic regression. Each of the logistic models are constructed from a sparse and geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. seismic), source modeling results, and historic eruption information. Incorporating this data into...
Show moreForecasts of short term volcanic activity are generated using an event tree process that is driven by a set of empirical statistical models derived through logistic regression. Each of the logistic models are constructed from a sparse and geographically diverse dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. seismic), source modeling results, and historic eruption information. Incorporating this data into a single set of models provides a simple mechanism for simultaneously accounting for the geophysical changes occurring within the volcano and the historic behavior of analog volcanoes. A bootstrapping analysis of the training dataset allowed for the estimation of robust logistic model coefficients. Probabilities generated from the logistic models increase with positive modeling results, escalating seismicity, and high eruption frequency. The cross validation process produced a series of receiver operating characteristic (ROC) curves with areas ranging between 0.78 - 0.81, which indicate the algorithm has good predictive capabilities. In addition, ROC curves also allowed for the determination of a false positive rate and optimum detection threshold for each stage of the algorithm. The results demonstrate the logistic models are highly transportable and can compete with, and in some cases outperform, non-transportable empirical models trained with site specific information. The incorporation of source modeling results into the event tree's decision making process has begun the transition of volcano monitoring applications from simple mechanized pattern recognition algorithms to a physical model based forecasting system.
Show less - Date Issued
- 2012
- Identifier
- CFE0004253, ucf:49517
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004253
- Title
- Data-driven Predictive Analytics For Distributed Smart Grid Control: Optimization of Energy Storage, Voltage and Demand Response.
- Creator
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Valizadehhaghi, Hamed, Qu, Zhihua, Behal, Aman, Atia, George, Turgut, Damla, Pensky, Marianna, University of Central Florida
- Abstract / Description
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The smart grid is expected to support an interconnected network of self-contained microgrids. Nonetheless, the distributed integration of renewable generation and demand response adds complexity to the control and optimization of smart grid. Forecasts are essential due to the existence of stochastic variations and uncertainty. Forecasting data are spatio-temporal which means that the data correspond to regular intervals, say every hour, and the analysis has to take account of spatial...
Show moreThe smart grid is expected to support an interconnected network of self-contained microgrids. Nonetheless, the distributed integration of renewable generation and demand response adds complexity to the control and optimization of smart grid. Forecasts are essential due to the existence of stochastic variations and uncertainty. Forecasting data are spatio-temporal which means that the data correspond to regular intervals, say every hour, and the analysis has to take account of spatial dependence among the distributed generators or locations. Hence, smart grid operations must take account of, and in fact benefit from the temporal dependence as well as the spatial dependence. This is particularly important considering the buffering effect of energy storage devices such as batteries, heating/cooling systems and electric vehicles. The data infrastructure of smart grid is the key to address these challenges, however, how to utilize stochastic modeling and forecasting tools for optimal and reliable planning, operation and control of smart grid remains an open issue.Utilities are seeking to become more proactive in decision-making, adjusting their strategies based on realistic predictive views into the future, thus allowing them to side-step problems and capitalize on the smart grid technologies, such as energy storage, that are now being deployed atscale. Predictive analytics, capable of managing intermittent loads, renewables, rapidly changing weather patterns and other grid conditions, represent the ultimate goal for smart grid capabilities.Within this framework, this dissertation develops high-performance analytics, such as predictive analytics, and ways of employing analytics to improve distributed and cooperative optimization software which proves to be the most significant value-add in the smart grid age, as new network management technologies prove reliable and fundamental. Proposed optimization and control approaches for active and reactive power control are robust to variations and offer a certain level of optimality by combining real-time control with hours-ahead network operation schemes. The main objective is managing spatial and temporal availability of the energy resources in different look-ahead time horizons. Stochastic distributed optimization is realized by integrating a distributed sub-gradient method with conditional ensemble predictions of the energy storage capacity and distributed generation. Hence, the obtained solutions can reflect on the system requirements for the upcoming times along with the instantaneous cooperation between distributed resources. As an important issue for smart grid, the conditional ensembles are studied for capturing wind, photovoltaic, and vehicle-to-grid availability variations. The following objectives are pursued:- Spatio-temporal adaptive modeling of data including electricity demand, electric vehicles and renewable energy (wind and solar power)- Predictive data analytics and forecasting- Distributed control- Integration of energy storage systemsFull distributional characterization and spatio-temporal modeling of data ensembles are utilized in order to retain the conditional and temporal interdependence between projection data and available capacity. Then, by imposing measures of the most likely ensembles, the distributed control method is carried out for cooperative optimization of the renewable generation and energy storage within the smart grid.
Show less - Date Issued
- 2016
- Identifier
- CFE0006408, ucf:51481
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006408
- Title
- Re-Thinking the Intentionality of Fraud: Constructing and Testing the Theory of Unintended Amoral Behavior to Explain Fraudulent Financial Reporting.
- Creator
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Dill, Andrew, Sutton, Steven, Arnold, Vicky, Schmitt, Donna, Schminke, Marshall, University of Central Florida
- Abstract / Description
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My three-paper dissertation is aimed at applying the concepts of bounded ethicality and ethical fading to accounting fraud. Typical of relatively new fields such as behavioral ethics, theoretical models are scarce (Tenbrunsel (&) Smith-Crowe, 2008). As such, the purpose of Study 1 is to unify disparate theories and ideas from psychology and behavioral ethics as a means of constructing a theory, the Theory of Unintended Amoral Behavior (TUAB), which includes the concepts of bounded ethicality...
Show moreMy three-paper dissertation is aimed at applying the concepts of bounded ethicality and ethical fading to accounting fraud. Typical of relatively new fields such as behavioral ethics, theoretical models are scarce (Tenbrunsel (&) Smith-Crowe, 2008). As such, the purpose of Study 1 is to unify disparate theories and ideas from psychology and behavioral ethics as a means of constructing a theory, the Theory of Unintended Amoral Behavior (TUAB), which includes the concepts of bounded ethicality and ethical fading. In addition, the pressure for management to meet earnings expectations is discussed through the lens of the TUAB as an example of how one may unknowingly misreport.Studies 2 and 3 apply the TUAB to investigate how certain contextual factors interact with egocentric biases to increase the likelihood of ethical fading. Specifically, Study 2 consists of an experiment exploring how inferior pay among managers interacts with egocentric perceptions of fairness and envy to affect the likelihood of one engaging in ethical fading and fraudulent behavior. Study 3 also utilizes an experimental methodology to examine how the pressure to meet earnings forecasts interacts with egocentric perceptions of fairness and negative affect to influence the probability of ethical fading and fraudulent acts.The results for Study 2 indicate that one who is paid at a lower rate is more likely to view this disparity as unfair, which leads to a greater feeling of envy. Although envy had no significant direct effect on ethical fading in the primary analyses, a supplemental analysis revealed that a person's risk preference might moderate this relationship. The primary findings of Study 2 suggest that individuals who experience a higher degree of ethical fading are more likely to commit fraud, and that ethical fading, along with perceived unfairness, seem to be significant psychological processes that explain how differences in pay may lead to fraud. The primary finding of Study 3 is that, like Study 2, fraud is more likely to occur as an individual experiences a higher degree of ethical fading. Furthermore, this study suggests that those who are closest to meeting an earnings target are the most likely to engage in fraudulent behavior. Finally, the results failed to find any support that one's egocentric perceptions of fairness and negative affect contribute towards his or her ethical behavior in a goal achievement setting. The primary contributions of this dissertation is that it unifies various theories and ideas from psychology and behavioral ethics to establish a testable theory (TUAB) that includes the concepts of bounded ethicality and ethical fading, serves as an initial test of TUAB, and provides evidence that unethical behavior is not necessarily the result of one consciously forsaking his or her ethics for some other desired goal (i.e., profit).
Show less - Date Issued
- 2016
- Identifier
- CFE0006097, ucf:51211
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006097
- Title
- Drinking Water Infrastructure Assessment with Teleconnection Signals, Satellite Data Fusion and Mining.
- Creator
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Imen, Sanaz, Chang, Ni-bin, Wang, Dingbao, Wanielista, Martin, Bohlen, Patrick, University of Central Florida
- Abstract / Description
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Adjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region - Las...
Show moreAdjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region - Las Vegas, Nevada. The water quality in Lake Mead is impaired by forest fires, soil erosion, and land use changes in nearby watersheds and wastewater effluents from the Las Vegas Wash. In addition, more than a decade of drought has caused a sharp drop by about 100 feet in the elevation of Lake Mead. These hydrological processes in the drought event led to the increased concentration of total organic carbon (TOC) and total suspended solids (TSS) in the lake. TOC in surface water is known as a precursor of disinfection byproducts in drinking water, and high TSS concentration in source water is a threat leading to possible clogging in the water treatment process. Since Lake Mead is a principal source of drinking water for over 25 million people, high concentrations of TOC and TSS may have a potential health impact. Therefore, it is crucial to develop an early warning system which is able to support rapid forecasting of water quality and availability. In this study, the creation of the nowcasting water quality model with satellite remote sensing technologies lays down the foundation for monitoring TSS and TOC, on a near real-time basis. Yet the novelty of this study lies in the development of a forecasting model to predict TOC and TSS values with the aid of remote sensing technologies on a daily basis. The forecasting process is aided by an iterative scheme via updating the daily satellite imagery in concert with retrieving the long-term memory from the past states with the aid of nonlinear autoregressive neural network with external input on a rolling basis onward. To account for the potential impact of long-term hydrological droughts, teleconnection signals were included on a seasonal basis in the Upper Colorado River basin which provides 97% of the inflow into Lake Mead. Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. Empirical mode decomposition as well as wavelet analysis are utilized to extract the intrinsic trend and the dominant oscillation of the sea surface temperature (SST) and precipitation time series. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant index regions in the oceans are extracted. With these characterized associations, individual contribution of these SST forcing regions that are linked to the related precipitation responses are further quantified through the use of the extreme learning machine. Results indicate that the non-leading SST regions also contribute saliently to the terrestrial precipitation variability compared to some of the known leading SST regions and confirm the capability of predicting the hydrological drought events one season ahead of time. With such an integrated advancement, an early warning system can be constructed to bridge the current gap in source water monitoring for water supply.
Show less - Date Issued
- 2015
- Identifier
- CFE0005632, ucf:50215
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005632
- Title
- Integrated Remote Sensing and Forecasting of Regional Terrestrial Precipitation with Global Nonlinear and Nonstationary Teleconnection Signals Using Wavelet Analysis.
- Creator
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Mullon, Lee, Chang, Ni-bin, Wang, Dingbao, Wanielista, Martin, University of Central Florida
- Abstract / Description
-
Global sea surface temperature (SST) anomalies have a demonstrable effect on terrestrial climate dynamics throughout the continental U.S. SST variations have been correlated with greenness (vegetation densities) and precipitation via ocean-atmospheric interactions known as climate teleconnections. Prior research has demonstrated that teleconnections can be used for climate prediction across a wide region at sub-continental scales. Yet these studies tend to have large uncertainties in...
Show moreGlobal sea surface temperature (SST) anomalies have a demonstrable effect on terrestrial climate dynamics throughout the continental U.S. SST variations have been correlated with greenness (vegetation densities) and precipitation via ocean-atmospheric interactions known as climate teleconnections. Prior research has demonstrated that teleconnections can be used for climate prediction across a wide region at sub-continental scales. Yet these studies tend to have large uncertainties in estimates by utilizing simple linear analyses to examine chaotic teleconnection relationships. Still, non-stationary signals exist, making teleconnection identification difficult at the local scale. Part 1 of this research establishes short-term (10-year), linear and non-stationary teleconnection signals between SST at the North Atlantic and North Pacific oceans and terrestrial responses of greenness and precipitation along multiple pristine sites in the northeastern U.S., including (1) White Mountain National Forest (-) Pemigewasset Wilderness, (2) Green Mountain National Forest (-) Lye Brook Wilderness and (3) Adirondack State Park (-) Siamese Ponds Wilderness. Each site was selected to avoid anthropogenic influences that may otherwise mask climate teleconnection signals. Lagged pixel-wise linear teleconnection patterns across anomalous datasets found significant correlation regions between SST and the terrestrial sites. Non-stationary signals also exhibit salient co-variations at biennial and triennial frequencies between terrestrial responses and SST anomalies across oceanic regions in agreement with the El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) signals. Multiple regression analysis of the combined ocean indices explained up to 50% of the greenness and 42% of the precipitation in the study sites. The identified short-term teleconnection signals improve the understanding and projection of climate change impacts at local scales, as well as harness the interannual periodicity information for future climate projections. Part 2 of this research paper builds upon the earlier short-term study by exploring a long-term (30-year) teleconnection signal investigation between SST at the North Atlantic and Pacific oceans and the precipitation within Adirondack State Park in upstate New York. Non-traditional teleconnection signals are identified using wavelet decomposition and teleconnection mapping specific to the Adirondack region. Unique SST indices are extracted and used as input variables in an artificial neural network (ANN) prediction model. The results show the importance of considering non-leading teleconnection patterns as well as the known teleconnection patterns. Additionally, the effects of the Pacific Ocean SST or the Atlantic Ocean SST on terrestrial precipitation in the study region were compared with each other to deepen the insight of sea-land interactions. Results demonstrate reasonable prediction skill at forecasting precipitation trends with a lead time of one month, with r values of 0.6. The results are compared against a statistical downscaling approach using the HadCM3 global circulation model output data and the SDSM statistical downscaling software, which demonstrate less predictive skill at forecasting precipitation within the Adirondacks.
Show less - Date Issued
- 2014
- Identifier
- CFE0005535, ucf:50319
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005535
- Title
- A GASOLINE DEMAND MODEL FOR THE UNITED STATES LIGHT VEHICLE FLEET.
- Creator
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Rey, Diana, Al-Deek, Haitham, University of Central Florida
- Abstract / Description
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ABSTRACT The United States is the world's largest oil consumer demanding about twenty five percent of the total world oil production. Whenever there are difficulties to supply the increasing quantities of oil demanded by the market, the price of oil escalates leading to what is known as oil price spikes or oil price shocks. The last oil price shock which was the longest sustained oil price run up in history, began its course in year 2004, and ended in 2008. This last oil price shock...
Show moreABSTRACT The United States is the world's largest oil consumer demanding about twenty five percent of the total world oil production. Whenever there are difficulties to supply the increasing quantities of oil demanded by the market, the price of oil escalates leading to what is known as oil price spikes or oil price shocks. The last oil price shock which was the longest sustained oil price run up in history, began its course in year 2004, and ended in 2008. This last oil price shock initiated recognizable changes in transportation dynamics: transit operators realized that commuters switched to transit as a way to save gasoline costs, consumers began to search the market for more efficient vehicles leading car manufactures to close "gas guzzlers" plants, and the government enacted a new law entitled the Energy Independence Act of 2007, which called for the progressive improvement of the fuel efficiency indicator of the light vehicle fleet up to 35 miles per gallon in year 2020. The past trend of gasoline consumption will probably change; so in the context of the problem a gasoline consumption model was developed in this thesis to ascertain how some of the changes will impact future gasoline demand. Gasoline demand was expressed in oil equivalent million barrels per day, in a two steps Ordinary Least Square (OLS) explanatory variable model. In the first step, vehicle miles traveled expressed in trillion vehicle miles was regressed on the independent variables: vehicles expressed in million vehicles, and price of oil expressed in dollars per barrel. In the second step, the fuel consumption in million barrels per day was regressed on vehicle miles traveled, and on the fuel efficiency indicator expressed in miles per gallon. The explanatory model was run in EVIEWS that allows checking for normality, heteroskedasticty, and serial correlation. Serial correlation was addressed by inclusion of autoregressive or moving average error correction terms. Multicollinearity was solved by first differencing. The 36 year sample series set (1970-2006) was divided into a 30 years sub-period for calibration and a 6 year "hold-out" sub-period for validation. The Root Mean Square Error or RMSE criterion was adopted to select the "best model" among other possible choices, although other criteria were also recorded. Three scenarios for the size of the light vehicle fleet in a forecasting period up to 2020 were created. These scenarios were equivalent to growth rates of 2.1, 1.28, and about 1 per cent per year. The last or more optimistic vehicle growth scenario, from the gasoline consumption perspective, appeared consistent with the theory of vehicle saturation. One scenario for the average miles per gallon indicator was created for each one of the size of fleet indicators by distributing the fleet every year assuming a 7 percent replacement rate. Three scenarios for the price of oil were also created: the first one used the average price of oil in the sample since 1970, the second was obtained by extending the price trend by exponential smoothing, and the third one used a longtime forecast supplied by the Energy Information Administration. The three scenarios created for the price of oil covered a range between a low of about 42 dollars per barrel to highs in the low 100's. The 1970-2006 gasoline consumption trend was extended to year 2020 by ARIMA Box-Jenkins time series analysis, leading to a gasoline consumption value of about 10 millions barrels per day in year 2020. This trend line was taken as the reference or baseline of gasoline consumption. The savings that resulted by application of the explanatory variable OLS model were measured against such a baseline of gasoline consumption. Even on the most pessimistic scenario the savings obtained by the progressive improvement of the fuel efficiency indicator seem enough to offset the increase in consumption that otherwise would have occurred by extension of the trend, leaving consumption at the 2006 levels or about 9 million barrels per day. The most optimistic scenario led to savings up to about 2 million barrels per day below the 2006 level or about 3 millions barrels per day below the baseline in 2020. The "expected" or average consumption in 2020 is about 8 million barrels per day, 2 million barrels below the baseline or 1 million below the 2006 consumption level. More savings are possible if technologies such as plug-in hybrids that have been already implemented in other countries take over soon, are efficiently promoted, or are given incentives or subsidies such as tax credits. The savings in gasoline consumption may in the future contribute to stabilize the price of oil as worldwide demand is tamed by oil saving policy changes implemented in the United States.
Show less - Date Issued
- 2009
- Identifier
- CFE0002539, ucf:47659
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002539
- Title
- Fusing Freight Analysis Framework and Transearch Data: An Econometric Data Fusion Approach.
- Creator
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Momtaz, Salah Uddin, Eluru, Naveen, Abdel-Aty, Mohamed, Anowar, Sabreena, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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A major hurdle in freight demand modeling has always been the lack of adequate data on freight movements for different industry sectors for planning applications. Freight Analysis Framework (FAF), and Transearch (TS) databases contain annualized commodity flow data. The primary motivation for our study is the development of a fused database from FAF and TS to realize transportation network flows at a fine spatial resolution (county-level) while accommodating for production and consumption...
Show moreA major hurdle in freight demand modeling has always been the lack of adequate data on freight movements for different industry sectors for planning applications. Freight Analysis Framework (FAF), and Transearch (TS) databases contain annualized commodity flow data. The primary motivation for our study is the development of a fused database from FAF and TS to realize transportation network flows at a fine spatial resolution (county-level) while accommodating for production and consumption behavioral trends (provided by TS). Towards this end, we formulate and estimate a joint econometric model framework grounded in maximum likelihood approach to estimate county-level commodity flows. The algorithm is implemented for the commodity flow information from 2012 FAF and 2011 TS databases to generate transportation network flows for 67 counties in Florida. The data fusion process considers several exogenous variables including origin-destination indicator variables, socio-demographic and socio-economic indicators, and transportation infrastructure indicators. Subsequently, the algorithm is implemented to develop freight flows for the Florida region considering inflows and outflows across the US and neighboring countries. The base year models developed are employed to predict future year data for years 2015 through 2040 in 5-year increments at the same spatial level. Furthermore, we disaggregate the county level flows obtained from algorithm to a finer resolution - statewide transportation analysis zone (SWTAZ) defined by the FDOT. The disaggregation process allocates truck-based commodity flows from a 79-zone system to an 8835-zone system. A two-stage factor multiplication method is proposed to disaggregate the county flow to SWTAZ flow. The factors are estimated both at the origin and destination level using a random utility factional split model approach. Eventually, we conducted a sensitivity analysis of the parameterization by evaluating the model structure for different numbers of intermediate stops in a route and/or the number of available routes for the origin-destinations.
Show less - Date Issued
- 2018
- Identifier
- CFE0007763, ucf:52384
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007763
- Title
- A Comparative Evaluation of FDSA,GA, and SA Non-Linear Programming Algorithms and Development of System-Optimal Dynamic Congestion Pricing Methodology on I-95 Express.
- Creator
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Graham, Don, Radwan, Ahmed, Abdel-Aty, Mohamed, Al-Deek, Haitham, Uddin, Nizam, University of Central Florida
- Abstract / Description
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As urban population across the globe increases, the demand for adequatetransportation grows. Several strategies have been suggested as a solution to the congestion which results from this high demand outpacing the existing supply of transportation facilities.High (-)Occupancy Toll (HOT) lanes have become increasingly more popular as a feature on today's highway system. The I-95 Express HOT lane in Miami Florida, which is currently being expanded from a single Phase (Phase I) into two Phases,...
Show moreAs urban population across the globe increases, the demand for adequatetransportation grows. Several strategies have been suggested as a solution to the congestion which results from this high demand outpacing the existing supply of transportation facilities.High (-)Occupancy Toll (HOT) lanes have become increasingly more popular as a feature on today's highway system. The I-95 Express HOT lane in Miami Florida, which is currently being expanded from a single Phase (Phase I) into two Phases, is one such HOT facility. With the growing abundance of such facilities comes the need for in- depth study of demand patterns and development of an appropriate pricing scheme which reduces congestion.This research develops a method for dynamic pricing on the I-95 HOT facility such as to minimize total travel time and reduce congestion. We apply non-linear programming (NLP) techniques and the finite difference stochastic approximation (FDSA), genetic algorithm (GA) and simulated annealing (SA) stochastic algorithms to formulate and solve the problem within a cell transmission framework. The solution produced is the optimal flow and optimal toll required to minimize total travel time and thus is the system-optimal solution.We perform a comparative evaluation of FDSA, GA and SA non-linear programmingalgorithms used to solve the NLP and the ANOVA results show that there are differences in the performance of the NLP algorithms in solving this problem and reducing travel time. We then conclude by demonstrating that econometric forecasting methods utilizing vector autoregressive (VAR) techniques can be applied to successfully forecast demand for Phase 2 of the 95 Express which is planned for 2014.
Show less - Date Issued
- 2013
- Identifier
- CFE0005000, ucf:50019
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005000
- Title
- MEASURING THE EFFECT OF ERRATIC DEMANDON SIMULATED MULTI-CHANNEL MANUFACTURINGSYSTEM PERFORMANCE.
- Creator
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Kohan, Nancy, Kulonda, Dennis, University of Central Florida
- Abstract / Description
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ABSTRACT To handle uncertainties and variabilities in production demands, many manufacturing companies have adopted different strategies, such as varying quoted lead time, rejecting orders, increasing stock or inventory levels, and implementing volume flexibility. Make-to-stock (MTS) systems are designed to offer zero lead time by providing an inventory buffer for the organizations, but they are costly and involve risks such as obsolescence and wasted expenditures. The main concern of make-to...
Show moreABSTRACT To handle uncertainties and variabilities in production demands, many manufacturing companies have adopted different strategies, such as varying quoted lead time, rejecting orders, increasing stock or inventory levels, and implementing volume flexibility. Make-to-stock (MTS) systems are designed to offer zero lead time by providing an inventory buffer for the organizations, but they are costly and involve risks such as obsolescence and wasted expenditures. The main concern of make-to-order (MTO) systems is eliminating inventories and reducing the non-value-added processes and wastes; however, these systems are based on the assumption that the manufacturing environments and customers' demand are deterministic. Research shows that in MTO systems variability and uncertainty in the demand levels causes instability in the production flow, resulting in congestion in the production flow, long lead times, and low throughput. Neither strategy is wholly satisfactory. A new alternative approach, multi-channel manufacturing (MCM) systems are designed to manage uncertainties and variabilities in demands by first focusing on customers' response time. The products are divided into different product families, each with its own manufacturing stream or sub-factory. MCM also allocates the production capacity needed in each sub-factory to produce each product family. In this research, the performance of an MCM system is studied by implementing MCM in a real case scenario from textile industry modeled via discrete event simulation. MTS and MTO systems are implemented for the same case scenario and the results are studied and compared. The variables of interest for this research are the throughput of products, the level of on-time deliveries, and the inventory level. The results conducted from the simulation experiments favor the simulated MCM system for all mentioned criteria. Further research activities, such as applying MCM to different manufacturing contexts, is highly recommended.
Show less - Date Issued
- 2004
- Identifier
- CFE0000240, ucf:46275
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000240
