Carbon accreditation of forest development projects is essential for sequestering atmospheric CO2 under the provisions of the Kyoto Protocol. The carbon sequestration potential of surface coal-mined lands is not well known. The purpose of this work was to determine how to measure carbon sequestration and estimate the additional amount that could be sequestered using different reforestation methods compared to the common practice of establishing grasslands. I developed a thermal oxidation technique for differentiating sequestered soil carbon from inorganic and fossilized carbon found at high levels in mine soils along with a geospatial and statistical protocol for carbon monitoring and accounting. I used existing tree, litter, and soil carbon data for 14 mined and 8 adjacent, non-mined forests in the Midwestern and Eastern coal regions to determine, and model sequestered carbon across the spectrum of site index and stand age in pine, mixed, and hardwood forest stands. Finally, I developed the framework of a decision support system consisting of the first iteration of a dynamic model to predict carbon sequestration for a 60-year period for three forest types (white pine, hybrid poplar, and native hardwoods) at three levels of management intensity: low (weed control), medium (weed control and tillage) and high (weed control, tillage, and fertilization). On average, the highest amount of ecosystem carbon on mined land was sequestered by pine stands (148 Mg ha-1), followed by hardwood (130 Mg ha-1) and mixed stands (118 Mg ha-1). Non-mined hardwood stands contained 210 Mg C ha-1, which was about 62% higher than the average of all mined stands. After 60 years, the net carbon in ecosystem components, wood products, and landfills ranged from 20 to 235 Mg ha-1 among all scenarios. The highest net amount of carbon was estimated under mixed hardwood vegetation established by the highest intensity treatment. Under this scenario, a surface-mined land of average site quality would sequester net carbon stock at 235 Mg C ha-1, at a rate of 3.9 Mg C ha-1 yr-1, which was 100% greater than a grassland scenario. Reforestation is a logical choice for mined land reclamation if carbon sequestration is a management objective. / Ph. D.

The thesis aims to introduce an efficient resource development strategy in electric utility long term planning under uncertainty considerations. In recent years, electric utilities have recognized the concepts of robustness, flexibility, and risk exposure, to be considered in their resource development strategy. The concept of robustness means to develop resource plans that can perform well for most, if not all futures, while flexibility is to allow inexpensive changes to be made if the future conditions deviate from the base assumptions. A risk exposure concept is used to quantify the risk hazards in planning alternatives for different kinds of future conditions. This study focuses on two technical issues identified to be important to the process of efficient resource development: decision-making analysis considering robustness and flexibility, and decision-making analysis considering risk exposure. The technique combines probabilistic methods and tradeoff analysis, thereby producing a decision set analysis concept to determine robustness that includes flexibility measures. In addition, risk impact analysis is incorporated to identify the risk exposure in planning alternatives. Contributions of the work are summarized as follows. First, an efficient resource development framework for planning under uncertainty is developed that combines features of utility function, tradeoff analysis, and the analytical hierarchy process, incorporating a performance evaluation approach. Second, the multi-attribute risk-impact analysis method is investigated to handle the risk hazards exposed in power system resource planning. Third, the penetration levels of wind and photovoltaic generation technologies into the total generation system mix, with their constraints, are determined using the decision-making model. The results from two case studies show the benefits of the proposed framework by offering the decision makers various options for lower cost, lower emission, better reliability, and higher efficiency plans. / Ph. D.

Few current modeling tools are designed to predict short-term, high-risk runoff from critical source areas (CSAs) in watersheds which are significant sources of non point source (NPS) pollution. This study couples the Soil and Water Assessment Tool-Variable Source Area (SWAT-VSA) model with the Climate Forecast System Reanalysis (CFSR) model and the Global Forecast System (GFS) model short-term weather forecast, to develop a CSA prediction tool designed to assist producers, landowners, and planners in identifying high-risk areas generating storm runoff and pollution. Short-term predictions for streamflow, runoff probability, and soil moisture levels were estimated in the South Fork of the Shenandoah river watershed in Virginia. In order to allow land managers access to the CSA predictions a free and open source software based web was developed. The forecast system consists of three primary components; (1) the model, which preprocesses the necessary hydrologic forcings, runs the watershed model, and outputs spatially distributed VSA forecasts; (2) a data management structure, which converts high resolution rasters into overlay web map tiles; and (3) the user interface component, a web page that allows the user, to interact with the processed output. The resulting framework satisfied most design requirements with free and open source software and scored better than similar tools in usability metrics. One of the potential problems is that the CSA model, utilizing physically based modeling techniques requires significant computational time to execute and process. Thus, as an alternative, a deep learning (DL) model was developed and trained on the process based model output. The DL model resulted in a 9% increase in predictive power compared to the physically based model and a ten-fold decrease in run time. Additionally, DL interpretation methods applicable beyond this study are described including hidden layer visualization and equation extractions describing a quantifiable amount of variance in hidden layer values. Finally, a large-scale analysis of soil phosphorus (P) levels was conducted in the Chesapeake Bay watershed, a current location of several short-term forecast tools. Based on Bayesian inference methodologies, 31 years of soil P history at the county scale were estimated, with the associated uncertainty for each estimate. These data will assist in the planning and implantation of short term forecast tools with P management goals. The short term modeling and communication tools developed in this work contribute to filling a gap in scientific tools aimed at improving water quality through informing land manager's decisions. / PHD

Many concrete problems may be cast in a multi-objective optimisation framework. The redundancy of existing methods for solving multi-objective programming problems susceptible to inconsistencies, coupled with the necessity for making in- herent assumptions before using a given method, make it hard for a nonspecialist to choose a method that ¯ts the situation at hand well. Moreover, using a method blindly, as suggested by the hammer principle (when you only have a hammer, you want everything in your hand to be a nail) is an awkward approach at best and a caricatural one at worst. This brings challenges to the design, development, implementation and deployment of a Decision Support System able to choose a method that is appropriate for a given problem and to apply the chosen method to solve the problem under consideration. The choice of method should be made according to the structure of the problem and the decision maker's opinion. The aim here is to embed a sample of methods representing the main multi-objective programming techniques and to help the decision maker find the most appropriate method for his problem. / Decisions Sciences / M. Sc. (Operations Research )

Thesis (PhD (Geography and Environmental Studies))—Stellenbosch University, 2008. / GIS has revolutionized geographic analysis and spatial decision support and has greatly enhanced our understanding of the real world though it’s mapping and spatial modelling capabilities. Although GIS software is becoming more powerful, less expensive and more userfriendly, GIS still remains the domain of a selected few who can operate and afford these systems. Since the introduction of web mapping tools such as Google Earth, accessibility to geographic information has escalated. Such tools enable anyone with access to a computer and the Internet to explore geographic data online and produce maps on demand. Web mapping products have, however, a very narrow range of functionality. In contrast to GIS that focuses on spatial data capture, storage, manipulation, analysis and presentation, the function of web mapping tools is to visualize and communicate geographical data. The positive impact of web mapping tools suggests, however, that GIS has not yet developed to a level where anyone can use the technology to support spatial decisions and enhance productivity. A possible solution is to close the functional gap between web mapping tools and GIS to make spatial analysis more accessible, thereby promoting geographical awareness and supporting better spatial decisions.

Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.

本文共壹冊，分柒章，約四萬言，章節目錄如下： 第一章：導論 一、前言，二、研究動機，三、研究目的，四、研究架構，五、研究限制。 第二章：緊急事故本質探討 一、名詞解釋，二、緊急事故性質，三、緊急事故影響與後果，四、面臨緊急事故時 之個人與組織行為，五、災變之防治。 第三章：緊急事故的管理 一、管理架構，二、管理之規劃與控制活動。 第四章：決策支援系統理論基礎 一、決策支援系統定義與特性，二、傳統EDP,MIS 與DSS 之比較，三、系統建立方法 。 第五章：緊急事故管理之決策支援系統設計 一、緊急事故下之決策程序，二、決策特徵，三、功能架構，四、系統建立。 第六章：個案實例 一、個案背景介紹，二、核能電廠緊急應變措施，三、系統需求，四、建立與實施。 第七章：結論與建議 一、結論，二、建議。 環繞人類四周環境中，常有許多不確定的災變隨時可能降臨。且發生災變時，如果資 訊缺乏或運用不當，常造成不必要的損失與傷亡。本研究係研究有關決策支援系統在 緊急事故管理上之應用，籍資訊的提供與利用，以支援緊急事故防治。 決策支援系統具有（一）易於使用，（二）模式庫與資料庫整合，（三）適於解決非 結構性問題，（四）具有良好彈性等特性，而緊急事故管理更明顯涉及決策者的價值 判斷，及對不確定環境的偏好，因此面臨這種結構程度低的問題，一個考慮完整的決 策支援系統將能提供管理人員更有效的支援。 本文之研究共分四部份，第一部份探討緊急事故的特性與本質，及面臨災變時人與群 體的行為與反應，第二部份探討緊急事故管理之決策程序與決策特性，第三部份則以 文獻分析方式探討決策支援系統相關文獻，以作為建造系統時指引，第四部份則以個 案研究法，針對核能電廠緊急事故之疏散掩蔽決策，提出應建立之決策支援系統。

Approved for public release, distribution is unlimited / Current network administrators use network management software to monitor and control elements within a network. This is largely a manual process since managers must interrogate devices individually and evaluate performance statistics manually. The systems provide multiple views on network data but lack capabilities that allow operators to visualize network performance. Since personnel are required to identify problems, interpret potential solutions, and decide on appropriate corrective measures without automatic assistance, maintaining and solving problems for a network can be time-consuming and complex significantly reducing network efficiency. Since FORCENET is a heterogeneous concept that combines various C4I networks, sensors, weapon systems, and platforms, a new model must be developed for network operations. This paper researches an improved model for fleet network operations management for distributed sea-based forces using existing technologies. Combining a collaborative tool, Decision Support System (DSS), and Augmented Reality (AR) imagery transforms Naval information network management from a "minimum threshold" to an "operations fusion" perspective. Little is known about AR technologies, but the potential exists for virtual network operations centers that can remotely direct networks for sea and shore assets through collaborative efforts. The product of this paper will serve as a baseline for network operations in the network centric environment. / Lieutenant, United States Naval Reserve

The increased demand for biofuels is expected to put additional strain on the available agricultural resources while at the same time causing environmental degradation. Hence, new energy policies need to be formulated and implemented in order to meet global energy needs while reducing the impact of biofuels farming and production. This research focuses on proving a decision support system which can aid the formulation of policies for the sustainable biofuel production. The system seeks to address policy formulation that requires reconciliation of the qualitative aspects of decision making (such as stakeholder’s viewpoints) with quantitative data, which often may be imprecise. To allow this, based on: Fuzzy logic and Multi Criteria Decision Making (MCDM) in the form of Analytical Hierarchy Process (AHP). Using these concepts, three software functionalities, “Options vs. Fuzzy Criteria Matrix”, “Analytical Hierarchy Process” and “Fuzzy AHP” were developed. These were added within the framework of pre-existing base software, Compendium (developed by the Open University, UK). A number of case study based models have been investigated using the software. These models made use of data from the Philippines and India in order to pinpoint suitable land and crop options for these countries. The models based on AHP and Fuzzy AHP were very successful in identifying suitable crop options for India by capturing both the stakeholder viewpoints and quantitative data. The software functionalities are very effective in scenario planning and selection of policies that would be beneficial in achieving a desired future scenario. The models further revealed that the newly developed software correctly identified many of the important issues in a consistent manner.

The project revolves around logistics and its adoption to the new rules. Theobjective of this project is to focus on minimizing data tampering to the lowest level possible.To achieve the set goals in this project, Decision support system and simulation havebeen used. However, to get clear insight about how they can be implemented, a systematicliterature review (Case Study incl.) has been conducted, followed by interviews with personnelat Kakinada port to understand the real-time complications in the field. Then, a simulatedexperiment using real-time data from Kakinada port has been conducted to achieve the set goalsand improve the level of transparency on all sides i.e., shipper, port and terminal.