Thomas A. Mazzuchi

Urban Water Demand Forecasting: Review of Methods and Models

AbstractThis paper reviews the literature on urban water demand forecasting published from 2000 to 2010 to identify methods and models useful for specific water utility decision making problems. Results show that although a wide variety of methods and models have attracted attention, applications of these models differ, depending on the forecast variable, its periodicity and the forecast horizon. Whereas artificial neural networks are more likely to be used for short-term forecasting, econometric models, coupled with simulation or scenario-based forecasting, tend to be used for long-term strategic decisions. Much more attention needs to be given to probabilistic forecasting methods if utilities are to make decisions that reflect the level of uncertainty in future demand forecasts.

A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier

With increasing Internet connectivity and traffic volume, recent intrusion incidents have reemphasized the importance of network intrusion detection systems for combating increasingly sophisticated network attacks. Techniques such as pattern recognition and the data mining of network events are often used by intrusion detection systems to classify the network events as either normal events or attack events. Our research study claims that the Hidden Naive Bayes (HNB) model can be applied to intrusion detection problems that suffer from dimensionality, highly correlated features and high network data stream volumes. HNB is a data mining model that relaxes the Naive Bayes methods conditional independence assumption. Our experimental results show that the HNB model exhibits a superior overall performance in terms of accuracy, error rate and misclassification cost compared with the traditional Naive Bayes model, leading extended Naive Bayes models and the Knowledge Discovery and Data Mining (KDD) Cup 1999 winner. Our model performed better than other leading state-of-the art models, such as SVM, in predictive accuracy. The results also indicate that our model significantly improves the accuracy of detecting denial-of-services (DoS) attacks.

Expert judgment in maintenance optimization

A comprehensive method for the use of expert opinion for obtaining lifetime distributions required for maintenance optimization is proposed. The method includes procedures for the elicitation of discretized lifetime distributions from several experts, the combination of the elicited expert opinion into a consensus distribution, and the updating of the consensus distribution with failure and maintenance data. The development of the method was prompted by the lack of statistical training of the experts and the high demands on their time. The use of a discretized life distribution provides more flexibility, is more comprehendible by the experts in the elicitation stage, and greatly reduces the computation in the combination and updating stages. The methodology is Bayes, using the Dirichlet distribution as the prior distribution for the elicited discrete lifetime distribution. Methods are described for incorporating information concerning the expertise of the experts into the analysis. >

A novel flexibility analysis approach for processes with stochastic parameters

Abstract This paper presents a novel flexibility analysis approach for process systems with stochastic parameters. Assuming a linear model for the process and a Gaussian distribution model for the parameter uncertainty, a computational strategy is proposed which fully exploits the probabilistic structure of the problem, and with which a stochastic flexibility index can be determined. This index measures the probability that a given design is feasible to operate by explicitly taking into account the existence of operating degrees of freedom. A major feature of the suggested procedure is that it can handle any number of correlated and/or independent parameters. Examples are presented to illustrate the proposed methodology.

A Bayesian perspective on some replacement strategies

In this paper we present a Bayesian decision theoretic approach for determining optimal replacement strategies. This approach enables us to formally incorporate, express, and update our uncertainty when determining optimal replacement strategies. We develop relevant expressions for both the block replacement protocol with minimal repair and the age replacement protocol and illustrate the use of our approach with real data.

A traffic density analysis of proposed ferry service expansion in San Francisco Bay using a maritime simulation model

Abstract A proposal has been made to the California legislature to dramatically increase the frequency and coverage of ferry service in the San Francisco Bay area. A major question in the approval process is the effect of this expansion on the level of congestion on the waterway and the effect this will have on the safety of vessels in the area. A simulation model was created to estimate the number of vessel interactions in the current system and their increases caused by three alternative expansion plans. The output of the simulation model is a geographic profile showing the frequency of vessel interactions across the study area, thus representing the level of congestion under each alternative. Comparing these geographic interaction profiles to a similar one generated for the current ferry service in the San Francisco Bay allows evaluation of the increase in exposure of ferries to adverse conditions, such as, for example, the interaction of high-speed ferries in restricted visibility conditions. This analysis has been submitted to the legislature as part of the overall assessment of the proposal and will be used in the expansion decision.

A Bayes approach to step-stress accelerated life testing

This paper develops a Bayes model for step-stress accelerated life testing. The failure times at each stress level are exponentially distributed, but strict adherence to a time-transformation function is not required. Rather, prior information is used to define indirectly a multivariate prior distribution for the failure rates at the various stress levels. Our prior distribution preserves the natural ordering of the failure rates in both the prior and posterior estimates. Methods are developed for Bayes point estimates as well as for making probability statements for use-stress life parameters. The approach is illustrated with an example.

Using system simulation to model the impact of human error in a maritime system

Human error is cited as the predominant cause of transportation accidents. This paper describes the modeling of human error related accident event sequences in a risk assessment of maritime oil transportation in Prince William Sound, Alaska. The risk analysts were confronted with incomplete and misleading data that made it difficult to use theoretical frameworks. They were required, therefore, to make significant modeling assumptions in order to produce valid and useful results. A two stage human error framwork was developed for the Prince William Sound Risk Assessment based on interviews with maritime experts. Conditional probabilities implied by this framework were elicited from system experts (tanker masters, mates, engineers, and state pilots) and used within a dynamic simulation to produce the risk analysis base case results discussed. The ability to quantify the effectiveness of proposed risk reduction interventions aimed at reducing human and organizational error were limited by the level of detail described by the taxonomy of human error.

The Prince William Sound Risk Assessment

After the grounding of the Exxon Valdez and its subsequent oil spill, all parties with interests in Prince William Sound (PWS) were eager to prevent another major pollution event. While they implemented several measures to reduce the risk of an oil spill, the stakeholders disagreed about the effectiveness of these measures and the potential effectiveness of further proposed measures. They formed a steering committee to represent all the major stakeholders in the oil industry, in the government, in local industry, and among the local citizens. The steering committee hired a consultant team, which created a detailed model of the PWS system, integrating system simulation, data analysis, and expert judgment. The model was capable of assessing the current risk of accidents involving oil tankers operating in the PWS and of evaluating measures aimed at reducing this risk. The risk model showed that actions taken prior to the study had reduced the risk of oil spill by 75 percent, and it identified measures estimated to reduce the accident frequency by an additional 68 percent, including improving the safety-management systems of the oil companies and stationing an enhanced-capability tug, called the Gulf Service, at Hinchinbrook Entrance. In all, various stakeholders made multimillion dollar investments to reduce the risk of further oil spills based on the results of the risk assessment.

A Risk Management Procedure for the Washington State Ferries

The state of Washington operates the largest passenger vessel ferry system in the United States. In part due to the introduction of high-speed ferries, the state of Washington established an independent blue-ribbon panel to assess the adequacy of requirements for passenger and crew safety aboard the Washington state ferries. On July 9, 1998, the Blue Ribbon Panel on Washington State Ferry Safety engaged a consultant team from The George Washington University and Rensselaer Polytechnic Institute/Le Moyne College to assess the adequacy of passenger and crew safety in the Washington state ferry (WSF) system, to evaluate the level of risk present in the WSF system, and to develop recommendations for prioritized risk reduction measures, which, once implemented, can improve the level of safety in the WSF system. The probability of ferry collisions in the WSF system was assessed using a dynamic simulation methodology that extends the scope of available data with expert judgment. The potential consequences of collisions were modeled in order to determine the requirements for onboard and external emergency response procedures and equipment. The methodology was used to evaluate potential risk reduction measures and to make detailed risk management recommendations to the blue-ribbon panel and the Washington State Transportation Commission.