Michael Hadjimichael

A fuzzy expert system for aviation risk assessment

The Flight Operations Risk Assessment System (FORAS) is a risk modeling methodology which represents risk factors and their interrelationships as a fuzzy expert system. A FORAS risk model provides a quantitative relative risk index representing an estimate of the cumulative effects of potential hazards on a single flight operation. FORAS systematizes the process of eliciting human expertise, provides for a natural representation of the knowledge in an expert system, and automates the process of risk assessment. The FORAS tool is valuable to airline safety departments for examining risk trends, to pilots and dispatchers for assessing risks associated with each flight, and to airline management for quantifying the effects of making safety-related changes. The quantitative relative risk index generated by FORAS allows comparisons between flights, and facilitates the communication of safety issues throughout the organization.

Subsessions: a granular approach to click path analysis

Electronic, web-based commerce enables and demands the application of intelligent methods to analyze information collected from consumer web sessions. We propose a method of increasing the granularity of the user session analysis by isolating useful subsessions within web page access sessions, where each subsession represents a frequently traversed path indicating high-level user activity. The subsession approximates user state information as well as anticipated user activity, and as a result is useful for personalization and pre-caching.

A meteorological fuzzy expert system incorporating subjective user input

Abstract. We present a fuzzy expert system, MEDEX, for forecasting gale-force winds in the Mediterranean basin. The most successful local wind forecasting in this region is achieved by an expert human forecaster with access to numerical weather prediction products. That forecasters knowledge is expressed as a set of ‘rules-of-thumb’. Fuzzy set methodologies have proved well suited for encoding the forecasters knowledge, and for accommodating the uncertainty inherent in the specification of rules, as well as in subjective and objective input. MEDEX uses fuzzy set theory in two ways: as a fuzzy rule base in the expert system, and for fuzzy pattern matching to select dominant wind circulation patterns as one input to the expert system. The system was developed, tuned, and verified over a two-year period, during which the weather conditions from 539 days were individually analyzed. Evaluations of MEDEX performance for both the onset and cessation of winter and summer winds are presented, and demonstrate that MEDEX has forecasting skill competitive with the US Navys regional forecasting center in Rota, Spain.

A fuzzy expert system to assist in the prediction of hazardous wind conditions within the Mediterranean basin

An expert system (MEDEX) for predicting the gale-force onset, continuation, and cessation of seven major wind types within the Mediterranean basin has been designed, developed, and tested. The six wind types consist of the bora (flowing through both the Adriatic and Aegean Seas), etesian, levante, mistral, sirocco and westerly (poniente and vendaval). Except for the sirocco, these winds result from synoptic situations that lead to topographical channelling. MEDEX is rule-based and incorporates fuzzy logic to handle both objective and subjective inputs, the latter being a unique application of fuzzy logic. MEDEX has approximately 330 fuzzy rules, covering the seven winds in both winter and summer seasons. While MEDEX has been designed as a nowcasting tool (0–12 h), it can be applied to any future time for which forecasting charts (consisting of surface pressure and 500 mb height fields) are available. Inputs consist of objective pressure gradients in addition to subjective interpretations of various synoptic features. These inputs, as well as the corresponding rules, were developed, tuned, and verified over a two-year period during which the weather conditions from 539 days were individually analysed. Ground truth verification was produced primarily from over-water Special Sensor Microwave/Imager (SSM/I) wind speed measurements but also included observations as available, model predictions, and official Navy wind warnings. Evaluations of MEDEX performance for both onset and cessation of winter and summer winds are presented. In addition, comparisons with forecast statistics for the Navys regional weather centre in Rota, Spain show that MEDEX has comparable forecasting skill. Copyright

Remote Cloud Ceiling Assessment Using Data-Mining Methods

Abstract Data-mining methods are applied to numerical weather prediction (NWP) output and satellite data to develop automated algorithms for the diagnosis of cloud ceiling height in regions where no local observations are available at analysis time. A database of hourly records that include Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) output, satellite data, and ground truth observations [aviation routine weather reports (METAR)] has been created. Data were collected over a 2.5-yr period for specific locations in California. Data-mining techniques have been applied to the database to determine relationships in the collected physical parameters that best estimate cloud ceiling conditions, with an emphasis on low ceiling heights. Algorithm development resulted in a three-step approach: 1) determine if a cloud ceiling exists, 2) if a cloud ceiling is determined to exist, determine if the ceiling is high or low (below 1 000 m), and 3) if the cloud ceiling is determined to be low, compute ceil...

Data Mining Numerical Model Output for Single-Station Cloud-Ceiling Forecast Algorithms

Accurate cloud-ceiling-height forecasts derived from numerical weather prediction (NWP) model data are useful for aviation and other interests where low cloud ceilings have an impact on operations. A demonstration of the usefulness of data-mining methods in developing cloud-ceiling forecast algorithms from NWP model output is provided here. Rapid Update Cycle (RUC) 1-h forecast data were made available for nearly every hour in 2004. Various model variables were extracted from these data and stored in a database of hourly records for routine aviation weather report (METAR) station KJFK at John F. Kennedy International Airport along with other single-station locations. Using KJFK cloud-ceiling observations as ground truth, algorithms were derived for 1-, 3-, 6-, and 12-h forecasts through a data-mining process. Performance of these cloud-ceiling forecast algorithms, as evaluated through cross-validation testing, is compared with persistence and Global Forecast System (GFS) model output statistics (MOS) performance (6 and 12 h only) over the entire year. The 1-h algorithms were also compared with the RUC model cloud-ceiling (or cloud base) height translation algorithms. The cloud-ceiling algorithms developed through data mining outperformed these RUC model translation algorithms, showed slightly better skill and accuracy than persistence at 3 h, and outperformed persistence at 6 and 12 h. Comparisons to GFS MOS (which uses observations in addition to model data for algorithm derivation) at 6 h demonstrated similar performance between the two methods with the cloud-ceiling algorithm derived through data mining demonstrating more skill at 12 h.

Subsessions: A granular approach to click path analysis: Click Path Analysis

The fiercely competitive web‐based electronic commerce (e‐commerce) environment has made necessary the application of intelligent methods to gather and analyze information collected from consumer web sessions. Knowledge about user behavior and session goals can be discovered from the information gathered about user activities, as tracked by web clicks. Most current approaches to customer behavior analysis study the user session by examining each web page access. However, the abstraction of subsessions provides a more granular view of user activity. Here, we propose a method of increasing the granularity of the user session analysis by isolating useful subsessions within sessions. Each subsession represents a high‐level user activity such as performing a purchase or searching for a particular type of information. Given a set of previously identified subsessions, we can determine at which point the user begins a preidentified subsession by tracking user clicks. With this information we can (1) optimize the user experience by precaching pages or (2) provide an adaptive user experience by presenting pages according to our estimation of the users ultimate goal. To identify subsessions, we present an algorithm to compute frequent click paths from which subsessions then can be isolated. The algorithm functions by scanning all user sessions and extracting all frequent subpaths by using a distance function to determining subpath similarity. Each frequent subpath represents a subsession. An analysis of the pages represented by the subsession provides additional information about semantically related activities commonly performed by users.

Automating the estimation of various meteorological parameters using satellite data and machine learning techniques

Satellite data from various sensors and platforms are being used to develop automated algorithms to assist in U.S. Navy operational weather assessment and forecasting. Supervised machine learning techniques are used to discover patterns in the data and develop associated classification and parameter estimation algorithms. These methods are applied to cloud classification in GOES imagery, tropical cyclone intensity estimation using SSM/I data, and cloud ceiling height estimation at remote locations using appropriate geostationary and polar orbiting satellite data in conjunction with numerical weather prediction output and climatology. All developed algorithms rely on training data sets that consist of records of attributes (computed from the appropriate data source) and the associated ground truth.

Data mining - a semantic model

Data mining techniques applied to decision support in real-life problems require a multi-step process. Inputs and outputs of these steps require some standard format to be followed in order to achieve a useful platform for the execution of data mining algorithms. There is a need to develop a uniform model where every operation can be expressed in a standard way, allowing algorithms to cooperate and to reuse results. We present, first, a common structure for the representation of inter-step results, and second, a model of the operator, i.e. the entity that handles and transforms this common structure according to a basic data mining algorithm.

Using a Fuzzy Expert System to Represent Risk: FORAS

The goal of the Flight Operations Risk Assessment System (FORAS) project is to address the needs of commercial aviation safety departments for an operational risk assessment tool. FORAS consists of an aviation risk modeling methodology and a set of software programs to create aviation risk models. It systematizes the process of eliciting human expertise, provides for a natural representation of the knowledge in a fuzzy expert system, and automates the process of risk assessment. A FORAS model is valuable to airline safety departments for examining risk trends, to dispatch departments for assessing risks associated with each flight, and to airline executives for quantifying the effects of making safety-related changes.