Aitor Mata

Forecasting the probability of finding oil slicks using a CBR system

A new predicting system is presented in which the aim is to forecast the presence of oil slicks in a certain area of the open sea after an oil spill. Case-based reasoning is a computational methodology designed to generate solutions to a certain problem by analysing previous solutions given to previous solved problems. In this case, the system designed to predict the presence of oil slicks wraps other artificial intelligence techniques such as a radial basis function networks, growing cell structures and principal components analysis in order to develop the different phases of the Case-based reasoning cycle. The proposed system uses information such as sea salinity, sea temperature, wind, currents, pressure, number and area of the slicks,?. obtained from various satellites. The system has been trained using data obtained after the Prestige oil spill, occurred in the Atlantic waters, in the northwest of Spain. The system developed has been able to accurately predict the presence of oil slicks in the north west of the Galician coast, using historical data.

OBIFS) isotropic image analysis for improving a predicting agent based systems

In this interdisciplinary study a novel hybrid forecasting system is presented, in which an isotropic buffer operator is applied for case-based creation within the structure of the organization-based multi-agent system. Commonly used as an image analysis technique by commercial Geographic Information Systems (GIS), the buffer operator in this particular system calculates the area of a forest fire for prediction and visualization tasks. The use of the buffer operator improves the quality of the data used by the system and in consequence the quality of the results obtained. The system has been successfully tested using real historical data on forest fires evolution, by generating accurate predictions.

CROS: A Contingency Response multi-agent system for Oil Spills situations

This paper presents CROS, a Contingency Response multi-agent system for Oil Spill situations. The system uses the Case-Based Reasoning methodology to generate predictions to determine the probability of finding oil slicks in certain areas of the ocean. CBR uses past information to generate new solutions to the current problem. The system employs a SOA-based multi-agent architecture so that the main components of the system can be remotely accessed. Therefore, all functionalities (applications and services) can communicate in a distributed way, even from mobile devices. The core of the system is a group of deliberative agents acting as controllers and administrators for all applications and services. CROS manages information such as sea salinity, sea temperature, wind speed, ocean currents and atmosphere pressure, obtained from several sources, including satellite images. The system has been trained using historical data obtained after the Prestige accident on the Galician west coast of Spain. Results have demonstrated that the system can accurately predict the presence of oil slicks in determined zones after an oil spill. The use of a distributed multi-agent architecture has been shown to enhance the overall performance of the system.

Ensemble Methods for Boosting Visualization Models

Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a study of the combination of different ensemble training techniques with a novel summarization algorithm for ensembles of topology preserving models. The aim of these techniques is the increase of the truthfulness of the visualization of the dataset obtained by this kind of algorithms and, as an extension, the stability conditions of the former. A study and comparison of the performance of some novel and classical ensemble techniques, using well-known datasets from the UCI repository (Iris and Wine), are presented in this paper to test their suitability, in the fields of data visualization and topology preservation when combined with one of the most widespread of that kind of models such as the Self-Organizing Map.

Isotropic Image Analysis for Improving CBR Forecasting

A novel hybrid forecasting Case-Based Reasoning (CBR) system is presented in this interdisciplinary study in which an isotropic buffer operator is applied for case-based creation. Commonly used as an image analysis technique by commercial Geographic Information Systems (GIS), the buffer operator in this particular system calculates the area of an oil slick for prediction and visualization tasks. The use of the buffer operator improves the quality of the data used by the system and in consequence the quality of the results obtained. The system generates predictions by using historical data on oil-slick formation following a spill.

OSM: A Multi-Agent System for Modeling and Monitoring the Evolution of Oil Slicks in Open Oceans

A multi-agent based prediction-system is presented in which the aim is to forecast the presence of oil slicks in a certain area of the open sea after an oil spill. In this case, the multi-agent architecture incorporates a prediction-system based on the CBR methodology, implemented in a series of interactive services, for modeling and monitoring the ocean water masses. The system’s nucleus is formed by a series of deliberative agents acting as controllers and administrators for all the implemented services. The implemented services are accessible in a distributed way, and can be accessed even from mobile devices. The proposed system uses information such as sea salinity, sea temperature, wind, currents, pressure, number and area of the slicks, etc. obtained from various satellites. The system has been trained using data obtained after the Prestige accident. The Oil Spill Multi-Agent System (OSM) has been able to accurately predict the presence of oil slicks in the north-west of the Galician coast using historical data.

A topology-preserving system for environmental models forecasting

This inter-disciplinary study presents a novel mathematical simulation model based on an algorithm for the summarization of self-organizing maps ensembles applied under the case-based reasoning (CBR) methodology to perform forecasting tasks. This methodology represents a knowledge-extraction frame, where past information is used to generate new solutions to new problems. The novel summarization algorithm based on topology-preserving models organizes the stored information simplifying the retrieval of the most useful information from the case base. This algorithm is used to organize the case base and to improve the speed and efficiency of the retrieval phase of the CBR cycle within the explained predicting system. The developed mathematical system was applied to a real case of study: a forest fire forecasting data set. Forest fires represent an environmental risk that should be predicted in order to avoid further damages. This novel system was able to predict the future situation of geographic areas after a forest fire had been originated.

Forest Fires Prediction by an Organization Based System

In this study, a new organization based system for forest fires prediction is presented. It is an Organization Based System for Forest Fires Forecasting (OBSFFF). The core of the system is based on the Case-Based Reasoning methodology, and it is able to generate a prediction about the evolution of the forest fires in certain areas. CBR uses historical data to create new solutions to current problems. The system employs a distributed multi-agent architecture so that the main components of the system can be remotely accessed. All the elements building the final system, communicate in a distributed way, from different type of interfaces and devices. OBSFFF has been applied to generate predictions in real forest fire situations, using historical data both to train the system and to check the results. Results have demonstrated that the system accurately predicts the evolution of the fires. It has been demonstrated that using a distributed architecture enhances the overall performance of the system.

Forest Fire Evolution Prediction Using a Hybrid Intelligent System

Forest fires represent a quite complex environment and an accurate prediction of the fires generated is crucial when trying to react quickly and effectively in such a critical situation. In this study, an hybrid system is applied to predict the evolution of forest fires. The Case-Based Reasoning methodology combined with a summarization of SOM ensembles algorithm has been used to face this problem. The CBR methodology is used as the solution generator in the system, reusing past solutions given to past problems to generate new solutions to new problems by adapting those past solutions to the new situations to face. On the other hand, a new summarization algorithm (WeVoS-SOM) is used to organize the stored information to make it easier to retrieve the most useful information from the case base. The developed system has been checked with forest fires historical and experimental data. The WeVoS-CBR system presented here has successfully predicted the evolution of the forest fires in terms of probability of finding fires in a certain area.

A Hybrid Solution for Advice in the Knowledge Management Field

This paper presents a hybrid artificial intelligent solution that helps to automatically generate proposals, aimed at improving the internal states of organization units from a Knowledge Management (KM) point of view. This solution is based on the combination of the Case-Based Reasoning (CBR) and connectionist paradigms. The required outcome consists of customized solutions for different areas of expertise related to the organization units, once a lack of knowledge in any of those has been identified. On the other hand, the system is fed with KM data collected at the organization and unit contexts. This solution has been integrated in a KM system that additionally profiles the KM status of the whole organization.