Environmental data processing is based on modeling and prediction of time series whose dynamic evolution is the result of the concurrence of many variables. Thegoal ofthis paper is to show how some recent advancs in data drives approaches (like Artificial Neural Networks, ANN, Fuzzy Inference Systems, FIS) can be of help to environmental problems solution. These kind of intelligent systems can be useful in environmental data analysis and interpretation from various perspectives: to perform knowledge discovery in large environmental databases ("environmental data mining"), to make prediction, to explain and interpret data and non-linear correlation among predictiong variables. The output of the intelligent processing systems can also facilitate decision-making. Environmental data show some characteristics features and peculiarities (noise, non linearity, non-stationarity, missing data, ...) that largely justifies the use of data oriented models. Here, we propose some specific open-ended issues in environmental monitoring (in particular, in air pollution monitoring and control), which require a modern approach for their assessment: identification and diagnosis of a given situation based on theprocessing of time and spatially varying data; forecasting of regular event (short time); forecasting of rare and extreme events (mid and long time); evaluation of a solution; inverse modeling. Thepresent paper illustrates practical applications in which intelligent systems have been deliberately introduced in the processing chains to solve problems that appears to be "unsolvable" by making use of more traditional statisticl and model-basd approaches. The paper will hopefully stimulate a wide interest on environmental data analysis and monitoring within the framework of supervised and unsupervised learning.

Environmental data interpretation: intelligent systems for modeling and prediction of urban air pollution data

MORABITO, Francesco Carlo;VERSACI, Mario
2003

Abstract

Environmental data processing is based on modeling and prediction of time series whose dynamic evolution is the result of the concurrence of many variables. Thegoal ofthis paper is to show how some recent advancs in data drives approaches (like Artificial Neural Networks, ANN, Fuzzy Inference Systems, FIS) can be of help to environmental problems solution. These kind of intelligent systems can be useful in environmental data analysis and interpretation from various perspectives: to perform knowledge discovery in large environmental databases ("environmental data mining"), to make prediction, to explain and interpret data and non-linear correlation among predictiong variables. The output of the intelligent processing systems can also facilitate decision-making. Environmental data show some characteristics features and peculiarities (noise, non linearity, non-stationarity, missing data, ...) that largely justifies the use of data oriented models. Here, we propose some specific open-ended issues in environmental monitoring (in particular, in air pollution monitoring and control), which require a modern approach for their assessment: identification and diagnosis of a given situation based on theprocessing of time and spatially varying data; forecasting of regular event (short time); forecasting of rare and extreme events (mid and long time); evaluation of a solution; inverse modeling. Thepresent paper illustrates practical applications in which intelligent systems have been deliberately introduced in the processing chains to solve problems that appears to be "unsolvable" by making use of more traditional statisticl and model-basd approaches. The paper will hopefully stimulate a wide interest on environmental data analysis and monitoring within the framework of supervised and unsupervised learning.
158603295X
Air Pollution; Fuzzy Ellipsoidal Systems; neuro-fuzzy systems; Complexity; Non-linear Systems
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/10199
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