Authors:
Mihaela Oprea
;
Marian Popescu
;
Sanda Florentina Mihalache
and
Elia Georgiana Dragomir
Affiliation:
Petroleum-Gas University of Ploiesti, Romania
Keyword(s):
Prediction Model, Data Mining, Adaptive Neuro-Fuzzy Inference System, Particulate Matter Air Pollution.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Fuzzy Systems
;
Knowledge-Based Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
The paper analyzes two artificial intelligence methods for particulate matter air pollutant prediction, namely data mining and adaptive neuro-fuzzy inference system (ANFIS). Both methods provide predictive knowledge under the form of rule base, the first method, data mining, as an explicit rule base, and ANFIS as an internal fuzzy rule base used to perform predictions. In order to determine the optimal number of prediction model inputs, we have perform a correlation analysis between particulate matter and other air pollutants. This operation imposed NO2 and CO concentrations as inputs of the prediction model, together with four values of PM10 concentration (from current hour to three hours ago), the output of the model being the prediction of the next hour PM10 concentration. The two prediction models are investigated through simulation in different structures and configurations using SAS® and MATLAB® respectively. The results are compared in terms of statistical parameters (RMSE, MA
PE) and simulation time.
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