Riccardo Taormina

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Artificial Neural Networks (ANNs) have been successfully employed for predicting and forecasting groundwater levels up to some time steps ahead. In this paper, we present an application of feed forward neural networks (FFNs) for long period simulations of hourly groundwater levels in a coastal unconfined aquifer sited in the Lagoon of Venice, Italy. After(More)
In this work, we suggest that the poorer results obtained with particle swarm optimization (PSO) in some previous studies should be attributed to the cross-validation scheme commonly employed to improve generalization of PSO-trained neural network river forecasting (NNRF) models. Crossvalidation entails splitting the training dataset into two, and accepting(More)
An emerging trend in feature selection is the development of two-objective algorithms that analyze the tradeoff between the number of features and the classification performance of the model built with these features. Since these two objectives are conflicting, a typical result stands in a set of Pareto-efficient subsets, each having a different cardinality(More)
Local air quality forecasting can be made on the basis of meteorological and air pollution time series. Such data contain redundant information. Partial mutual information criterion is used to select the regressors which carry the maximal non redundant information to be used to build a prediction model. An application is shown regarding the forecast of PM10(More)
This work contributes a modeling framework to characterize the effect of cyber-physical attacks (CPAs) on the hydraulic behavior of water distribution systems. The framework consists of an attack model and a MATLAB toolbox named epanetCPA. The former identifies the components of the cyber infrastructure (e.g., sensors or programmable logic controllers) that(More)
20 21 Artificial Neural Networks (ANNs) have been successfully employed for predicting and 22 forecasting groundwater levels up to some time steps ahead. In this paper, we present an 23 application of feed forward neural networks (FFNs) for long period simulations of hourly 24 groundwater levels in a coastal unconfined aquifer sited in the Lagoon of Venice,(More)
The issue of air quality is now a major concern for many citizens worldwide. Local air quality forecasting can be made on the basis of meteorological variables and air pollutants concentration time series. We propose an adaptive filter technique based on an artificial neural network (ANN) to make 24-hours maximal daily ozone-concentrations forecasts.
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