Multiple Estimator Systems for the Analysis of Water Quality Parameters

摘要

In the literature, the problem of the biophysical parameter estimation has been faced through the use of predefined regression models (e.g., linear or polynomial) or, more recently, of artificial neural networks. However, different estimation methods may provide different accuracies depending on the region of the input feature space to which the analyzed pattern belongs. In this paper, we propose a novel estimation approach that consists in defining a Multiple Estimator System (MES). The key idea of the MES is to capture the peculiarities of an ensemble of different estimators in order to improve the accuracy and robustness of the single estimators. The proposed MES can be implemented in two conceptually different ways: 1) by combining the estimates obtained by the different estimators (Combination-Based Approach); 2) by selecting the output (estimate) of the best single estimator identified according to an adaptive measure of accuracy applied to the input feature space (Selection-Based Approach). The MES was applied to the problem of estimating water quality parameters, with a particular focus on the measure of concentration of chlorophyll. In the experimental phase, we used a recent and promising regression approach based on Support Vector Machines (SVMs) to create a set of estimators characterized by different "architectures" to be integrated in the ensemble. Experimental results pointed out the capability of the MES in increasing both the accuracy and robustness of the system.