| Title | Evolutionary Polynomial Regression application for missing data handling in meteo-climatic gauging stations |
| Abstract | One of the most often encountered modelling problems is that of handling missing data, i.e. the problem of intermediate data gaps, where data/observations before and after the missing observations are available. The gaps in data represent discontinuities, which can pose difficulties both for model construction and model application phases. Evolutionary Polynomial Regression (EPR-MOGA) is a data-driven hybrid technique, which combines the effectiveness of genetic programming with the numerical regression for developing simple and easily interpretable mathematical model expressions. Evolutionary Polynomial Regression takes advantage of the evolutionary computing approach that allows the construction of several model expressions based on training data and least squares methodology to estimate numerical parameters/coefficients. These models can then be verified on a test set and gaps can be in-filled in test datasets by using one selected model. Because of the pseudo-polynomial formulations achievable by EPR-MOGA, it requires fewer numbers of parameters to be estimated, which in turn requires shorter time series for training. Another advantage of the EPR-MOGA approach is the ability to choose objective functions pertaining accuracy and parsimony. In the present work, an application of EPR-MOGA is shown on some sites belonging to the Apulian meteo-climatic monitoring network. |
| Source | GRASPA 2015, Bari, 15-16 giugno 2015GRASPA Working Papers Special Issue, pp. 25–28 |
| Keywords | Evolutionary Polynomial Regression; Missing Data Handling; Environmental Monitoring Networks |
| Journal | GRASPA Working Papers |
| Editor | GRASPA - Gruppo di Ricerca per le Applicazioni della Statistica ai Problemi Ambientali, S.l., Italia |
| Year | 2015 |
| Type | Contributo in atti di convegno |
| Authors | E. Barca, L. Berardi, D. B. Laucelli, G. Passarella, O. Giustolisi |
| Text | 331503 2015 Evolutionary Polynomial Regression; Missing Data Handling; Environmental Monitoring Networks Evolutionary Polynomial Regression application for missing data handling in meteo climatic gauging stations E. Barca, L. Berardi, D. B. Laucelli, G. Passarella, O. Giustolisi E. Barca 1 , L. Berardi 2 , D. B. Laucelli 2 , G. Passarella 1 , O. Giustolisi 2 1 Water Research Institute of the National Research Council, Department of Bari, Viale F. De Blasio, 5 70123 Bari, Italy; 2 Dept. of Civil Engineering and Architecture, Technical University of Bari, Via E. Orabona 4, 70125 Bari, Italy; One of the most often encountered modelling problems is that of handling missing data, i.e. the problem of intermediate data gaps, where data/observations before and after the missing observations are available. The gaps in data represent discontinuities, which can pose difficulties both for model construction and model application phases. Evolutionary Polynomial Regression EPR MOGA is a data driven hybrid technique, which combines the effectiveness of genetic programming with the numerical regression for developing simple and easily interpretable mathematical model expressions. Evolutionary Polynomial Regression takes advantage of the evolutionary computing approach that allows the construction of several model expressions based on training data and least squares methodology to estimate numerical parameters/coefficients. These models can then be verified on a test set and gaps can be in filled in test datasets by using one selected model. Because of the pseudo polynomial formulations achievable by EPR MOGA, it requires fewer numbers of parameters to be estimated, which in turn requires shorter time series for training. Another advantage of the EPR MOGA approach is the ability to choose objective functions pertaining accuracy and parsimony. In the present work, an application of EPR MOGA is shown on some sites belonging to the Apulian meteo climatic monitoring network. Proceedings of the GRASPA2015 Conference A. Fasso and A. Pollice Published version http //meetings.sis statistica.org/index.php/graspa2015/graspa2015/paper/viewFile/3289/589 E. Barca, L. Berardi, D.B. Laucelli, G. Passarella, O. Giustolisi 2015 Evolutionary Polynomial Regression application for missing data handling in meteo climatic gauging stations. In A. Fasso and A. Pollice Editors . Proceedings of the GRASPA2015 Conference, Bari, 15 16 June, 2015. Special issue of GRASPA Working Papers. ISSN 2037 7738. Special Issue GRASPA 2015 Bari 15 16 giugno 2015 Internazionale Contributo Evolutionary Polynomial Regression application for missing data handling in meteo climatic gauging stations 3289_7029_1_PB.pdf Contributo in atti di convegno GRASPA Gruppo di Ricerca per le Applicazioni della Statistica ai Problemi Ambientali 2037 7738 GRASPA Working Papers GRASPA Working Papers GRASPA Working Papers Working Papers. GRASPA Gruppo di Ricerca per le Applicazioni della Statistica ai Problemi Ambientali 2037 7738 GRASPA Working Papers GRASPA Working Papers GRASPA Working Papers Working Papers. giuseppe.passarella PASSARELLA GIUSEPPE emanuele.barca BARCA EMANUELE TA.P04.005.008 Integrazione di metodologie per il monitoraggio e la modellizzazione per la gestione delle risorse idriche |
