The meteo-climatic datasets are at the basis of a great deal of studies on environmental state and its consequent management. In this frame, the completeness of meteo-climatic datasets is required for accurate and reliable analysis. Unfortunately, completeness is a rare in practice and, consequently, a preliminary treatment for filling in all gaps is needed. In this work, two intuitive and easy procedures for handling missing data are presented based on the "similarity station" concept. Finally, a comparison between the proposed methods and the Multiple Imputation Chained Equations, which is the state of the art in the field of missing data handling, has been carried out.
Similarity indices of meteo-climatic gauging stations for missing data handling: definition and comparison with the MICE method
E Barca;G Passarella
2015-01-01
Abstract
The meteo-climatic datasets are at the basis of a great deal of studies on environmental state and its consequent management. In this frame, the completeness of meteo-climatic datasets is required for accurate and reliable analysis. Unfortunately, completeness is a rare in practice and, consequently, a preliminary treatment for filling in all gaps is needed. In this work, two intuitive and easy procedures for handling missing data are presented based on the "similarity station" concept. Finally, a comparison between the proposed methods and the Multiple Imputation Chained Equations, which is the state of the art in the field of missing data handling, has been carried out.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
