Scheda di dettaglio – i prodotti della ricerca
| Dato | Valore |
|---|---|
| Title | A METHODOLOGY FOR TREATING MISSING DATA APPLIED TO DAILY RAINFALL DATA IN THE CANDELARO RIVER BASIN (ITALY) |
| Abstract | Environmental time series are often affected by the "presence" of missing data, but when dealing statistically with data, the need to fill in the gaps estimating the missing values must be considered. At present, a large number of statistical techniques are available to achieve this objective; they range from very simple methods, such as using the sample mean, to very sophisticated ones, such as multiple imputation. A brand new methodology for missing data estimation is proposed, which tries to merge the obvious advantages of the simplest techniques (e.g. their vocation to be easily implemented) with the strength of the newest techniques. The proposed method consists in the application of two consecutive stages: once it has been ascertained that a specific monitoring station is affected by missing data, the "most similar" monitoring stations are identified among neighbouring stations on the basis of a suitable similarity coefficient; in the second stage, a regressive method is applied in order to estimate the missing data. In this paper, four different regressive methods are applied and compared, in order to determine which is the most reliable for filling in the gaps, using rainfall data series measured in the Candelaro River Basin located in South Italy. |
| Source | Environmental monitoring and assessment (Print) 160 (1-4), pp. 1–22 |
| Keywords | Time seriesMissing dataParametrical regressionUncertainty |
| Journal | Environmental monitoring and assessment (Print) |
| Editor | Kluwer Academic Publishers, London, Paesi Bassi |
| Year | 2010 |
| Type | Articolo in rivista |
| DOI | 10.1007/s10661-008-0653-3 |
| Authors | LO PRESTI R.; BARCA E.; PASSARELLA G. |
| Text | 42278 2010 10.1007/s10661 008 0653 3 ISI Web of Science WOS 000272615400001 Scopus 2 s2.0 74349121501 PubMed 19096911 Time series Missing data Parametrical regression Uncertainty A METHODOLOGY FOR TREATING MISSING DATA APPLIED TO DAILY RAINFALL DATA IN THE CANDELARO RIVER BASIN ITALY LO PRESTI R.; BARCA E.; PASSARELLA G. R. Lo Presti, E. Barca and G. Passarella Water Research Institute of the National Research Council, Department of Bari, Viale F. De Blasio, 5 70132 Bari, Italy Environmental time series are often affected by the presence of missing data, but when dealing statistically with data, the need to fill in the gaps estimating the missing values must be considered. At present, a large number of statistical techniques are available to achieve this objective; they range from very simple methods, such as using the sample mean, to very sophisticated ones, such as multiple imputation. A brand new methodology for missing data estimation is proposed, which tries to merge the obvious advantages of the simplest techniques e.g. their vocation to be easily implemented with the strength of the newest techniques. The proposed method consists in the application of two consecutive stages once it has been ascertained that a specific monitoring station is affected by missing data, the most similar monitoring stations are identified among neighbouring stations on the basis of a suitable similarity coefficient; in the second stage, a regressive method is applied in order to estimate the missing data. In this paper, four different regressive methods are applied and compared, in order to determine which is the most reliable for filling in the gaps, using rainfall data series measured in the Candelaro River Basin located in South Italy. 160 http //link.springer.com/article/10.1007/s10661 008 0653 3 Articolo su rivista internazionale con IF Received 19 February 2008 / Accepted 5 November 2008 / Published online 19 December 2008 author for correspondence, e mail giuseppe.passarella@ba.irsa.cnr.it A METHODOLOGY FOR TREATING MISSING DATA APPLIED TO DAILY RAINFALL DATA IN THE CANDELARO RIVER BASIN ITALY Articolo su rivista internazionale con IF Received 19 February 2008 / Accepted 5 November 2008 / Published online 19 December 2008 author for correspondence, e mail giuseppe.passarella@ba.irsa.cnr.it INT_J_13.pdf Articolo in rivista Kluwer Academic Publishers 0167 6369 Environmental monitoring and assessment Print Environmental monitoring and assessment Print Environ. monit. assess. Print Environmental monitoring and assessment. Print emanuele.barca BARCA EMANUELE LO PRESTI ROSSELLA giuseppe.passarella PASSARELLA GIUSEPPE TA.P04.005.008 Integrazione di metodologie per il monitoraggio e la modellizzazione per la gestione delle risorse idriche |