Scheda di dettaglio – i prodotti della ricerca
| Dato | Valore |
|---|---|
| Title | Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework |
| Abstract | Time series modeling and forecasting play important roles in many practical fields. A good understanding of soil water content and salinity variability and the proper prediction of variations in these variables in response to changes in climate conditions are essential to properly plan water resources and appropriately manage irrigation and fertilization tasks. This paper provides a 48-h forecast of soil water content and salinity in the peculiar context of irrigation with reclaimed water in semi-arid environments. The forecasting was performed based on (i) soil water content and salinity data from 50 cm beneath the soil surface with a time resolution of 15 min, (ii) hourly atmospheric data and (iii) daily irrigation amounts. Exploratory data analysis and data pre-processing phases were performed and then statistical models were constructed for time series forecasting based on the set of available data. The obtained prediction models showed good forecasting accuracy and good interpretability of the results. |
| Source | Sensors (Basel) 22 (20) |
| Keywords | soil water contentsoil salinitytime seriesirrigation with reused waterinfiltration |
| Journal | Sensors (Basel) |
| Editor | Molecular Diversity Preservation International (MDPI),, Basel, |
| Year | 2022 |
| Type | Articolo in rivista |
| DOI | 10.3390/s22208062 |
| Authors | Giorgio, Anthony; Del Buono, Nicoletta; Berardi, Marco; Vurro, Michele; Vivaldi, Gaetano Alessandro |
| Text | 478386 2022 10.3390/s22208062 ISI Web of Science WOS 000875348700001 soil water content soil salinity time series irrigation with reused water infiltration Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework Giorgio, Anthony; Del Buono, Nicoletta; Berardi, Marco; Vurro, Michele; Vivaldi, Gaetano Alessandro Ernst Young; Univ Bari Aldo Moro; CNR; Univ Bari Aldo Moro Time series modeling and forecasting play important roles in many practical fields. A good understanding of soil water content and salinity variability and the proper prediction of variations in these variables in response to changes in climate conditions are essential to properly plan water resources and appropriately manage irrigation and fertilization tasks. This paper provides a 48 h forecast of soil water content and salinity in the peculiar context of irrigation with reclaimed water in semi arid environments. The forecasting was performed based on i soil water content and salinity data from 50 cm beneath the soil surface with a time resolution of 15 min, ii hourly atmospheric data and iii daily irrigation amounts. Exploratory data analysis and data pre processing phases were performed and then statistical models were constructed for time series forecasting based on the set of available data. The obtained prediction models showed good forecasting accuracy and good interpretability of the results. 22 Published version Articolo in rivista Molecular Diversity Preservation International MDPI , 1424 8220 Sensors Basel Sensors Basel Sensors Basel Sensors. Basel michele.vurro VURRO MICHELE marco.berardi BERARDI MARCO |