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Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework
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 resourc...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9610225/ https://www.ncbi.nlm.nih.gov/pubmed/36298410 http://dx.doi.org/10.3390/s22208062 |
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author | Giorgio, Anthony Del Buono, Nicoletta Berardi, Marco Vurro, Michele Vivaldi, Gaetano Alessandro |
author_facet | Giorgio, Anthony Del Buono, Nicoletta Berardi, Marco Vurro, Michele Vivaldi, Gaetano Alessandro |
author_sort | Giorgio, Anthony |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9610225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96102252022-10-28 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 Sensors (Basel) Article 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. MDPI 2022-10-21 /pmc/articles/PMC9610225/ /pubmed/36298410 http://dx.doi.org/10.3390/s22208062 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Giorgio, Anthony Del Buono, Nicoletta Berardi, Marco Vurro, Michele Vivaldi, Gaetano Alessandro Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework |
title | Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework |
title_full | Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework |
title_fullStr | Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework |
title_full_unstemmed | Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework |
title_short | Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework |
title_sort | soil moisture sensor information enhanced by statistical methods in a reclaimed water irrigation framework |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9610225/ https://www.ncbi.nlm.nih.gov/pubmed/36298410 http://dx.doi.org/10.3390/s22208062 |
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