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Tytuł:
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Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework.
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Autorzy:
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Giorgio A; Ernst & Young, 70126 Bari, Italy.
Del Buono N; Dipartimento di Matematica, Università degli Studi di Bari Aldo Moro, Via E. Orabona 4, 70125 Bari, Italy.
Berardi M; Istituto di Ricerca sulle Acque, Consiglio Nazionale delle Ricerche, Via F. De Blasio 5, 70132 Bari, Italy.
Vurro M; Istituto di Ricerca sulle Acque, Consiglio Nazionale delle Ricerche, Via F. De Blasio 5, 70132 Bari, Italy.
Vivaldi GA; Dipartimento di Scienze Agro Ambientali e Territoriali, Università degli Studi di Bari Aldo Moro, Via G. Amendola 165/A, 70126 Bari, Italy.
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Źródło:
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Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Oct 21; Vol. 22 (20). Date of Electronic Publication: 2022 Oct 21.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: Basel, Switzerland : MDPI, c2000-
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MeSH Terms:
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Soil*
Agricultural Irrigation*/methods
Salinity ; Water ; Climate
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References:
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Sci Total Environ. 2019 Apr 10;660:1513-1521. (PMID: 30743943)
N Biotechnol. 2020 May 25;56:87-95. (PMID: 31877378)
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Grant Information:
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5ABY6P0 Regione Puglia
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Contributed Indexing:
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Keywords: infiltration; irrigation with reused water; soil salinity; soil water content; time series
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Substance Nomenclature:
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0 (Soil)
059QF0KO0R (Water)
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Entry Date(s):
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Date Created: 20221027 Date Completed: 20221028 Latest Revision: 20221030
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Update Code:
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20240105
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PubMed Central ID:
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PMC9610225
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DOI:
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10.3390/s22208062
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PMID:
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36298410
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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.
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