Data mining predictive algorithms for estimating soil water content

Soil water content (SWC) plays a key role in the management of water and soil resources. Accurate prediction of SWC is an important issue in water and soil studies. Recently, some data mining and machine learning techniques were proposed for SWC prediction and achieved encouraging results. This pape...

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Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 28; H. 6; S. 4915 - 4931
Hauptverfasser:	Emami, Somayeh, Rezaverdinejad, Vahid, Dehghanisanij, Hossein, Emami, Hojjat, Elbeltagi, Ahmed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2024 Springer Nature B.V
Schlagworte:	Algorithms Application of Soft Computing Artificial Intelligence Calibration Computational Intelligence Control Data mining Decision trees Engineering Error analysis Error reduction Estimation Machine learning Mathematical Logic and Foundations Mechatronics Moisture content Neural networks Predictions Remote sensing Robotics Root-mean-square errors Soil layers Soil water Support vector machines Topography Water consumption M5P Random subspace ensemble Random tree Soil water content REPTree
ISSN:	1432-7643, 1433-7479
Online-Zugang:	Volltext
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