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\hypersetup{pdftitle={Developing Adaptive Neuro"=fuzzy Inference System for Predicting Soil Moisture in Agriculture Fields},pdfauthor={Marjan Ghaemi, Rouzbeh Shad, Behzad Bagheri Shirvan},pdfsubject={Tropentag 2016: Abstract},pdfkeywords={Adaptive neuro-fuzzy inference system, available water holding capacity, farm scale, readily available properties},pdfpagemode=None,colorlinks=true}
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\parbox[b]{13.4cm}{\centering \large{\textbf{Tropentag, September 18-21, 2016, Vienna, Austria}}\\[1ex] \Large{``Solidarity in a competing world ---\\fair use of resources''\\[2ex]}}
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\Large{\textbf{Developing Adaptive Neuro"=fuzzy Inference System for Predicting Soil Moisture in Agriculture Fields\footnote{\textbf{Contact Address:} Marjan Ghaemi, Ferdowsi University of Mashhad (FUM), Engineering Faculty, Civil Department, Mashhad, Iran, \mbox{e-mail}: \email{mghaemi270@gmail.com}}\\[0.8ex]}}
\normalsize{\textsc{Marjan Ghaemi, Rouzbeh Shad, Behzad Bagheri Shirvan}}
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\begin{itemize*}
\item[]{\small{\textit{Ferdowsi University of Mashhad (FUM), Engineering Faculty, Iran, }}}
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\index[author]{Ghaemi, Marjan}
\index[author]{Shad, Rouzbeh}
\index[author]{Shirvan, Behzad Bagheri}
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\textbf{Abstract}
\begin{abstract}
\normalsize{
Providing suitable methods for soil moisture assessment is essential for management strategies to improve agricultural practices. Since, Iran is situated in arid and semi"=arid climatic conditions, this research was conducted in agriculture fields of the north of Shirvan to develop a model based on adaptive neuro"=fuzzy inference system (ANFIS) for predicting available water holding capacity (AWHC). One hundred composited soil samples (0--\mbox{30\,cm}) were collected by systematic soil sampling grid design at \mbox{30\,m} spacing apart and soil properties such as soil organic carbon (SOC), soil particle---size distribution, available water holding capacity (AWHC) and bulk density (BD) were measured. Then, the sample data were divided into two sets (train data set and test data set) and three ANFIS learning methods including Levenberg-Marquardt, Bayesian Regulisation and Scaled Conjugate Gradiantis were used to train different predefined fuzzy functions using training sets. Statistical parameters including mean square error (MSE), correlation coefficient of \mbox{5\,\%} level (r) and bias value were calculated for test data set validation. The results of this research showed that adaptive neuro"=fuzzy inference system method could be effectively utilised as a tool leading to better understanding phenomena with nonlinear and ambiguous nature such as soil moisture.}

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\noindent \textbf{Keywords:} Adaptive neuro"=fuzzy inference system, available water holding capacity, farm scale, readily available properties
\index[key]{Adaptive neuro"=fuzzy inference system}
\index[key]{Available water holding capacity}
\index[key]{Farm!scale}
\index[key]{Readily available properties}
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