ESTIMATION OF SOIL MOISTURE CONTENT USING ARTIFICIAL NEURAL NETWORK

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NAMDAR KHOJASTEH D.; SHORAFA M.; OMID M.

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Paper Information

Journal: IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING Year:2011 | Volume:5 | Issue:14 Page(s): 53-60

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Title

ESTIMATION OF SOIL MOISTURE CONTENT USING ARTIFICIAL NEURAL NETWORK

Author(s)

NAMDAR KHOJASTEH D. | SHORAFA M. | OMID M. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKQ2

CLAY MINERALOGYQ3

SOIL TEXTUREQ2

TIME DOMAIN REFLECTOMETRY AND VOLUMETRIC WATER CONTENTQ2

Abstract

Estimating soil moisture content is very important in soil science and hydrologic studies. Time domain reflectometry (TDR) has been suggested for measuring soil moisture. Soil moisture as a porous environment can be predicted by TDR (wavelength) with probe installed in the environment. Since the reflected wave back under the influence of environment, soil moisture content can be measured. Therefore, the method can be used in the natural conditions of soil moisture without time consuming and with high accuracy and it is important application. Our objectives were to evaluate the ability of published models to fit TDR calibration data for the soils of different texture (clay, clay loam, loam, sandy clay, silty clay). An ARTIFICIAL NEURAL NETWORK (ANN) was used to predict the Ka–qv relationship considering soil physical parameters. The parameters that give the most significant reduction in the root mean square error (RMSE) are bulk density and clay content. The results showed that ANN predictions are better than other models such as Birchak et al. (2), De Loor (3), Malicki et al. (4), Topp et al. (4), Whalley (5) with comparable coefficient of determination and RMSE. Topp et al. model is showed poor result among models under study. Thus, by using ANN, highly accurate data can be obtained without need for elaborating soil specific calibration experiments.

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APA: Copy

NAMDAR KHOJASTEH, D., SHORAFA, M., & OMID, M.. (2011). ESTIMATION OF SOIL MOISTURE CONTENT USING ARTIFICIAL NEURAL NETWORK. IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, 5(14), 53-60. SID. https://sid.ir/paper/134883/en

Vancouver: Copy

NAMDAR KHOJASTEH D., SHORAFA M., OMID M.. ESTIMATION OF SOIL MOISTURE CONTENT USING ARTIFICIAL NEURAL NETWORK. IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING[Internet]. 2011;5(14):53-60. Available from: https://sid.ir/paper/134883/en

IEEE: Copy

D. NAMDAR KHOJASTEH, M. SHORAFA, and M. OMID, “ESTIMATION OF SOIL MOISTURE CONTENT USING ARTIFICIAL NEURAL NETWORK,” IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, vol. 5, no. 14, pp. 53–60, 2011, [Online]. Available: https://sid.ir/paper/134883/en

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