caa a22 4500 445837926 CHVBK 20180317145335.0 cr unu---uuuuu 170323e20110901xx s 000 0 eng 10.1007/s00271-010-0246-0 doi (NATIONALLICENCE)springer-10.1007/s00271-010-0246-0 A new approach for neutron moisture meter calibration: artificial neural network [Elektronische Daten] [Eyüp Köksal, Bilal Cemek, Cengiz Artık, Kadir Temizel, Mehmet Taşan] The neutron moisture meter (NMM) is a widely used device for sensing soil water content (SWC). Calibration accuracy and precision of the NMM are critical to obtain reliable results, and linear regression analysis of SWC against NMM count data is the most common method of calibration. In this study, artificial neural network (ANN) calibration models were developed and compared with linear regression. For this purposes, training and validation data were obtained from 2 calibration and 16 testing plots, respectively. Calibration plots consist of wet and dry soil water conditions separately. Data measured in dry beans and red pepper plots that have four different water levels were used to determine validity of regression and ANN-based calibration models. Volumetric SWC and NMM count ratio measurements were taken for depth intervals of 30cm throughout a 120-cm-deep soil profile. Several neural network architectures were explored in order to determine the optimal network architecture. Data analyses were conducted for each soil layer and for the whole profile, separately, based on both linear regression and ANN. Linear regression calibration equation coefficients of determination (r 2) for the 0-30, 30-60, 60-90 and 90-120cm depth ranges calculated by regression models were 0.85, 0.84, 0.72 and 0.82, respectively, and r 2 values were 0.94, 0.95, 0.87 and 0.88 based on ANN models, respectively. Using the data set from the entire 120-cm soil profile for calibration by ANN, the r 2 value was raised to 0.97. Springer-Verlag, 2010 Köksal Eyüp Agriculture Faculty, Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Kurupelit, Samsun, Turkey aut Cemek Bilal Agriculture Faculty, Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Kurupelit, Samsun, Turkey aut Artık Cengiz Soil and Water Resources Research Institute, Samsun, Turkey aut Temizel Kadir Agriculture Faculty, Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Kurupelit, Samsun, Turkey aut Taşan Mehmet Agriculture Faculty, Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Kurupelit, Samsun, Turkey aut Irrigation Science Springer-Verlag 29/5(2011-09-01), 369-377 0342-7188 29:5<369 2011 29 271 https://doi.org/10.1007/s00271-010-0246-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00271-010-0246-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Köksal Eyüp Agriculture Faculty, Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Kurupelit, Samsun, Turkey aut NATIONALLICENCE 700 1- Cemek Bilal Agriculture Faculty, Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Kurupelit, Samsun, Turkey aut NATIONALLICENCE 700 1- Artık Cengiz Soil and Water Resources Research Institute, Samsun, Turkey aut NATIONALLICENCE 700 1- Temizel Kadir Agriculture Faculty, Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Kurupelit, Samsun, Turkey aut NATIONALLICENCE 700 1- Taşan Mehmet Agriculture Faculty, Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Kurupelit, Samsun, Turkey aut NATIONALLICENCE 773 0- Irrigation Science Springer-Verlag 29/5(2011-09-01), 369-377 0342-7188 29:5<369 2011 29 271 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer