Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters
This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to meas...
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| Format: | Article |
| Language: | English |
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Society of Land Measurements and Cadastre from Transylvania (SMTCT)
2010-09-01
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| Series: | Notulae Scientia Biologicae |
| Online Access: | http://notulaebiologicae.ro/index.php/nsb/article/view/4737 |
| _version_ | 1811343447672487936 |
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| author | Amir LAKZIAN Mohammad BANNAYAN AVAL Nasrin GORBANZADEH |
| author_facet | Amir LAKZIAN Mohammad BANNAYAN AVAL Nasrin GORBANZADEH |
| author_sort | Amir LAKZIAN |
| collection | DOAJ |
| description | This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC), -33 kPa, and permanent wilting point (PWP), -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN), Artificial Neural Network (ANN) and pedotransfer functions (PTF) were used to predict the soil water at each sampling location. Mean square deviation (MSD) and its components, index of agreement (d), root mean square difference (RMSD) and normalized RMSD (RMSDr) were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC. |
| first_indexed | 2024-04-13T19:30:37Z |
| format | Article |
| id | doaj.art-ee67bce03d6649d3805e7a782deadd02 |
| institution | Directory Open Access Journal |
| issn | 2067-3205 2067-3264 |
| language | English |
| last_indexed | 2024-04-13T19:30:37Z |
| publishDate | 2010-09-01 |
| publisher | Society of Land Measurements and Cadastre from Transylvania (SMTCT) |
| record_format | Article |
| series | Notulae Scientia Biologicae |
| spelling | doaj.art-ee67bce03d6649d3805e7a782deadd022022-12-22T02:33:13ZengSociety of Land Measurements and Cadastre from Transylvania (SMTCT)Notulae Scientia Biologicae2067-32052067-32642010-09-01231141204971Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water ParametersAmir LAKZIAN0Mohammad BANNAYAN AVAL1Nasrin GORBANZADEH2Ferdowsi University of Mashhad, Faculty of Agriculture, P.O. Box 91775-1163, MashhadFerdowsi University of Mashhad, Faculty of Agriculture, P.O. Box 91775-1163, MashhadFerdowsi University of Mashhad, Faculty of Agriculture, P.O. Box 91775-1163, MashhadThis paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC), -33 kPa, and permanent wilting point (PWP), -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN), Artificial Neural Network (ANN) and pedotransfer functions (PTF) were used to predict the soil water at each sampling location. Mean square deviation (MSD) and its components, index of agreement (d), root mean square difference (RMSD) and normalized RMSD (RMSDr) were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.http://notulaebiologicae.ro/index.php/nsb/article/view/4737 |
| spellingShingle | Amir LAKZIAN Mohammad BANNAYAN AVAL Nasrin GORBANZADEH Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters Notulae Scientia Biologicae |
| title | Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters |
| title_full | Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters |
| title_fullStr | Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters |
| title_full_unstemmed | Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters |
| title_short | Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters |
| title_sort | comparison of pattern recognition artificial neural network and pedotransfer functions for estimation of soil water parameters |
| url | http://notulaebiologicae.ro/index.php/nsb/article/view/4737 |
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