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Email this article Interpolation of soil infiltration in furrow irrigation: Comparison of kriging, inverse distance weighting, multilayer perceptron and principal component analysis methods
Abstract
Study on soil infiltration rate as part of water cycle is essential for managing water resources and designing irrigation systems. The present study was conducted with the aim to compare Kriging, inverse distance weighting (IDW), multilayer perceptron (MLP) and principal component analysis (PCA) methods in the interpolation of soil infiltration in furrow irrigation, and determine the best interpolation method. To conduct infiltration tests, furrows were made on the farm in four triad groups. Infiltration through the blocked furrows method was measured 10, 20, 30, 40, 50, 60, 90, 120, 150, 160, 180 and 210 min after irrigation at a 10-meter distance in each furrow. Data were analyzed by GS+ and Neuro Solutions (NS) software packages. In this study, the maximum error (ME), mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative error (RE) and correlation coefficient (r) were used to compare the interpolation methods. The results of analysis of variance (ANOVA) indicated that differences in methods based on RMSE, MBE, MAE and ME indices were not significant; however, this difference was significant based on r and RE indices. According to the ANOVA results, it can be said that the PCA method with a r of 0.69 and RE of 31%, was predicted with a higher accuracy as compared to other methods.
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Arif, C., Mizuguchi, M., Seitan, B.I., Doe, R., 2012. Estimation of soil moisture in paddy field using artificial neural networks. International Journal of Advanced Research in Artificial Intelligence. 1(1), 17-21.
Beucher, A., Moller, A.B., Grave, M.H., 2017. Artificial neural networks and decision tree classification for predicting soil drainage classes in Denmark. Gendarme (Article in press).
Bodrud-Doza, Md., Towfiqul Islam, A.R.M., Ahmed, F., Das, S., Saha, N., Safiur Rahman, M., 2016. Characterization of groundwater quality using water evaluation indices, multivariate statistics and geostatistics in central Bangladesh. Water Science. 30, 19-40.
Dai, F., Zhou, Q., Lv, Zh., Wang, X., Liu, G., 2014. Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecological Indicators. 45, 184-194.
Das, G., 2000. Hydrology and soil conservation Engineering, Asoke K. Ghosh, Prentice Hall of India, 489 pp.
Delbari, M., Bahraini Motlagh, M., Kiani, M. and Amiri, M. 2013. Investigating spatio-temporal variability of groundwater quality parameters using geostatistics and GIS. International Research Journal of Applied and Basic Sciences, 4(12), 3623-3632.
Ekhmaj, A.I., 2010. Predicting Soil Infiltration Rate Using Artificial Neural Network. International Conference on Environmental Engineering and Applications.
Elliott, R.L., Walker, W.R., 1989. Field evaluation of furrow infiltration and advance functions. Transactions of the ASAE (American Society of Agricultural Engineers). 25(2), 396-400.
Fulop, I. A., Jozsa, J., Karamer, T., 1998. A neural network application in estimating wind induced shallow lake motion, Hydro Informatics. 98(2), 753-757.
Garzia Bonelli, M., Ferrini, M., Manni, A., 2017. Artificial neural networks to evaluate organic and inorganic contamination in agricultural soils. Chemosphere. 186, 124-131.
Goovaerts, P., 1997. Geostatistics for Natura Resources Evaluation . NewYork: Oxford University Press.
Hodam, S., Sarkar, S., Marak, A.G.R., and Bandyopadhyay, A. 2017. Spatial Interpolation of reference Evapotranspiration in India: Comparison of IDW and Kriging Method. Journal of the Institution of Engineers (India). 98(4): 511-524.
Isaaks, E.H., Sirvastana, R.M., 1989. An introduction to applied geostatistics. Oxford University Press, 561 p.
Jahani, S., Delbari, M., 2009. Evaluation and estimation of the 24-hour maximum rainfall in the province of Golestan. Journal of Water Engineering. 2(4), 13-22 (in Persian)
Khatri, L., Smith, R., 2006. Read-time prediction of soil infiltration characteristics for the management of furrow irrigation. Irrigation Science. 25(1):33-43.
Kravchenko, A., Bullock, D.G., 1997. A comparitive study of interpolation methods for mapping soil properties. Argon. 91: 393-400.
Marashi, M., Mohammadi Torkashvand, A., Ahmadi, A., Esfandiari, M. 2017. Estimation of soil aggregate stability indices using artificial neural network and multiple linear regression models. Spanish Journal of Soil Sciences. 7(2): 89-99.
Menhaj, M.B., 2009. Fundamentals of Neuaral Networks, Computational Intelligence, Vol, 1. 8th Edition, Amirkabir university of technology, Tehran, Iran. 715 p (in Persian)
Misaghi, F., Mohammadi, K., 2007. Estimating spatial distribtion of rainfall using statistical and geostatistical methods and comparison with artificial networks. The Scientific Journal of Agricultural (SJA). 29(4),1-13.
Mohammadi, J., 2001. Review on fundamentals of geostatistics and its application to soil science. Journal of Soil and Waters Sciences. 15(1), 99-121 (in Persian)
Namdar-Khojasteh, D., Shorafa, M., Omid, M., Fazeli-Shaghani, M., 2010. Application of artificial neural networks in modeling soil solution electrical conductivity. Soil science Journal. 175 (9), 432-437.
Nanos, N., Montero, G., 2002. Spatial prediction of diameter distribution models. Forest Ecology and Management. 161, 147-158.
Nevtipilova, V., Pastwa, J., Boori, M. S., Vozenilek, V., 2014. Testing Artificial Neural Network (ANN) for Spatial Interpolation. Journal of Geology & Geophysics. 3(2), 01-09.
Pahlavan Rad, M.R., Dehghani, A.A., Shahriari, A., Akbari Moghaddam, A. R., Dahmardeh, Kh., 2016. Predicting spatial variability of soil salinity and clay content using geostatistics and artificial neural networks methods. J of soil Management and sutainable production. 6(1). http://ejsms.gau.ac.ir (in persian)
Parchami Araghi, F., Mirlatifi, S.M., Ghorbani Dashtaki, Sh., Mahdian, M. H., 2010. Estimating Cumulative Infiltration Using Artificial Neural Networks in Calcareous Soils. Journal of water and soil. 24(3),512-526 (in Persian)
Perez-Ortela, M., Daccache, A., Hess, T.M., Knox, J.W., 2015. Simulating paets of irrigation heterogeneity of onion (Allium Cepa L.) yield in a humid climate. Irrigation Science 33, 1-14.
Salmeron, M., Urrego, Y. F., Cavero, J., 2012. Effect of non-uniform sprinkler irrigation and plant density on simulated maize yield. Agricultural Water Management 113, 1-9.
Sanchez, I., Zapata, N., Faci, J. M., 2010. Combined effect of technical, meteorological and agronomical factors on solid- set sprinkler Irrigation: I. Irrigation Performance and Soil water recharge in alfalfa and maize. Agricultural water management 97, 1571-1581.
Sitharam, T.G., Samui, P., Anbazhagan P., 2008. Spatial variability of rock depth in temperate forests. Geotechnical and Geological Engineering. 26, 503-517.
Tabatabaei, S. J., Nayshabori, M. R., Fardad, H., Liaghat, A., 2005. Agricultural Management Effect on Soil Basic Infiltration Rate in Furrow Irrigation of Corn. Journal of soil and water sciences. 19(2), 255-262 (in Persian)
Tabatabaei, S.H., Ghazali, M., 2011.Accuracy of interpolation methods in estimating the groundwater level (Case study: Farsan- Jooneghan and Sefiddasht Aquifers).Water and Soil Science (Journal of Science and Technology of Agriculture and Natural Resources).15(57), 11-22 (in Persian)
Webster, R., Oliver, M.A., 2000. Geostatistics for environmental Scientists. Wiley Press, 271 p.
Zauche, M., Bel, L., Vandour, E., 2017. Geostatistical mapping of topsoil organic carbon and uncertainty assessment in western Paris corplands (France). Geoderma Regional. 10, 126-137.
DOI: http://dx.doi.org/10.17951/pjss.2019.52.1.59
Date of publication: 2019-05-29 07:07:44
Date of submission: 2018-06-07 14:27:49
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