DOI:https://doi.org/10.3232/SJSS.2017.V7.N2.04

Estimación de índices de estabilidad de agregados de suelo mediante redes neuronales artificiales y modelos de regresión múltiple linear

Maryam Marashi, Ali Mohammadi Torkashvand, Abbas Ahmadi, Mehrdad Esfandyari

Resumen

Durante las últimas décadas se ha utilizado un sistema de inteligencia artificial para desarrollar funciones de pedotransferencia (PTFs) que permiten estimar las propiedades del suelo. En este trabajo se evaluó la capacidad del modelo de regresión múltiple linear (MLR) y de las redes neuronales artificiales (ANNs) para desarrollar PTFs que permitan estimar el diámetro medio ponderado (MWD) a partir de propiedades rutinarias del suelo (P1) y de la combinación de propiedades rutinarias del suelo y agregados de dimension fractal (P2). Los resultados mostraron que el modelo ANN para estimar el MWD es más exacto que el modelo MLR. La aplicación de la dimensión fractal de los agregados como herramienta de predicción en ambos métodos mejoró la exactitud de las PTFs.

Vistas: 137
Descargas PDF (English): 137

 

Referencias


Ahmadi A, Neyshabouri MR, Rouhipour H, Asadi H. 2011. Fractal dimension of soil aggregates as an index of soil erodibility. J Hydrol. 400(3):305-311.

Alijanpour Shalmani A, Shabanpour M, Asadi H, Bagheri F. 2011. Estimation of soil aggregate stability in forest`s soils of Guilan province by artificial neural networks and regression pedotransfer functions. Water Soil Sci. 21(3):153-162.

Besalatpour AA, Ayoubi S, Hajabbasi MA, Mosaddeghi MR, Schulin R. 2013. Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed. Catena 111:72-79.

Besalatpour A, Hajabbasi MA, Ayoubi S, Afyuni M, Jalalian A, Schulin R. 2012. Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system. Soil Sci Plant Nutr. 58(2):149-160.

Bocco M, Willington E, Arias M. 2010. Comparison of regression and neural networks models to estimate solar radiation. Chil J Agric Res. 70(3):428-435.

Bower CA, Reitemeier RF, Fireman M. 1952. Exchangeable cation analysis of saline and alkali soils. Soil Sci. 73: 251-261.

Cañasveras JC, Barrón V, Del Campillo MC, Torrent J, Gómez JA. 2010. Estimation of aggregate stability indices in Mediterranean soils by diffuse reflectance spectroscopy. Geoderma 158(1):78-84.

de Boer DH, Stone M, Lévesque LMJ. 2000. Fractal dimensions of individual particles and particle populations of suspended solids in streams. Hydrol Processes 14:653-667.

Dimoyiannis DG, Tsadilas CD, Valmis S. 1998. Factors affecting aggregate instability of Greek agricultural soils. Commun Soil Sci Plant Anal. 29(9-10):1239-1251.

Gago J, Martínez-Núñez L, Landín M, Gallego PP. 2010. Artificial neural networks as an alternative to the traditional statistical methodology in plant research. J Plant Physiol. 167(1):23-27.

Gee GW, Or D. 2002. Particle-size analysis. In: Warren AD, editor. Methods of Soil Analysis. Part 4. Physical Methods. Madison, WI: Soil Sci. Soc. Am., Inc. p. 255-295.

Huang Y, Lan Y, Thomson SJ, Fang A, Hoffmann WC, Lacey RE. 2010. Development of soft computing and applications in agricultural and biological engineering. Comput Electron Agr. 71(2):107-127.

Idowu OJ. 2003. Relationships between aggregate stability and selected soil properties in humid tropical environment. Commun Soil Sci Plant Anal. 34(5-6):695-708.

Igwe C, Mbagwu J. 1995. Physical properties of soils of Southeastern Nigeria and the role of some aggregating agents in their stability. Soil Sci. 160(6):431-441.

Kalkan E, Akbulut S, Tortum A, Celik S. 2009. Prediction of the unconfined compressive strength of compacted granular soils by using inference systems. Environ Geol. 58(7):1429-1440.

Kisi Ö. 2004. Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol Sci J. 49(6):1025-1040.

Kisi O, Haktanir T, Ardiclioglu M, Ozturk O, Yalcin E, Uludag S. 2009. Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Adv Eng Software 40(6):438-444.

Lentzsch P, Wieland R, Wirth S. 2005. Application of multiple regression and neural network approaches for landscape-scale assessment of soil microbial biomass. Soil Biol Biochem. 37(9):1577-1580.

Liu X, Zhang G, Heathman GC, Wang Y, Huang CH. 2009. Fractal features of soil particle-size distribution as affected by plant communities in the forested region of Mountain Yimeng, China. Geoderma 154(1):123-130.

Nimmo JR, Perkins KS. 2002. Aggregate stability and size distribution. In: Warren AD, editor. Methods of Soil Analysis. Part 4. Physical Methods. Madison, WI: Soil Sci. Soc. Am., Inc. p. 317-328.

Parent LE, Parent SE, Kätterer T, Egozcue JJ. 2011. Fractal and compositional analysis of soil aggregation. In: Proceedings of the CoDaWork 2011, 4th International Workshop on Compositional Data Analysis; 2011 May 9-13; Girona, Spain; p. 1-14.

Prosperini N, Perugini D. 2008. Particle size distributions of some soils from the Umbria Region (Italy): fractal analysis and numerical modeling. Geoderma 145(3):185-195.

Rasiah V, Kay BD. 1994. Characterizing changes in aggregate stability subsequent to introduction of forages. Soil Sci Soc Am J. 58(3):935-942.

Rieu M, Sposito G. 1991. Fractal fragmentation, soil porosity, and soil water properties: II. Applications. Soil Sci Soc Am J. 55(5):1239-1244.

Saffari M, Yasrebi J, Sarikhani F, Gazni R, Moazallahi M, Fathi H, Emadi M. 2009. Evaluation of Artificial Neural Network Models for Prediction of Spatial Variability of Some Soil Chemical Properties. Res J Biol Sci. 4(7):815-820.

Silva RB, Iori P, Armesto C, Bendini HN. 2010. Assessing rainfall erosivity with artificial neural networks for the Ribeira Valley. Brazil. Intl J Agron.:1-7.

Sobhani J, Najimi M, Pourkhorshidi AR, Parhizkar T. 2010. Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models. Construc Build Mater. 24:709-718.

Sparks DL, Page AL, Helmke PA, Loeppert RH, Soltanpour PN, Tabatabai MA, Sumner ME. 1996. Methods of soil analysis. Part 3. Chemical methods. Madison, WI: Soil Science Society of America Inc.

Tracey JA, Zhu J, Crooks KR. 2011. Modeling and inference of animal movement using artificial neural networks. Environ Ecol Stat. 18(3):393-410.

Trasar-Cepeda, C, Leiros C, Gil-Sotres F, Seoane S. 1998. Towards a biochemical quality index for soils: an expression relating several biological and biochemical properties. Biol Fertil Soils 26(2):100-106.

Walkey JA, Black JA. 1934. Estimation of organic carbon by the chromic acid titration method. Soil Sci. 37:29-31.

Yilmaz I, Kaynar O. 2011. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Sys Appl. 38(5):5958-5966.

Zhao SW, Jing SU, Yang YH, Liu NN, Wu JS, Shangguan ZP. 2006. A fractal method of estimating soil structure changes under different vegetations on Ziwuling Mountains of the Loess Plateau, China. Agr Sci China 5(7):530-538.

Zornoza R, Mataix-Solera J, Guerrero C, Arcenegui V, García-Orenes F, Mataix-Beneyto J, Morugán A. 2007. Evaluation of soil quality using multiple lineal regression based on physical, chemical and biochemical properties. Sci Total Environ. 378(1):233-237.





Con el mecenazgo de
Universia
Avda. de Cantabria, s/n - 28660, Boadilla del Monte
Madrid, España
EMail: info@sjss.universia.net