Comparison of artificial neural network and multiple linear regressions efficiency for predicting soil salinity in Yazd -Ardakan plain, central Iran

Authors

1 Combat to desertification, Natural Resource Faculty, Ardakan University, Yazd, Iran

2 Department of soil science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran

3 Department of Soil Science, College of Agriculture, Shahid Chamran University of Ahvaz, Khuzestan, Iran

Abstract

The study was conducted to evaluate the efficacy of artificial neural network (ANN) and multivariate regression (MLR) analysis to predict spatial variability of soil salinity in central Iran, using remotely sensed data. The analysis was based on data acquired from EOS AMI remote sensing satellite. The two methods was used to study linear and non-linear relationship between soil reflectance and soil salinity. In MLR analysis, stepwise method and neural network were applied using sensitivity coefficient by arranging inputs through the backward propagation, and then modeling was done. The R2 and RMSE were 0.23 and 0.33 for MLR, and 0.79 and 0.11 for ANN, respectively. Digital values of VNIR1 and NDVI48 were identified as the most important factors in MLR, whereas Sum19 and SWIR6 were recognized as the most important data to predict soil salinity using ANN. The results indicated that ANN model is used to detect non- linear relationship between soil salinity and ASTER data at the study area.

Keywords

References

Adams M, Zhao F, McGrath S, Nicholson F Chambers B, 2004, Predicting cadmium concentrations in wheat and barley grain using soil properties, Journal of Environmental Quality, 33: 532-541.
Allbed A, Kumar L Sinha P, 2014, Mapping and modelling spatial variation in soil salinity in the Al Hassa Oasis based on remote sensing indicators and regression techniques, Remote Sensing, 6: 1137-1157.
Arshad R R, Sayyad G, Mosaddeghi M Gharabaghi B, 2013, Predicting saturated hydraulic conductivity by artificial intelligence and regression models, ISRN Soil Science, 2013.
Douaoui A E K, Nicolas H Walter C, 2006, Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data, Geoderma, 134: 217-230.
Freire S, de Lisboa N, Fonseca I, Brasil R, Rocha J Tenedório J A. Using Artificial Neural Networks for Digital Soil Mapping–a comparison of MLP and SOM approaches. 2013. AGILE.
Haykin S Network N, 2004, A comprehensive foundation, Neural Networks, 2.
Kalkhajeh Y K, Arshad R R, Amerikhah H Sami M, 2012, Comparison of multiple linear regressions and artificial intelligence-based modeling techniques for prediction the soil cation exchange capacity of Aridisols and Entisols in a semi-arid region, Australian journal of agricultural engineering, 3: 39.
Khan N M, Rastoskuev V V, Sato Y Shiozawa S, 2005, Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators, Agricultural Water Management, 77: 96-109.
Lake H R, Akbarzadeh A Mehrjardi R T, 2009, Development of pedo transfer functions (PTFs) to predict soil physico-chemical and hydrological characteristics in southern coastal zones of the Caspian Sea, Journal of Ecology and the Natural Environment, 1: 160-172.
Mata J, 2011, Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models, Engineering Structures, 33: 903-910.
Metternicht G Zinck J, 2003, Remote sensing of soil salinity: potentials and constraints, Remote sensing of Environment, 85: 1-20.
Miao Y, Mulla D J Robert P C, 2006, Identifying important factors influencing corn yield and grain quality variability using artificial neural networks, Precision Agriculture, 7: 117-135.
Navarro‐Pedreño J, Jordan M, Meléndez‐Pastor I, Gomez I, Juan P Mateu J, 2007, Estimation of soil salinity in semi‐arid land using a geostatistical model, Land Degradation & Development, 18: 339-353.
Pachepsky Y A, Timlin D Varallyay G, 1996, Artificial neural networks to estimate soil water retention from easily measurable data, Soil Science Society of America Journal, 60: 727-733.
Ren D Abdelsalam M G, 2006, Tracing along-strike structural continuity in the Neoproterozoic Allaqi-Heiani Suture, southern Egypt using principal component analysis (PCA), fast Fourier transform (FFT), and redundant wavelet transform (RWT) of ASTER data, Journal of African Earth Sciences, 44: 181-195.
Rouse Jr J, 1974, Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation.
Rumelhart J Clelland  M 1986. Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1 and 2. Cambridge: MIT Press.
Shahabi M, Jafarzadeh A A, Neyshabouri M R, Ghorbani M A Valizadeh Kamran K, 2017, Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods, Archives of Agronomy and Soil Science, 63: 151-160.
Tajgardan T, Ayoubi S, Shataee S, Sahrawat K L Gorgan I, 2010, Soil surface salinity prediction using ASTER data: Comparing statistical and geostatistical models, Australian Journal of Basic and Applied Sciences, 4: 457-467.
Tajgardan T, Shataee S Ayoubi S. In spatial prediction of soil salinity in the arid zones using ASTER data, case study: north of Ag Ghala, Golestan Province, Iran.  Proceedings of Asian Conference on Remote Sensing (ACRS), Kuala Lumpur, Malaysia, 2007.
Tayebi M H, Tangestani M H Roosta H 2010. Envirnomental impact assessment using Neural Network Model: A case study of the Jahani, konarsiah and Kohe Gach salt plugs, SE Shiraz, Iran, na.
Were K, Bui D T, Dick Ø B Singh B R, 2015, A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape, Ecological Indicators, 52: 394-403.
Wilding L. Spatial variability: its documentation, accommodation and implication to soil surveys.  Soil spatial variability. Workshop, 1985. 166-194.
Yang H, Griffiths P R Tate J, 2003, Comparison of partial least squares regression and multi-layer neural networks for quantification of nonlinear systems and application to gas phase Fourier transform infrared spectra, Analytica Chimica Acta, 489: 125-136.
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, Science of the Total Environment, 378: 233-237.
Desert Ecosystem Engineering
Volume 7, Issue 1in English
September 2018
Pages 20-11

Files

Share

How to cite

Statistics