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Modeling agricultural soil bulk density using artificial neural network and adaptive neuro-fuzzy inference system

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Abstract

Developing models that can accurately predict the soil bulk density using other soil parameters is of great importance given the arduous task of determining the soil bulk density. We conducted the factorial experiment based on the randomized complete block design with five replications to determine the factors that affect the soil bulk density (BD) in three types of soil texture: loam, sandy loan, and loamy sand. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were applied to predict and model the soil bulk density using several independent parameters that affect it, including the soil cone index (CI), moisture content (MC), and electrical conductivity (EC). The analysis indicated that the developed ANN using the Bayesian tuning algorithm with R2 = 0.93 is the most suitable model compared to other models that were created. We also performed the soil bulk density modeling using the three effective parameters of soil by ANFIS (adaptive neuro-fuzzy inference system) applying the hybrid method. The coefficient of determination for the ANFIS model was 0.988 (R2 = 0.98), which indicates the correct choice of the parameters affecting the soil bulk density. The comparison between the artificial neural network models and the neuro-fuzzy model developed in this study shows the complete advantage of ANFIS systems in predicting the soil bulk density as supported by the statistical parameters The results showed that ANN and ANFIS are highly capable to predict the soil bulk density in agricultural lands.

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Data availability

The data presented in this study are available on request from the corresponding author. The characteristics of the code used in this study has been described below and is available at the below link.

Name of the code: Soil Bulk Density.fis

Contact: abbaspour@uma.ac.ir and +98-9144516255

Hardware requirements: Operating system Windows 10, Disk space 2 GB and RAM 2 GB.

Program language: English

Software required: MATLAB 2017a

Program size: 17 KB

The source codes are available for downloading at the link: https://github.com/yabbaspour/AbbaspourGilandeh. Also, Matlab source code for this work is available as Supplementary Materials of this manuscript.

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Funding

This research was funded by University of Mohaghegh Ardabili.

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Authors and Affiliations

  1. Department of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

    Yousef Abbaspour-Gilandeh, Mohammadreza Abbaspour-Gilandeh & Gholamhossein Shahgoli

  2. Department of Geosciences, Georgia State University, Atlanta, GA, 30302-4105, USA

    Hassan A. Babaie

Authors
  1. Yousef Abbaspour-Gilandeh
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  2. Mohammadreza Abbaspour-Gilandeh
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  3. Hassan A. Babaie
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  4. Gholamhossein Shahgoli
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Contributions

Yousef Abbaspour-Gilandeh: conceptualization, methodology, validation, project administration, funding acquisition, writing-original draft preparation. Mohammadreza Abbaspour-Gilandeh: software, formal analysis, investigation, visualization. Hassan A. Babaie: conceptualization, methodology, validation, writing—review and editing. Gholamhossein Shahgoli: methodology, formal analysis, investigation, resources, writing—original draft preparation. The manuscript revised by all authors.

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Correspondence to Yousef Abbaspour-Gilandeh.

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Communicated by: H. Babaie

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Abbaspour-Gilandeh, Y., Abbaspour-Gilandeh, M., Babaie, H.A. et al. Modeling agricultural soil bulk density using artificial neural network and adaptive neuro-fuzzy inference system. Earth Sci Inform 16, 57–65 (2023). https://doi.org/10.1007/s12145-022-00920-6

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