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New empirical equation to estimate the soil moisture content based on thermal properties using machine learning techniques

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Abstract

Information about soil moisture content is crucial for the sustenance of agricultural system because it helps to make decision on irrigation scheduling and water management. However, the conventional procedures for determining the soil moisture content need much effort, and time-consuming with large dataset. It is known that soil thermal properties have significant influence on the moisture content of soil. Therefore, the soil moisture content can be determined based on the soil thermal properties, which can easily be measured with portable equipment known as KD2 Pro. This study presents an alternative technique for estimating the soil moisture content from thermal properties using machine learning (ML). Actual measurements of moisture contents and thermal properties at seventy-five points were used. Three ML techniques including artificial neural network (ANN), fuzzy logic (FL), and support vector machine (SVM) were used to predict the moisture content of soil from its thermal properties (thermal conductivity, thermal diffusivity, and specific heat). The results show that all the three techniques (ANN, FL, and SVM) were able to predict moisture content with acceptable errors where the average absolute error is around 5.65%. Moreover, a new empirical equation is presented to allow quick estimation of the moisture content. Ultimately, the developed models can be employed to predict the soil moisture content in any farmland with known thermal properties, which will lead to cost reduction and less time and effort to determine soil moisture content.

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Abbreviations

AAPE:

average absolute percentage error

AI:

artificial intelligence

ANFIS:

adaptive neuro-fuzzy inference system

ANN:

artificial neural network

CDF:

cumulative distribution function

CV:

coefficient of variation

FL:

fuzzy logic system

PDF:

probability density function

RMSE:

root mean square error

R-value:

correlation coefficient

SD:

standard deviation

SVM:

support vector machine

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Acknowledgments

Authors would like to acknowledge the College of Petroleum and Geosciences (CPG) at King Fahd University of Petroleum and Minerals (KFUPM) for the technical supports and provision of valuable software.

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Authors and Affiliations

  1. Boone Pickens School of Geology, Oklahoma State University, Stillwater, OK, 74078, USA

    Oluseun A. Sanuade

  2. Petroleum Engineering Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

    Amjed M. Hassan & Abdulazeez Abdulraheem

  3. Mewbourne School of Petroleum and Geological Engineering, The University of Oklahoma, Norman, OK, 73019, USA

    Adesoji O. Akanji

  4. Department of Earth Sciences, Ajayi Crowther University, Oyo, Nigeria

    Abayomi A. Olaojo

  5. Department of Geology, University of Ibadan, Ibadan, Nigeria

    Michael A. Oladunjoye

Authors
  1. Oluseun A. Sanuade
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  2. Amjed M. Hassan
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  3. Adesoji O. Akanji
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  4. Abayomi A. Olaojo
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  5. Michael A. Oladunjoye
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  6. Abdulazeez Abdulraheem
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Correspondence to Oluseun A. Sanuade.

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Responsible Editor: Biswajeet Pradhan

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Sanuade, O.A., Hassan, A.M., Akanji, A.O. et al. New empirical equation to estimate the soil moisture content based on thermal properties using machine learning techniques. Arab J Geosci 13, 377 (2020). https://doi.org/10.1007/s12517-020-05375-x

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