ORIGINAL PAPER New empirical equation to estimate the soil moisture content based on thermal properties using machine learning techniques Oluseun A. Sanuade 1 & Amjed M. Hassan 2 & Adesoji O. Akanji 3 & Abayomi A. Olaojo 4 & Michael A. Oladunjoye 5 & Abdulazeez Abdulraheem 2 Received: 1 October 2019 /Accepted: 28 April 2020 # Saudi Society for Geosciences 2020 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. Keywords Moisture content . Artificial intelligence . Thermal properties . Predictive models Introduction The measurement of moisture content of soil plays vital role in several activities including expectation of crop yield, forecasting of flood, forecasting of erosion and slope failure, and management of water reservoir (Zaidi and Masmoudi 2011; Noor and Al-Moubaraki 2014; Khazaei and Moayedi 2017; Hamouda and Phillips 2018). This has prompt various sectors such as agriculture, biological, and environmental mon- itoring, local meteorology, geology, and hydrology to require reliable information of moisture content of soil in order to properly execute their activities. The knowledge of moisture content of soil is very important for the sustenance of any agricultural system. This in turn can be essential for determin- ing the factors that can be used to make decision regarding scheduling of irrigation and management of water for better crop yield, improved quality of crop, conservation of water, and in general saving of cost (Evans and Sadler 2008; Greaves and Wang 2017). Thus, it is very important to be monitoring the obtainable water because plants usually start experiencing water stress immediately below the wilting point, and this is detrimental to the condition of soil and hinder plant growth. Therefore, there is need to estimate the moisture content either on the field or in the laboratory. However, monitoring of Responsible Editor: Biswajeet Pradhan * Oluseun A. Sanuade sheunsky@gmail.com 1 Boone Pickens School of Geology, Oklahoma State University, Stillwater, OK 74078, USA 2 Petroleum Engineering Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia 3 Mewbourne School of Petroleum and Geological Engineering, The University of Oklahoma, Norman, OK 73019, USA 4 Department of Earth Sciences, Ajayi Crowther University, Oyo, Nigeria 5 Department of Geology, University of Ibadan, Ibadan, Nigeria Arabian Journal of Geosciences (2020) 13:377 https://doi.org/10.1007/s12517-020-05375-x