Artificial Neural Network (ANN) based Soil Temperature model of Highly Plastic Clay

ABSTRACT Artificial neural networks (ANNs) are one of the popular methods of artificial intelligence that seek to follow the human mind function and nervous system with its successful application increased in many areas of engineering. The current study is focused to develop an ANN-based predictive model of the soil temperature of Yazoo clay using based on field instrumentation data. To provide an acceptable dataset for developing the predictive model, the investigation was carried out in six instrumented slopes within the 25 miles (40.2 km) radius from metropolitan Jackson in Mississippi. The six selected slopes were instrumented with soil moisture sensors, automated rain gauge, air, and soil temperature sensors starting from mid-August 2018. Volumetric moisture content, precipitation, air, and soil temperature values at 1.5 m (5 ft) depth at the crest of the six slopes were collected using automated data loggers and observed for more than seventeen months. The established database consisting of 13650 datasets was implemented for ANN intelligent system and multiple-degree Fourier series non-linear regression technique for predicting the hourly soil temperature. The total hourly natural rainfall and time, average previous soil temperature, and average hourly air temperature were set to be the inputs of the model and the hourly soil temperature was set to be the output of the model. These datasets were used as the training data and validated with each target slope. Sensitivity analysis was also conducted, and the most influential input parameters on the data output were determined. In this study, the change of soil temperature with atmospheric temperature was investigated, and a predictor model was developed by adopting the Levenberg-Marquardt (LM) algorithm method and the Tan-sigmoid transfer function. The developed ANNs model showed an excellent fit with the observed field values.