Metaheuristic Optimization Algorithms Hybridized With Artificial Intelligence Model for Soil Temperature Prediction: Novel Model

Abstract

An enhanced hybrid artificial intelligence model was developed for soil temperature (ST) prediction. Among several soil characteristics, soil temperature is one of the essential elements impacting the biological, physical and chemical processes of the terrestrial ecosystem. Reliable ST prediction is significant for multiple geo-science and agricultural applications. The proposed model is a hybridization of adaptive neuro-fuzzy inference system with optimization methods using mutation Salp Swarm Algorithm and Grasshopper Optimization Algorithm (ANFIS-mSG). Daily weather and soil temperature data for nine years (1 of January 2010 - 31 of December 2018) from five meteorological stations (i.e., Baker, Beach, Cando, Crary and Fingal) in North Dakota, USA, were used for modeling. For validation, the proposed ANFIS-mSG model was compared with seven models, including classical ANFIS, hybridized ANFIS model with grasshopper optimization algorithm (ANFIS-GOA), salp swarm algorithm (ANFIS-SSA), grey wolf optimizer (ANFIS-GWO), particle swarm optimization (ANFIS-PSO), genetic algorithm (ANFIS-GA), and Dragonfly Algorithm (ANFIS-DA). The ST prediction was conducted based on maximum, mean and minimum air temperature (AT). The modeling results evidenced the capability of optimization algorithms for building ANFIS models for simulating soil temperature. Based on the statistical evaluation; for instance, the root mean square error (RMSE) was reduced by 73 & x0025;, 74.4 & x0025;, 71.2 & x0025;, 76.7 & x0025; and 80.7 & x0025; for Baker, Beach, Cando, Crary and Fingal meteorological stations, respectively, throughout the testing phase when ANFIS-mSG was used over the standalone ANFIS models. In conclusion, the ANFIS-mSG model was demonstrated as an effective and simple hybrid artificial intelligence model for predicting soil temperature based on univariate air temperature scenario.