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

Liu Penghui,Suraj Kumar Bhagat,Sinan Q Salih,Beste Hamiye Beyaztas,Nadhir Al-Ansari,Zaher Mundher Yaseen,Chongchong Qi,Vijay P Singh,Ahmed A Ewees

doi:10.1109/access.2020.2979822

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%, 74.4%, 71.2%, 76.7% and 80.7% 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.

Highlights

Soil temperature (ST) controls most of the biological, chemical, and physical processes within the soil [1], such asThe associate editor coordinating the review of this manuscript and approving it for publication was Nuno Garcia .evapotranspiration, plant germination/growth, root development, and microbial activities is evident [2]
This study aimed to predict ST using a univariate modeling scheme by incorporating only the air temperature information. It was evaluated the ability of the proposed hybrid intelligence model (i.e., adaptive neuro-fuzzy inference system (ANFIS)-mSG) to predict ST at different meteorological stations (i.e., Baker, Beach, Cando, Crary and Fingal) of North Dakota (ND), USA
The proposed hybrid intelligence model was validated against several benchmark models (i.e., ANFIS, ANFIS-DA, ANFIS-GA, ANFIS-GO, ANFIS-GWO, ANFIS-PSO and ANFIS-Salp Swarm Algorithm (SSA))

Summary

Introduction

Soil temperature (ST) controls most of the biological, chemical, and physical processes within the soil [1], such asThe associate editor coordinating the review of this manuscript and approving it for publication was Nuno Garcia .evapotranspiration, plant germination/growth, root development, and microbial activities is evident [2]. Soil temperature (ST) controls most of the biological, chemical, and physical processes within the soil [1], such as. The associate editor coordinating the review of this manuscript and approving it for publication was Nuno Garcia. Evapotranspiration, plant germination/growth, root development, and microbial activities is evident [2]. ST is the most important variable that influences the freezing depth and affects agricultural activities, irrigation scheduling, and soil drainage [3]. Several factors, including topography, air temperature, solar radiation, precipitation, soil moisture, and. L. Penghui et al.: Metaheuristic Optimization Algorithms Hybridized With Artificial Intelligence Model for ST Prediction thermal characteristics, govern ST [4], [5]. Attempts have focused on finding the relation between soil temperature and other variables for prediction of ST [6]

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Results

Conclusion