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  • 1.
    Penghui, Liu
    et al.
    Computer Science Department, Baoji University of Arts and Sciences, Baoji, China.
    Ewees, Ahmed A.
    Computer Department, Damietta University, Damietta, Egypt.
    Hamiye Beyaztas, Beste
    Department of Statistics, Istanbul Medeniyet University, Istanbul, Turkey.
    Qi, Chongchong
    School of Resources and Safety Engineering, Central South University, Changsha, China.
    Salih, Sinan Q.
    Institute of Research and Development, Duy Tan University, Da Nang, Vietnam. Computer Science Department, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq .
    Al-Ansari, Nadhir
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Bhagat, Suraj Kumar
    Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
    Yaseen, Zaher Mundher
    Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
    Singh, Vijay P.
    Department of Biological and Agricultural Engineering, Texas A&M University, College Station, USA. Zachry Department of Civil Engineering, Texas A&M University, College Station, USA.
    Metaheuristic Optimization Algorithms Hybridized With Artificial Intelligence Model for Soil Temperature Prediction: Novel Model2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 51884-51904Article in journal (Refereed)
    Abstract [en]

    An enhanced hybrid articial 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 signicant 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 ve 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 Dragon y 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 articial intelligence model for predicting soil temperature based on univariate air temperature scenario.

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