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Metaheuristic Optimization Algorithms Hybridized With Artificial Intelligence Model for Soil Temperature Prediction: Novel Model
Computer Science Department, Baoji University of Arts and Sciences, Baoji, China.ORCID-id: 0000-0002-2699-9460
Computer Department, Damietta University, Damietta, Egypt.ORCID-id: 0000-0002-0666-7055
Department of Statistics, Istanbul Medeniyet University, Istanbul, Turkey.ORCID-id: 0000-0002-6266-6487
School of Resources and Safety Engineering, Central South University, Changsha, China.ORCID-id: 0000-0001-5189-1614
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2020 (engelsk)Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 8, s. 51884-51904Artikkel i tidsskrift (Fagfellevurdert) Published
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.

sted, utgiver, år, opplag, sider
2020. Vol. 8, s. 51884-51904
Emneord [en]
Air temperature, soil temperature, hybrid intelligence model, metaheuristic, North Dakota
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URN: urn:nbn:se:ltu:diva-78129DOI: 10.1109/ACCESS.2020.2979822Scopus ID: 2-s2.0-85082515985OAI: oai:DiVA.org:ltu-78129DiVA, id: diva2:1416144
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Validerad;2020;Nivå 2;2020-04-21 (alebob)

Tilgjengelig fra: 2020-03-22 Laget: 2020-03-22 Sist oppdatert: 2020-04-21bibliografisk kontrollert

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