Work

Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical grazinglands agro-ecosystems

Year: 2024

Type: article

Abstract: Soil temperature is a key meteorological parameter that plays an important role in determining rates of physical, chemical and biological reactions in the soil. Ground temperature can vary substantially under different land cover types and climatic conditions. Proper prediction of soil temperature is thus essential for the accurate simulation of land surface processes. In this study, two intelligent neural models-artificial neural networks (ANNs) and Sperm Swarm Optimization (SSO) were used for estimating of soil temperatures at four depths (5, 10, 20, 50 cm) using seven-year meteorological data acquired from Archbold Biological Station in South Florida. The results of this study in subtropical grazinglands of Florida showed that the integrated artificial neural network and SSO models (MLP-SSO) were more accurate tools than the original structure of artificial neural network methods for soil temperature forecasting. In conclusion, this study recommends the hybrid MLP-SSO model as a suitable tool for soil temperature prediction at different soil depths.

Source: Scientific Reports

DOI: 10.1038/s41598-023-48025-4

Authors: Seyed Mostafa Biazar, Hisham A. Shehadeh, Mohammad Ali Ghorbani, Golmar Golmohammadi, Amartya Saha

Institutions: University of Florida, Amman Arab University, University of Tabriz, Institute of Food and Agricultural Sciences, Archbold Biological Station

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Zero hunger, Climate action

Open Access status: gold

Estimated APC paid: $2,590