Abstract
ABSTRACTA genetic-based neural network ensemble (GNNE) is applied for estimation of daily soil temperatures (DST) at distinct depths. A sequential genetic-based negative correlation learning algorithm (SGNCL) is adopted to train the GNNE parameters. CLMS algorithm is used to achieve the optimum weights of components. Recorded data for two different stations located in Iran are used for the development of the GNNE models. Furthermore, the GNNE predictions are compared with the existing machine-learning models. The results demonstrate that GNNE outperforms other methods for the prediction of DSTs.
Highlights
Accurate estimation of soil temperature plays a vital role in many scientific fields such as agriculture, hydrology, geotechnics, solar and geothermal energy
The proficiency of the genetic-based neural network ensemble (GNNE) models is assessed by comparing with two evolutionary models such as extreme learning machine (ELM) and self-adaptive evolutionary (SaE)-EL
The results demonstrate that the GNNE model provides significant level of precision in predicting daily soil temperatures in all depths for both stations as well
Summary
Accurate estimation of soil temperature plays a vital role in many scientific fields such as agriculture, hydrology, geotechnics, solar and geothermal energy. Soil temperature depends on several factors such as meteorological conditions, physical soil parameters, topographical parameters, and further hydrological parameters of time and depth. The variation of soil temperature at different depths provides useful information about the land-surface ecosystem processes, environmental and climatic conditions, climate change, and production of crops. Soil temperature controls the interactive processes between ground and atmosphere. Many meteorological variables including relative humidity, air temperature and atmospheric pressure are measured in meteorological sites, measurements of soil temperature data or its spatial variability are not usually available. Developing theoretical methods to estimate soil temperature from the existing meteorological data is vital
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