Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms

Alizamir, Meysam; Ahmed, Kaywan Othman; Kim, Sungwon; Heddam, Salim; Gorgij, AliReza Docheshmeh; Chang, Sun Woo

doi:10.1371/journal.pone.0293751

Cited by 1 publication

(1 citation statement)

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“…However, these temperature levels are not monitored by most of the developing countries. In addition, some of the existing monitoring stations are not properly maintained 21 , therefore, the soil temperature recordings along the depth are minimal. However, understanding the temperature variation for the first 10 cm layer would help the researchers to project the adverse impact 22 .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence to predict soil temperatures by development of novel model

Mampitiya,

Rozumbetov,

Rathnayake

et al. 2024

Sci Rep

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Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan.

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Section: Introductionmentioning

confidence: 99%