Mampitiya Lakindu, Rozumbetov Kenjabek, Rathnayake Namal, Erkudov Valery, Esimbetov Adilbay, Arachchi Shanika, Kantamaneni Komali, Hoshino Yukinobu, Rathnayake Upaka
Water Resources Management and Soft Computing Research Laboratory, Athurugiriya, Millennium City, 10150, Sri Lanka.
Department of Anatomy, Physiology and Biochemistry of Animals, Nukus Branch of Samarkand State University of Veterinary Medicine, Animal Husbandry and Biotechnology, 230100, Nukus, Uzbekistan.
Sci Rep. 2024 Apr 30;14(1):9889. doi: 10.1038/s41598-024-60549-x.
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.
在不断变化的环境中，地表和不同深度的土壤温度对于了解土壤的生物、化学和物理性质至关重要。这对于实现粮食可持续性至关重要。然而，由于仪器设备简陋以及许多其他不可避免的原因，如干旱、洪水和飓风等自然灾害，全球大多数发展中地区在建立可靠的数据测量和记录方面面临困难。因此，一个准确的预测模型将解决这些困难。乌兹别克斯坦因其干旱的气候而成为关注气候变化的国家之一。因此，本研究首次提出了一个基于乌兹别克斯坦努库斯气候因素的综合模型，用于预测地表和10厘米深度的土壤温度水平。为了基于广泛使用的性能指标了解最佳性能模型，对八个机器学习模型进行了训练。长短期记忆（LSTM）模型在准确预测10厘米深度的土壤温度水平方面表现出色。更重要的是，这里开发的模型可以利用实测气候数据和预测的地表土壤温度水平来预测10厘米深度的温度水平。该模型无需任何地面土壤温度测量即可预测10厘米深度的土壤温度。所开发的模型可有效地用于规划应用，以实现乌兹别克斯坦努库斯等干旱地区粮食生产的可持续性。
