Maqola

Artificial intelligence to predict soil temperatures by development of novel model

Lakindu MampitiyaWater Resources Management and Soft Computing Research Laboratory, Athurugiriya, Millennium City, 10150, Sri LankaKenjabek RozumbetovDepartment of Anatomy, Physiology and Biochemistry of Animals, Nukus Branch of Samarkand State University of Veterinary Medicine, Animal Husbandry and Biotechnology, 230100, Nukus, UzbekistanNamal RathnayakeDepartment of Civil Engineering, Faculty of Engineering, The University of Tokyo, 1 Chome-1-1 Yayoi, Bunkyo City, Tokyo, 113-8656, JapanValery ErkudovDepartment of Normal Physiology, St. Petersburg State Pediatric Medical University, 194100, Saint Petersburg, RussiaAdilbay T. EsimbetovDepartment of Anatomy, Physiology and Biochemistry of Animals, Nukus Branch of Samarkand State University of Veterinary Medicine, Animal Husbandry and Biotechnology, 230100, Nukus, UzbekistanShanika ArachchiDepartment of Electronics and Mechanical Engineering, Faculty of Engineering and Technology, Atlantic Technological University, Letterkenny, F92 FC93, IrelandKomali KantamaneniSchool of Engineering, University of Central Lancashire, Preston, PR1 2HE, UKYukinobu HoshinoSchool of Systems Engineering, Kochi University of Technology, 185 Miyanokuchi, Tosayamada, Kami, Kochi, 782-8502, JapanUpaka RathnayakeDepartment of Civil Engineering and Construction, Faculty of Engineering and Design, Atlantic Technological University, Sligo, F91 YW50, Ireland. [email protected]

Scientific Reportsjournal2024en

ABI

Annotatsiya

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.

Hali tarjima qilinmagan

Mavzular

Engineering and Agricultural Innovations Smart Agriculture and AI Irrigation Practices and Water Management

Identifikatorlar

DOI: 10.1038/s41598-024-60549-x

Iqtiboslar va manbalar

0 ta iqtibos47 ta foydalanilgan manba

Koʻrsatkichlar — AkademScholar · Tez orada