Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India

Autor:	Anurag Malik, Yazid Tikhamarine, Parveen Sihag, Shamsuddin Shahid, Mehdi Jamei, Masoud Karbasi
Rok vydání:	2021
Předmět:	Machine Learning Soil Health Toxicology and Mutagenesis Temperature Environmental Chemistry General Medicine Neural Networks Computer Pollution Algorithms
Zdroj:	Environmental science and pollution research internationalReferences. 29(47)
ISSN:	1614-7499
Popis:	Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict the daily ST at 5-cm (ST
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c2e2a0d09d5b90f345b25df32f27c03e https://pubmed.ncbi.nlm.nih.gov/35597830 Zobrazit plný text záznamu Full text from SpringerLink