Evidence map›Paper›PMID 42115642›Full record

ArticleScientific reports2026

Monthly soil temperature modelling across different depths using machine learning algorithms in northeast India.

Abhaya Kumar Pradhan, Deepak Jhajharia, Kiran Bala Behura, Subhankar Ghosh, Muhammad Touseef, Laishram Kanta Singh, Sougrakpam Roma Devi, Mainak Ghosh, Amulya Kumar Mohanty, Ghanashyam Singh Yurembam and 3 more

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Abhaya Kumar PradhanDepartment of Soil and Water Conservation Engineering, College of Agricultural Engineering & Technology, OUAT, Bhubaneswar, Odisha, 751003, India.
Deepak JhajhariaDepartment of Soil and Water Conservation Engineering, College of Agricultural Engineering & Post Harvest Technology, CAU, Ranipool, Gangtok, Sikkim, 737135, India.ORCID http://orcid.org/0000-0003-1111-8648
Kiran Bala BehuraDepartment of Soil and Water Conservation Engineering, College of Agricultural Engineering & Technology, OUAT, Bhubaneswar, Odisha, 751003, India.ORCID http://orcid.org/0009-0007-3425-1376
Subhankar GhoshAgricultural and Food Engineering Department, IIT Kharagpur, Kharagpur, West Bengal, 721302, India. subhankarghosh1994@yahoo.in.ORCID http://orcid.org/0000-0001-7083-9555
Muhammad TouseefSchool of Civil Engineering and Architecture, Guangxi University, Nanning, 530004, China.ORCID http://orcid.org/0000-0002-6144-1358
Laishram Kanta SinghICAR-Krishi Vigyan Kendra, Indian Council of Agricultural Research Complex for NEH Region, Lamphelpat Imphal, Manipur, 795004, India.ORCID http://orcid.org/0000-0002-4340-9768
Sougrakpam Roma DeviICAR-Krishi Vigyan Kendra, Indian Council of Agricultural Research Complex for NEH Region, Lamphelpat Imphal, Manipur, 795004, India.
Mainak GhoshICAR-Agricultural Technology Application Research Institute, Zone-VII Indian Council of Agricultural Research, Umiam (Barapani), Meghalaya, 793103, India.ORCID https://orcid.org/0000-0002-3348-975X
Amulya Kumar MohantyICAR-Agricultural Technology Application Research Institute, Zone-VII Indian Council of Agricultural Research, Umiam (Barapani), Meghalaya, 793103, India.
Ghanashyam Singh YurembamDepartment of Soil and Water Conservation Engineering, College of Agricultural Engineering & Post Harvest Technology, CAU, Ranipool, Gangtok, Sikkim, 737135, India.ORCID http://orcid.org/0000-0003-1835-3149
T Loidang ChanuDepartment of Basic Engineering and Applied Sciences, College of Agricultural Engineering &, Post Harvest Technology, CAU, Ranipool, Gangtok, Sikkim, 737135, India.
Shivam GuptaDepartment of Irrigation and Drainage Engineering, Mahamaya College of Agricultural Engineering and Technology, ANDUAT, Kumarganj, Ayodhya, Uttar Pradesh, 224229, India.ORCID http://orcid.org/0000-0002-6026-1523
Ghanshyam T PatleDepartment of Soil and Water Conservation Engineering, College of Agricultural Engineering & Post Harvest Technology, CAU, Ranipool, Gangtok, Sikkim, 737135, India.ORCID http://orcid.org/0000-0002-9175-8567

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soil temperature is one of the most crucial factors for agricultural systems, which affects various hydrogeological processes in soil. Soil temperature is a frequently used variable in machine learning (ML) approaches for modelling those intricate processes. In regions with limited data, such as northeast India, soil temperature is a meteorological variable that is rarely available. The present study aims at soil temperature modelling in three different soil depths (5, 15 and 30 cm), using eight long-term meteorological variables, for example, minimum, mean and maximum air temperatures, rainfall, sunshine hours, minimum and maximum relative humidity, and number of rainy days over the Tochlai region of Jorhat, Assam (northeast India). To accomplish this goal, five suitable ML techniques, for example, Random Forest Regression (RFR), Support Vector Regression (SVR), Boosted Regression Trees (BRT), Classification And Regression Tree (CART), and Bayesian Neural Network (BNN) were employed. Results indicate that the RFR (R

Indexed as

Accumulated local effectsBayesian neural networkMachine learningNortheast IndiaRandom forest regressionSoil temperature prediction

Identifiers

PMID42115642
PMCPMC13350838

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.