Model-based soil temperature estimation using climatic parameters: the case of Azerbaijan Province, Iran-Reference-Cited by-同舟云学术

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Model-based soil temperature estimation using climatic parameters: the case of Azerbaijan Province, Iran

Published:2019-04-25 Issue:3 Volume:4 Page:203-215
ISSN:2474-9508
Container-title:Geology, Ecology, and Landscapes
language:en
Short-container-title:Geology, Ecology, and Landscapes

Author:

Sihag Parveen¹^ORCID,Esmaeilbeiki Fatemeh²,Singh Balraj³^ORCID,Pandhiani Siraj Muhammed⁴

Affiliation:

1. Civil Engineering Department, National Institute of Technology, Kurukshetra, India

2. Department of Soil Science and Engineering, University of Tabriz, Tabriz, Iran

3. Department of Civil Engineering, Panipat Institute of Engineering and Technology, Panipat, India

4. Department of General Studies, University College Jubail, Jubail, Saudi Arabia

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/24749508.2019.1610841

Reference44 articles.

1. An artificial neural network for classifying and predicting soil moisture and temperature using Levenberg-Marquardt algorithm

2. Prediction of soil temperature using regression and artificial neural network models

3. Bagging predictors

4. Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation

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1. Modeling Zn Availability and Uptake by Citrus Plants Using Easily Measured Soil Characteristics;Environmental Modeling & Assessment;2024-03-15

2. Multi-scale habitat modeling framework for predicting the potential distribution of sheep gastrointestinal nematodes across Iran’s three distinct climatic zones: a MaxEnt machine-learning algorithm;Scientific Reports;2024-02-03

3. Discussion of “A Data-Driven Influential Factor Analysis Method for Fly Ash–Based Geopolymer Using Optimized Machine-Learning Algorithms”;Journal of Materials in Civil Engineering;2023-10

4. Extreme learning machine for soil temperature prediction using only air temperature as input;Environmental Monitoring and Assessment;2023-07-16

5. A Review of Machine Learning Approaches to Soil Temperature Estimation;Sustainability;2023-05-07

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