Prediction of Soil Cation Exchange Capacity Using Different Soil Parameters by Intelligent Models-Reference-Cited by-同舟云学术

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Prediction of Soil Cation Exchange Capacity Using Different Soil Parameters by Intelligent Models

Published:2019-08-20 Issue:17 Volume:50 Page:2123-2139
ISSN:0010-3624
Container-title:Communications in Soil Science and Plant Analysis
language:en
Short-container-title:Communications in Soil Science and Plant Analysis

Author:

Seyed Jalali Seyed Alireza¹,Navidi Mir Naser¹,Seyed Mohammadi Javad²,Meymand Ali Zeinadini¹,Mohammad Esmail Zahra²

Affiliation:

1. Member of Scientific Board, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

2. Researcher of Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

Publisher

Informa UK Limited

Subject

Soil Science,Agronomy and Crop Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/00103624.2019.1654501

Reference48 articles.

1. Neural network models to predict cation exchange capacity in arid regions of Iran

2. Using pattern recognition for estimating cultivar coefficients of a crop simulation model

3. Geochemistry at the Earth’s Surface

4. Prediction of CEC Using Fractal Parameters by Artificial Neural Networks

5. Prediction of Water Retention of Soils from the Humid Tropics by the Nonparametric k -Nearest Neighbor Approach

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1. Random forest, an efficient smart technique for analyzing the influence of soil properties on pistachio yield;Environment, Development and Sustainability;2024-01-16

2. A new robust hybrid model based on support vector machine and firefly meta-heuristic algorithm to predict pistachio yields and select effective soil variables;Ecological Informatics;2023-05

3. Rapid elemental prediction of heterogeneous tropical soils from pXRF data: a comparison of models via linear regressions and machine learning algorithms;Soil Research;2023-03-27

4. Machine learning for cation exchange capacity prediction in different land uses;CATENA;2022-09

5. Estimating Quantity of Date Yield Using Soil Properties by Regression and Artificial Neural Network;Communications in Soil Science and Plant Analysis;2022-08-06

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