Prediction of Cadmium Content Using Machine Learning Methods

Author:

Keçeci Mehmet1,Gökmen fatih2,Usul Mustafa3,Koca Celal1,Uygur veli4

Affiliation:

1. Directorate of Soil, Fertilizer and Water Resources Research Institute, Yenimahalle, Ankara, Turkiye

2. Department of Soil Science and Plant Nutrition, Agricultural Faculty, Iğdır University, Iğdır, Turkiye

3. General Directorate of Agricultural Reform, the Ministry of Agriculture and Forestry, Çankaya, Ankara, Turkiye.

4. Department of Soil Science and Plant Nutrition, Agricultural Faculty, Isparta University of Applied Sciences, Isparta, Turkiye

Abstract

Abstract Heavy metals are the most environmentally hazardous pollution type in agricultural soils, threatening human and ecological health. Cadmium (Cd) is a highly toxic element but distinctively different with its high mobility in soil environments. The study aimed to evaluate the Cd concentration of Konya plain soils with a specific attribute to soil fertilization practices, mainly phosphorous fertilizers. A total of 538 surface (0-20 cm) soil samples were analysed for the routine soil properties and total phosphorus (P) and Cd. Descriptive statistics, machine learning and regression models considered the accumulation of Cd in soils. Among the MARS, Decision Trees, Linear Regression, Random Forest, and XGBoost machine learning methods used in Cd prediction, the XGBoost model proved to be the best prediction model with a coefficient of determination of 98.1%. EC, pH, CaCO3, silt, and P2O5, which are the soil components used in Cd estimation of XGBoost model, explained 56.51% of the total variance in relation to measured soil properties. Therefore machine learning processes could be a useful tool to estimate the nature of an element in the soils of a specific region by using routine soil properties.

Publisher

Research Square Platform LLC

Reference58 articles.

1. Salt dome related soil salinity in southern Iran: Prediction and mapping with averaging machine learning models;Abedi F;Land Degradation & Development,2021

2. Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape;Ågren AM;Geoderma,2021

3. Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai sheep;Ali M;Pakistan Journal of Zoology,2015

4. Alloway BJ. (Ed.). (2012) Heavy metals in soils: trace metals and metalloids in soils and their bioavailability (Vol. 22). Springer Science & Business Media.

5. Assessing models for prediction of some soil chemical properties from portable X-ray fluorescence (pXRF) spectrometry data in Brazilian Coastal Plains;Andrade R;Geoderma,2020

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3