A systematic evaluation of advanced machine learning models for nickel contamination management in soil using spectral data DOI Creative Commons

Kechao Li,

Tao Hu, Min Zhou

et al.

Journal of Hazardous Materials Advances, Journal Year: 2024, Volume and Issue: unknown, P. 100576 - 100576

Published: Dec. 1, 2024

Language: Английский

A WebGIS-Based System for Supporting Saline–Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China DOI Creative Commons
Yingqiang Song,

Yinxue Pan,

Meiyan Xiang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(11), P. 1948 - 1948

Published: May 28, 2024

Monitoring and evaluation of soil ecological environments are very important to ensure saline–alkali health the safety agricultural products. It is foremost importance to, within a regional risk-reduction strategy, develop useful online system for assessment prediction prevent people from suffering threat sudden disasters. However, traditional manual or empirical parameter adjustment causes mismatch hyperparameters model, which cannot meet urgent need high-performance properties using multi-dimensional data in WebGIS system. To this end, study aims monitoring real-time ecology Yellow River Delta, China. The applied advanced web-based GIS, including front-end back-end technology stack, cross-platform deployment machine learning models, database embedded multi-source environmental variables. adopts five-layer architecture integrates functions such as statistical analysis, assessment, salt prediction, management. visually displays results air quality, vegetation index, area. provides users with risk analyze heavy metal pollution soil. Specially, introduces tree-structured Parzan estimator (TPE)-optimized model achieve accurate salinity. TPE–RF had highest accuracy (R2 = 94.48%) testing set comparison TPE–GBDT exhibited strong nonlinear relationship between variables developed can provide information government agencies farmers, great significance production protection.

Language: Английский

Citations

1
Systematic Comparison of Machine Learning Models for Soil Nickel Contamination Using Spectral Data DOI
Chongchong Qi,

Kechao Li,

Tao Hu

et al.

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

Citations

0
Assessing the impact of multi-source environmental variables on soil organic carbon in different land use types of China using an interpretable high-precision machine learning method DOI Creative Commons

Feng Wang,

Robert Y. Liang,

Shuyue Li

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 169, P. 112865 - 112865

Published: Dec. 1, 2024

Language: Английский

Citations

0
Prediction of copper contamination in soil across EU using spectroscopy and machine learning: handling class imbalance problem DOI Creative Commons
Chongchong Qi,

Nana Zhou,

Tao Hu

et al.

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: unknown, P. 100728 - 100728

Published: Dec. 1, 2024

Language: Английский

Citations

0
A systematic evaluation of advanced machine learning models for nickel contamination management in soil using spectral data DOI Creative Commons

Kechao Li,

Tao Hu, Min Zhou

et al.

Journal of Hazardous Materials Advances, Journal Year: 2024, Volume and Issue: unknown, P. 100576 - 100576

Published: Dec. 1, 2024

Language: Английский

Citations

0