Estimation of Arsenic Content in Soil Based on Laboratory and Field Reflectance Spectroscopy

Estimation of Arsenic Content in Soil Based on Laboratory and Field Reflectance Spectroscopy

In this study, in order to solve the difficulty of the inversion of soil arsenic (As) content using laboratory and field reflectance spectroscopy, we examined the transferability of the prediction method. Sixty-three soil samples from the Daye city area of the Jianghan Plain region of China were tak...

Full description

Bibliographic Details
Main Authors:	Lifei Wei, Ziran Yuan, Ming Yu, Can Huang, Liqin Cao
Format:	Article
Language:	English
Published:	MDPI AG 2019-09-01
Series:	Sensors
Subjects:	soil arsenic content hyperspectral remote sensing characteristic bands iteratively retaining informative variables random forest regression eXtreme gradient boosting regression
Online Access:	https://www.mdpi.com/1424-8220/19/18/3904

Similar Items

Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil
by: Lifei Wei, et al.
Published: (2020-04-01)

Acceleration of Hyperspectral Skin Cancer Image Classification through Parallel Machine-Learning Methods
by: Bernardo Petracchi, et al.
Published: (2024-02-01)

Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning
by: Runhong Zhang, et al.
Published: (2021-01-01)

Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis
by: Zhen Li, et al.
Published: (2023-09-01)

Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function
by: Xia Wei Zhu, et al.
Published: (2024-02-01)

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing
by: Lifei Wei, et al.
Published: (2020-07-01)

Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery
by: Lifei Wei, et al.
Published: (2020-01-01)

An Intelligent Perception Model and Parameters Adjust Method for Quality of Experience
by: Yanqin Wu, et al.
Published: (2022-05-01)

Research on nonstroke dementia screening and cognitive function prediction model for older people based on brain atrophy characteristics
by: Wei Zhang, et al.
Published: (2022-11-01)

An Improved Gradient Boosting Regression Tree Estimation Model for Soil Heavy Metal (Arsenic) Pollution Monitoring Using Hyperspectral Remote Sensing
by: Lifei Wei, et al.
Published: (2019-05-01)

Machine learning assisted design of high-entropy alloys with ultra-high microhardness and unexpected low density
by: Shunli Zhao, et al.
Published: (2024-02-01)

XGB4mcPred: Identification of DNA N4-Methylcytosine Sites in Multiple Species Based on an eXtreme Gradient Boosting Algorithm and DNA Sequence Information
by: Xiao Wang, et al.
Published: (2021-09-01)

An application of a supervised machine learning model for predicting life expectancy
by: Brian Aholi Lipesa, et al.
Published: (2023-06-01)

A novel approach for detecting advanced persistent threats
by: Jaafer Al-Saraireh, et al.
Published: (2022-12-01)

Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific
by: Qingwen Jin, et al.
Published: (2019-06-01)

A Novel Remaining Useful Life Prediction Approach Combined eXtreme Gradient Boosting and Multi-Quantile Recurrent Neural Network
by: Yantao Yin, et al.
Published: (2024-01-01)

IdentPMP: identification of moonlighting proteins in plants using sequence-based learning models
by: Xinyi Liu, et al.
Published: (2021-08-01)

Predicting Flow Stress Behavior of an AA7075 Alloy Using Machine Learning Methods
by: Jens Decke, et al.
Published: (2022-09-01)

Deep and machine learning for daily streamflow estimation: a focus on LSTM, RFR and XGBoost
by: Özlem Terzi, et al.
Published: (2023-10-01)

Developing and Preliminary Testing of a Machine Learning-Based Platform for Sales Forecasting Using a Gradient Boosting Approach
by: Antonio Panarese, et al.
Published: (2022-10-01)

Power Grid Material Demand Forecasting Based on Pearson Feature Selection and Multi-Model Fusion
by: Zhou Dai, et al.
Published: (2022-04-01)

Prediction of hyperuricemia in people taking low-dose aspirin using a machine learning algorithm: a cross-sectional study of the National Health and Nutrition Examination Survey
by: Bin Zhu, et al.
Published: (2024-01-01)

A Powerful Prediction Framework of Fracture Parameters for Hydraulic Fracturing Incorporating eXtreme Gradient Boosting and Bayesian Optimization
by: Zhe Liu, et al.
Published: (2023-12-01)

Enhancing predictions of antimicrobial resistance of pathogens by expanding the potential resistance gene repertoire using a pan-genome-based feature selection approach
by: Ming-Ren Yang, et al.
Published: (2022-04-01)

Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network
by: Minghui Liu, et al.
Published: (2020-10-01)

An Analysis of the Factors Affecting Forest Mortality and Research on Forecasting Models in Southern China: A Case Study in Zhejiang Province
by: Zhentian Ding, et al.
Published: (2023-11-01)

Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia
by: Yujing Cheng, et al.
Published: (2024-01-01)

Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?
by: Chen Bai, et al.
Published: (2022-04-01)

HGSOXGB: Hunger-Games-Search-Optimization-Based Framework to Predict the Need for ICU Admission for COVID-19 Patients Using eXtreme Gradient Boosting
by: Farhana Tazmim Pinki, et al.
Published: (2023-09-01)

Optimizing Kidney Stone Prediction through Urinary Analysis with Improved Binary Particle Swarm Optimization and eXtreme Gradient Boosting
by: Abdullah Alqahtani, et al.
Published: (2023-04-01)

Using Explainable Machine Learning to Explore the Impact of Synoptic Reporting on Prostate Cancer
by: Femke M. Janssen, et al.
Published: (2022-01-01)

An online soft sensor method for biochemical reaction process based on JS-ISSA-XGBoost
by: Ligang Zhang, et al.
Published: (2023-11-01)

Comparison of Machine Learning Methods for Predicting Soil Total Nitrogen Content Using Landsat-8, Sentinel-1, and Sentinel-2 Images
by: Qingwen Zhang, et al.
Published: (2023-06-01)

Vibration prediction and analysis of strip rolling mill based on XGBoost and Bayesian optimization
by: Yang Zhang, et al.
Published: (2022-06-01)

A Novel Method of Statistical Line Loss Estimation for Distribution Feeders Based on Feeder Cluster and Modified XGBoost
by: Shouxiang Wang, et al.
Published: (2017-12-01)

Use of Machine Learning with Fused Spectral Data for Prediction of Product Sensory Characteristics: The Case of Grape to Wine
by: Claire E. J. Armstrong, et al.
Published: (2023-02-01)

Estimation of wheat tiller density using remote sensing data and machine learning methods
by: Jinkang Hu, et al.
Published: (2022-12-01)

Extreme programming explained : embrace change /
by: 247803 Beck, Kent, et al.
Published: (2005)

Agile software construction /
by: Hunt, John, 1964-
Published: (2006)

Support For Distributed Programming In Extreme Style
by: Jacek Dajda, et al.
Published: (2005-01-01)