Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 12, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Multi-indicator water-quality prediction in mining areas using a feature-tokenizer transformer with spatiotemporal

Zihan Liu1, Xiang Sui1, Xianzhou Lyu1

  • 1College of Earth Science and Technology, Shandong University of Science and Technology, Qingdao, 266590, PR China; State Key Laboratory of Disaster Prevention and Ecology Protection in Open-pit Coal Mines, Shandong University of Science and Technology, Qingdao, 266590, PR China.

Environmental Research
|July 7, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

HICnet: A two-stage framework based on region partitioning for predicting HIC values from car point clouds.

Accident; analysis and prevention·2026
Same author

Nonlinear biogeographic responses and threshold patterns in vegetative versus reproductive traits across China's ecosystems.

Journal of environmental management·2026
Same author

Anthropogenic sustained increase in near bottom suspended particulate matter concentration of the deep marginal sea.

Environmental research·2026
Same author

Immune-related deubiquitylation spectrum of microsatellite stability colorectal cancer reveals USP7 as a potential immunotherapeutic target.

Molecular cancer·2025
Same author

T Cell Exhaustion and Dendritic Cell-Mediated Tertiary Lymphoid Structures (TLSs) Modulation Affect Response to Neoadjuvant Chemoradiotherapy in Microsatellite Stable Rectal Cancer.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Coupled salt and nitrogen dynamics in coastal reservoir-adjacent aquifer systems under extreme rainfall event.

Journal of contaminant hydrology·2025

A new Feature-Tokenizer Transformer (FT-Transformer) model accurately predicts mining area water quality across multiple indicators, outperforming existing methods. This advancement aids in assessing and managing water quality in complex mining environments.

Area of Science:

  • Environmental Science
  • Data Science
  • Water Resource Management

Background:

  • Mining impacts water quality, creating challenges for prediction in large, heterogeneous areas.
  • Existing models struggle with stable, multi-indicator water quality prediction across diverse mining regions.

Purpose of the Study:

  • To develop a robust multi-indicator framework for nationwide mining-area water quality prediction.
  • To integrate diverse data sources for improved predictive accuracy and model understanding.

Main Methods:

  • Developed a Feature-Tokenizer Transformer (FT-Transformer) multi-task framework.
  • Integrated 55,744 monitoring records with spatial, mining, temporal, and temperature data.
  • Jointly predicted pH, dissolved oxygen (DO), ammonium nitrogen (NH4-N), and permanganate index (CODMn) using cross-validation against benchmark models.
Keywords:
Environmental driversFT-TransformerMining disturbanceMining-area water qualityMulti-indicator predictionNested cross-validation

Related Experiment Videos

Last Updated: Jul 12, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Main Results:

  • The FT-Transformer achieved a mean R² of 0.790, outperforming Elastic Net, Random Forest, and XGBoost for most indicators.
  • Achieved high R² values for NH4-N (0.826) and CODMn (0.820), with strong performance for pH (0.756) and DO (0.756).
  • Multi-task learning improved joint predictions, and analyses revealed indicator-specific and context-dependent model behaviors.

Conclusions:

  • The FT-Transformer provides a powerful tool for multi-indicator water quality prediction in heterogeneous mining areas.
  • The framework supports model applicability screening and monitoring priority assessment.
  • Understanding indicator-specific responses enhances environmental management strategies in mining regions.