Work

An Adaptive IoT-Based ForecastingFramework for Structural and Environmental Risk Detection in Tailings Dams

Year: 2026

Type: article

Abstract: Tailings dams represent one of the most environmentally sensitive infrastructures in the mining industry. To address the need for continuous and accurate monitoring, this paper presents an adaptive forecasting framework that combines Internet of Things (IoT) technologies with machine learning (ML) models to detect early signs of structural and ecological risks. The proposed system architecture is modular and scalable and enables the automated training, selection, and deployment of predictive models for multivariate sensor data. Each sensor data flow is independently analyzed by using a configurable set of algorithms (including linear, convolutional, recurrent, and residual models). The framework is deployed via containers with a CI/CD pipeline and includes real-time visualization through Grafana dashboards. A use case involving tiltmeters and piezometers in an operational tailing dam shows the system’s high predictive accuracy, with mean relative errors below 4% across all variables (in fact, many of them have a mean relative error below 1%). These results highlight the potential of the proposed solution to improve structural and environmental safety in mining operations.

Source: Electronics

DOI: 10.3390/electronics15030658

Authors: Raul Rabadán-Arroyo, Ester Simó, Francesc Aguiló-Gost, Francisco Hernández-Ramírez, Xavier Masip-Bruin

Institutions: Universitat Politècnica de Catalunya, Worldsensing (Spain)

Language: English

Topic: Tailings Management and Properties

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Responsible consumption and production

Open Access status: gold

Estimated APC paid: $2,896