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Digital Technologies and Machine Learning in Environmental Hazard Monitoring: A Synthesis of Evidence for Floods, Air Pollution, Earthquakes, and Fires

Year: 2026

Type: article

Abstract: This review synthesizes the state of the art on the integration of digital technologies, particularly machine learning, the Internet of Things (IoT), and advanced image processing techniques, for enhanced hazard monitoring. Focusing on air pollution, earthquakes, floods, and fires, we analyze articles selected from Scopus published between 2015 and 2024. This study classifies the selected articles based on hazard type, digital technology application, geographical location, and research methodology. We assess the effectiveness of various approaches in improving the accuracy and efficiency of hazard detection, monitoring, and prediction. The review highlights the growing trend of leveraging multi-sensor data fusion, deep learning models, and IoT-enabled systems for real-time monitoring and early warning. Furthermore, we identify key challenges and future directions in the development of robust and scalable hazard monitoring systems, emphasizing the importance of data-driven solutions for sustainable environmental management and disaster resilience.

Source: Sensors

DOI: 10.3390/s26030893

Authors: Jacek Lukasz Wilk-Jakubowski, Artur Kuchcinski, Grzegorz Kazimierz Wilk-Jakubowski, Andrzej Palej, Lukasz Pawlik

Institution: Kielce University of Technology

Language: English

Topic: Energy and Environmental Systems

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities, Climate action

Open Access status: gold

Estimated APC paid: $3,137