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

## TL;DR

This paper reviews how digital technologies and machine learning improve monitoring of environmental hazards like floods and air pollution.

## Contribution

It provides a synthesis of recent advancements and challenges in using digital tools for hazard monitoring.

## Key findings

- Digital technologies like IoT and machine learning are increasingly used for real-time hazard monitoring.
- Multi-sensor data fusion and deep learning models are effective for early warning systems.
- Challenges include scalability and data integration for robust hazard monitoring systems.

## 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.