A Novel Approach on IoT-Based Natural Disaster Prediction and Early Warning Systems (EWS)

A Novel Approach on IoT-Based Natural Disaster Prediction and Early Warning Systems (EWS)

Karthikeyan Pathinettampadian, Nagarani N., Shivani Suvatheka S., Al Mohamed Bilal A.
Copyright: © 2024 |Pages: 19
DOI: 10.4018/979-8-3693-3362-4.ch011
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Abstract

Natural disasters cause significant damage and human losses, emphasizing the need for predictive systems and efficient warning mechanisms. Exploring the potential of an internet of things (IoT)-driven early warning system (EWS) is crucial for detecting and notifying individuals about diverse disasters like earthquakes, floods, tsunamis, and landslides. In a disaster, the device transmits data to the microcontroller, where it undergoes validation and processing using ML algorithms to predict disaster possibilities. Data from edge nodes reaches the cloud via a gateway, with fog nodes filtering and accessing it. After verification, persistent alarming weather conditions trigger a warning alert, conveyed promptly to individuals in disaster-prone regions through diverse communication channels. An IoT-based open-source application with a user-friendly interface continuously monitors parameters like water intensity and rainfall during floods, and ground vibrations for earthquakes. Alerts are generated when parameters exceed set thresholds, providing a cost-effective disaster detection solution with timely alerts to vulnerable communities.
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Introduction

An Early Warning System (EWS) constitutes an integrated framework encompassing hazard monitoring, forecasting, prediction, disaster risk assessment, communication, and preparedness activities. This comprehensive system empowers individuals, communities, governments, businesses, and other stakeholders to take timely actions that mitigate disaster risks before hazardous events occur. Key components of an EWS include (a) risk knowledge and assessment, (b) monitoring parameters for enhanced predictions, (c) timely dissemination of warnings, and (d) preparedness for disaster response. The utilization of advanced information and communication technologies presents a viable solution for expanding multi-hazard warning systems, especially in countries lacking national implementations. These technologies, including the Internet of Things, Cloud Computing, and Artificial Intelligence, play pivotal roles in monitoring, forecasting, and alarm generation within Early Warning systems. They offer cost-effective deployment and facilitate smart and efficient alert and information broadcasting. In particular, these technologies empower the examination and interpretation of data. from the environment, contributing to the effectiveness of Early Warning efforts.

Internet of Things

The Internet of Things comprises infrastructures that interconnect various objects, facilitating the collection, transfer, and access to the generated data. Its primary goal is to connect objects, sensors, and actuators to perform diverse tasks, including customized environmental monitoring(Shah S et al.2019). A fundamental and generic IoT architecture typically consists of three levels: (a) the local environment in which the sensors and actuators are placed where they sense, monitor, and collect the required data (b) a transport layer enabling communication between end-nodes in the first layer and higher layers of the infrastructures; and (c) a storage layer where the collected data are stored for the later access and to maintain records of the event that can be viewed from anywhere in the world via internet. This is usually implemented in the cloud that interfaces systems for user access and data visualization. In adherence to the United Nations Sustainable Development Goal 12, sustainable cities place a significant emphasis on disaster risk reduction (Mei G et al. 2019). The Gesi Smarter 2030 report highlights the crucial role played by IoT in the realm of disaster management and Early Warning systems. It provides the means for extensive environmental monitoring through diverse data sources, low-latency communications, and real-time data processing. These capabilities enable the generation of accurate and timely warnings on the occurrence of a disaster or during forecasting.

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