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    Iot techniques for disaster prediction and prevention
    (2023) Abdullah, Mustafa Hadi; Hamodat, Zaid
    Natural catastrophes such as landslides, floods, fires, and volcanic eruptions, as well as the damage produced by these events, are global issues that result in financial and human losses. This problem is exacerbated by changes in the planet's environmental conditions and is primarily evident in metropolitan areas. Because of pollution and a lack of planning, the deterioration of the ecosystem is more pronounced in these areas, damaging the ecology and influencing the local climate. As a result, this initiative makes three major contributions: (i) the use and evaluation of new IoT standards and emerging technologies in conjunction with WSN for the collection and distribution of data in natural environments, (ii) the use of the collected data for the prediction of natural disasters using Machine Learning (ML) techniques, with a case study on the characteristics of rivers and rainfall in Iraq and Turkey, and (iii) the proposal of an IoT-based and ML-based fault-tolerant architecture for the system.