## 4 IoT-Cloud-Based EWS
The model simplifies the extraction of essential waveform properties, resulting in a higher degree of credibility in earthquake parameter assessment. The suggested model predicts magnitude, latitude, and longitude with an accuracy of within 28 \(\times\) 10\({}^{-6}\), 3.3 \(\times\) 10\({}^{-6}\), and 100 \(\times\) 10\({}^{-6}\) degrees, respectively. That model immediately communicates event features to a sink IoT node. It provides guidance to the relevant administration on how to proceed. It is noted that AE has proved beneficial in feature extraction regardless of the application [178]. The framework for earthquake prediction proposed in [179] is a novel approach based on federated learning (FL). This FL framework outperformed the previously developed ML model for earthquake estimation through an IoT gateway in terms of reliability and accuracy. The model achieved an accuracy of 88% by analyzing multidimensional data over a 100 km radial area, excluding the Western Himalayas, and studying the data. In [180], an EEW based on an IoT and an ML model was suggested to predict tsunamis using tsunami data dating back to 2100 BC and was trained on earthquake parameters in the dataset. It achieved an accuracy of 95% in predicting earthquake location, depth, and magnitude. In [181], a DL approach that can identify P-waves despite background noise was developed using MEMS for observing events. The model can detect the probability of occurring preceding significant shocks and accurately predict P-waves between 1.5 and 2.5 s before their arrival. In [182], the authors used detector nodes to detect earthquakes locally by probing the environment and assessing data from probes in the surrounding area. The method stores all data locally, making it resistant to node failures and partial network outages, thus increasing privacy. The test network consisted of twenty node codes joined with ten neighbor nodes chosen at random. The total number of detectors was sampled every ten seconds. In [183], a Multilayer Perceptron-classifier was developed to provide a severity-based warning by predicting the possibility of an onsite intensity exceeding a pre-trained PGA threshold associated with damaging intensities on the MMI scale--seismic properties observed by the strong-motion signal starting from the P-wave in the developed model. The authors of [184] proposed an independent model of earthquake detection via low-cost acceleration nodes. The model utilized four different sensor types for establishing an EEWS with different types of data, e.g., noise from buildings, vibrations, and earthquake records. To test the sensors, two actual earthquakes were replicated on a shake table. The study found that low-cost acceleration sensors can detect earthquakes by monitoring differences in acceleration induced by a range between 0.02 g to 0.8 g, which can be detected by the sensors. Therefore, the authors used scaled data within that range. An ML methodology with earthquake characteristics was utilized, as opposed to the more conventional seismic methodologies that are typically used [185; 186; 187]. The authors broke the detection problem down into two distinct groups, namely, static settings and dynamic settings. They provide the most effective ML approach and input data for the static environment based on an experimental evaluation of numerous features for circumventing the issue of discriminating earthquake and noise components to reduce the number of false alarms. This model was validated with the help of 385 earthquakes ranging in magnitude from 4.0 to 8.0. The authors of the paper [188] introduced the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system as a cutting-edge ML-based technique that includes data from GPS stations and earthquake sensors in order to recognize large and medium earthquakes. The model relies on an innovative stacking ensemble technique that has been validated by geoscientists using a real-world dataset. This approach was used to build DMSEEW.