| Title |
Artificial intelligence based real-time earthquake prediction |
| ID_Doc |
43948 |
| Authors |
Bhatia, M; Ahanger, TA; Manocha, A |
| Title |
Artificial intelligence based real-time earthquake prediction |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.engappai.2023.105856 |
| Abstract |
Earthquake prediction is considered a vital endeavour for human safety. Effective earthquake prediction can drastically reduce human damage, which is of utmost importance to the community and individuals. In the current research world, there is a boom in scientific interest in the prediction of seismic events. With the technological revolution in data acquisition, communication networks, edge-cloud computing, the Internet of Things (IoT), and big data analysis, it is feasible to develop an intelligent earthquake prediction model for early warnings at vulnerable locations. Conspicuously, a collaborative IoT-Edge-centered smart earthquake monitoring and prediction framework using cloud and edge computing are proposed. IoT technology is utilized to acquire real-time sensor data, which is forwarded to the edge layer for feature classification utilizing a novel bayesian belief model technique. Furthermore, Adaptive Neuro-Fuzzy Inference System (ANFIS) mechanism is employed to forecast the magnitude of earthquakes in the cloud layer. Based on the experimental simulation, enhanced effectiveness is acquired for the presented framework in terms of classification performance (Precision (92.52%), Sensitivity (91.72%), and Specificity (91.01%)). Additionally, results show that the utilization of edge computing significantly reduces computational delay (23.06s). Moreover, enhanced accuracy and throughput are acquired for the presented model in terms of reliability (95.26%) and stability (92.16%). |
| Author Keywords |
Internet of Things; Edge computing; ANFIS prediction; Smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000960913400001 |
| WoS Category |
Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Multidisciplinary; Engineering, Electrical & Electronic |
| Research Area |
Automation & Control Systems; Computer Science; Engineering |
| PDF |
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