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Smart city article details
| Title | Efficiency Analysis Of Lstm-Based Earthquake Forecasting For Smart City Resilience: A Time Complexity Perspective |
|---|---|
| ID_Doc | 22209 |
| Authors | Duraisamy R.D.; Natarajan V.; Siva N.A.V.; Kalyan K.P.; Sai N.C. |
| Year | 2025 |
| Published | Deep Learning and Blockchain Technology for Smart and Sustainable Cities |
| DOI | http://dx.doi.org/10.1201/9781003476047-15 |
| Abstract | In the evolving landscape of urban development, smart cities represent a visionary approach to addressing the complexities of modern living. Amidst these advancements, the integration of earthquake forecasting stands out as a pivotal step toward bolstering urban resilience. By harnessing predictive technologies, smart cities can proactively prepare for seismic events, thereby safeguarding infrastructure and inhabitants. This research introduces a novel composite long short-term memory (LSTM) technique optimized for minimizing prediction time in earthquake forecasting for smart city resilience. This investigation uses LSTM and composite LSTM models to forecast earthquakes based on seismic data. The main objective is to comprehend the time complexity of LSTM and composite LSTM and suggest ways to improve earthquake prediction using LSTM models. This investigation uses a dataset on seismic activity to train and evaluate LSTM and composite LSTM models. The comparison helps to analyze the time complexity of LSTM and composite LSTM, providing insights into computational efficiency. The research is focused on discovering ways to enhance the accuracy and efficiency of earthquake prediction using LSTM architectures. Through a comprehensive analysis of time complexity, we demonstrate the efficacy of this approach in providing rapid and accurate predictions. By leveraging composite LSTM models, smart cities can enhance their preparedness and response mechanisms, ensuring timely mitigation of seismic risks and bolstering urban resilience. This investigation is actively contributing to the ongoing efforts to enhance seismic hazard assessment. This study underscores the significance of efficient prediction techniques in advancing the capabilities of smart city infrastructure for disaster management. © 2025 selection and editorial matter, V. Subramaniyaswamy, G Revathy, Logesh Ravi, N. Thillaiarasu, and Naresh Kshetri; individual chapters, the contributors. |
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