Name
Abstract | ||
|---|---|---|
•An architecture for emergency event prediction is proposed.•Binary classification and regression models are developed.•LSTM recurrent neural networks are adopted.•The proposed models overwhelmed time series forecasting and machine learning.•The assumption on spatial dependency was evaluated. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.eswa.2017.12.037 | Expert Systems with Applications |
Keywords | Field | DocType |
Emergency events,Emergency prediction system,Recurrent neural network,Long short-term memory | Data mining,Time series,Architecture,Computer science,Recurrent neural network,Emergency response systems,Autoregressive integrated moving average,Risk management,Artificial intelligence,Moving average,Machine learning | Journal |
Volume | ISSN | Citations |
97 | 0957-4174 | 8 |
PageRank | References | Authors |
0.49 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bitzel Cortez | 1 | 8 | 0.49 |
| Berny Carrera | 2 | 9 | 1.20 |
| Young-jin Kim | 3 | 21 | 1.51 |
| Jae-Yoon Jung | 4 | 297 | 31.94 |