Predicting Hurricane Trajectories Using a Recurrent Neural Network
Authors: Sheila Alemany, Jonathan Beltran, Adrian Perez, Sam Ganzfried468-475
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results show that this proposed technique is competitive to methods currently employed by the NHC and can predict up to approximately 120 hours of hurricane path. |
| Researcher Affiliation | Collaboration | 1School of Computing and Information Sciences, Florida International University, Miami, FL 2Ganzfried Research, Miami, FL {salem010, jbelt021, apere946}@fiu.edu, sam.ganzfried@gmail.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for the source code of the methodology described in this paper. |
| Open Datasets | Yes | The raw Atlantic hurricane/tropical storm data used in the study were extracted from the NOAA database.2 The data contains all hurricanes and tropical storms from 1920 to 2012. Footnote 2 points to: http://weather.unisys.com/hurricane/atlantic/ |
| Dataset Splits | Yes | The data provided by Unisys Weather data was divided where 85% of the total hurricanes were used for training, and 15% were used for testing the accuracy of our model. In other words, the training set and testing contained 27,477 and 4,850 individual data tuples, respectively. As a result, validation of the training set was completed on 10% of the 85% training set, or 2,747 data tuples. |
| Hardware Specification | Yes | The model was trained on an NVIDIA Ge Force GTX 1060 with 6GB of RAM which allowed the model to complete training in 200 seconds. |
| Software Dependencies | No | The paper mentions using 'Keras' and 'TensorFlow' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The number of grid blocks in our model could be tuned depending on the amount of hurricane data available. ... we utilized a total of 7,256 grid blocks. The grid blocks were of size 1x1 degrees latitude by longitude. A regularization hyperparameter, known as the dropout value, randomly ignores a percentage of the input to prevent the model from co-adapting to the training set, or overfitting, of hurricane trajectories (Srivastava et al. 2014). This value was set to 0.1 and was tuned and selected using cross-validation... Therefore, we employed three hidden layers each with a long short-term memory cell... Keras provides a default learning rate hyperparameter of the value 0.001. |