https://github.com/sandyherho/indraanndeepeval

This repository contains code and figures associated with the "Performance evaluation of a simple feed-forward deep neural network model applied to annual rainfall anomaly index (RAI) over Indramayu, Indonesia" manuscript
https://github.com/sandyherho/indraanndeepeval

climatology deep-neural-networks feedforward-neural-network hydrology indramayu maritime-continent rainfall

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This repository contains code and figures associated with the "Performance evaluation of a simple feed-forward deep neural network model applied to annual rainfall anomaly index (RAI) over Indramayu, Indonesia" manuscript

• Host: GitHub
• URL: https://github.com/sandyherho/indraanndeepeval
• Owner: sandyherho
• License: gpl-3.0
• Created: 2023-06-26T00:32:38.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-06-28T22:58:32.000Z (almost 3 years ago)
• Last Synced: 2024-01-29T09:51:17.912Z (over 2 years ago)
• Topics: climatology, deep-neural-networks, feedforward-neural-network, hydrology, indramayu, maritime-continent, rainfall
• Language: Python
• Homepage:
• Size: 7.44 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

          
# Performance evaluation of a simple feed-forward deep neural network model applied to annual rainfall anomaly index (RAI) over Indramayu, Indonesia

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This GitHub repository contains code used for **Performance evaluation of a simple feed-forward deep neural network model applied to annual rainfall anomaly index (RAI) over Indramayu, Indonesia** created by [Sandy H. S. Herho](https://scholar.google.com/citations?user=uYQgjxMAAAAJ&hl=id), [Dasapta E. Irawan](https://scholar.google.com/citations?user=Myvc78MAAAAJ&hl=en), [Faiz R. Fajary](https://scholar.google.com/citations?user=cTqtdTIAAAAJ&hl=en), [Rusmawan Suwarman](https://scholar.google.com/citations?user=NfMfR8LMVz8C&hl=en) and [Siti N. Kaban](https://scholar.google.com/citations?user=Jc0NPJsAAAAJ&hl=en) at the [Applied Geology Research Group](https://itb.ac.id/applied-geology-research-group), Bandung Institute of Technology (ITB), Indonesia.

### License

This code was released under the [GPL-3.0 License](https://github.com/sandyherho/IndraAnnDeepEval/blob/main/LICENSE.txt).

### Citation

If you find this code useful in your study, please  consider citing our paper:

`

@article{herhoEtAl23b,

         author={Herho, S. H. S. and Irawan, D. E. and Fajary, F. R. and Suwarman, R. and Kaban, S. N. },

         title={{P}erformance evaluation of a simple feed-forward deep neural network model applied to annual rainfall anomaly index (RAI) over {I}ndramayu, {I}ndonesia},

         journal={xxxxx},

         year={2023},

         volume={x},

         number={x},

         pages={x - x},

         doi={xx}

}

`

### Requirements

We run the code under the [Python 3](https://www.python.org/) computing environment by using the following libraries:

- [cartopy](https://scitools.org.uk/cartopy/docs/latest/)

- [matplotlib](https://matplotlib.org/)

- [numpy](https://numpy.org/)

- [keras](https://keras.io/)

- [keras-visualizer](https://github.com/mahyar-amiri/keras-visualizer)

- [pandas](https://pandas.pydata.org/)

- [tensorflow](https://www.tensorflow.org/)

- [scikit-learn](https://scikit-learn.org/)

- [xarray](https://docs.xarray.dev/en/)

Climate Hazards Infrared Precipitation with Stations (CHIRPS) precipitation dataset [(Funk et al, 2015)](https://www.nature.com/articles/sdata201566) was accessed via [Climate Hazards Center, UC Santa Barbara website](https://data.chc.ucsb.edu/products/CHIRPS-2.0/).

### Acknowledgements

Spyros Giannelos (Imperial College London) was acknowledged for providing valuable discussion. This study was supported by ITB Research, Community Service and Innovation Program (P3MI-ITB).