{"id":37598160,"url":"https://github.com/sandyherho/indraanndeepeval","last_synced_at":"2026-01-16T10:00:04.486Z","repository":{"id":176429920,"uuid":"658513007","full_name":"sandyherho/IndraAnnDeepEval","owner":"sandyherho","description":"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 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Indonesia\n\n\n[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)\n[![GitHub watchers](https://img.shields.io/github/watchers/Naereen/StrapDown.js.svg?style=social\u0026label=Watch\u0026maxAge=2592000)](https://github.com/sandyherho/IndraAnnDeepEval/watchers)\n[![No Maintenance Intended](http://unmaintained.tech/badge.svg)](http://unmaintained.tech/)\n[![DOI](https://zenodo.org/badge/658513007.svg)](https://zenodo.org/badge/latestdoi/658513007)\n\n[![python](https://img.shields.io/badge/python-★★★-lightgrey?labelColor=3776AB\u0026logo=Python\u0026style=for-the-badge\u0026logoColor=white)](https://www.python.org/)\n![Overleaf](https://img.shields.io/badge/-Overleaf-47A141?logo=Overleaf\u0026style=for-the-badge\u0026logoColor=white)\n\n\nThis 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\u0026hl=id), [Dasapta E. Irawan](https://scholar.google.com/citations?user=Myvc78MAAAAJ\u0026hl=en), [Faiz R. Fajary](https://scholar.google.com/citations?user=cTqtdTIAAAAJ\u0026hl=en), [Rusmawan Suwarman](https://scholar.google.com/citations?user=NfMfR8LMVz8C\u0026hl=en) and [Siti N. Kaban](https://scholar.google.com/citations?user=Jc0NPJsAAAAJ\u0026hl=en) at the [Applied Geology Research Group](https://itb.ac.id/applied-geology-research-group), Bandung Institute of Technology (ITB), Indonesia.\n\n### License\nThis code was released under the [GPL-3.0 License](https://github.com/sandyherho/IndraAnnDeepEval/blob/main/LICENSE.txt).\n\n### Citation\nIf you find this code useful in your study, please  consider citing our paper:\n\n\n`\n@article{herhoEtAl23b,\n         author={Herho, S. H. S. and Irawan, D. E. and Fajary, F. R. and Suwarman, R. and Kaban, S. N. },\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},\n         journal={xxxxx},\n         year={2023},\n         volume={x},\n         number={x},\n         pages={x - x},\n         doi={xx}\n}\n`\n\n### Requirements\n\nWe run the code under the [Python 3](https://www.python.org/) computing environment by using the following libraries:\n\n- [cartopy](https://scitools.org.uk/cartopy/docs/latest/)\n- [matplotlib](https://matplotlib.org/)\n- [numpy](https://numpy.org/)\n- [keras](https://keras.io/)\n- [keras-visualizer](https://github.com/mahyar-amiri/keras-visualizer)\n- [pandas](https://pandas.pydata.org/)\n- [tensorflow](https://www.tensorflow.org/)\n- [scikit-learn](https://scikit-learn.org/)\n- [xarray](https://docs.xarray.dev/en/)\n\nClimate 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/).\n\n### Acknowledgements\n\nSpyros Giannelos (Imperial College London) was acknowledged for providing valuable discussion. This study was supported by ITB Research, Community Service and Innovation Program (P3MI-ITB).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandyherho%2Findraanndeepeval","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsandyherho%2Findraanndeepeval","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandyherho%2Findraanndeepeval/lists"}