Performance evaluation of a simple feed-forward deep neural network model applied to annual rainfall anomaly index (RAI) over Indramayu, Indonesia
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, Dasapta E. Irawan, Faiz R. Fajary, Rusmawan Suwarman and Siti N. Kaban at the Applied Geology Research Group, Bandung Institute of Technology (ITB), Indonesia.
This code was released under the GPL-3.0 License.
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} }
We run the code under the Python 3 computing environment by using the following libraries:
Climate Hazards Infrared Precipitation with Stations (CHIRPS) precipitation dataset (Funk et al, 2015) was accessed via Climate Hazards Center, UC Santa Barbara website.
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).