Fetulhak/Encoder-Decoder-Model-for-Rainfall-Runoff-Modeling

implementation of different LSTM models including Encoder-Decoder based approaches for Rainfall-Runoff Modeling for Awash River in Ethiopia. # Encoder-Decoder-Model-for-Rainfall-Runoff-Modeling (Case of Upper Awash Basin in Ethiopia) Implementation of different LSTM models including Encoder-Decoder based approaches for Rainfall-Runoff Modeling for Awash River in Ethiopia. Time series prediction is a widespread problem. Applications range from price and weather/flood/rain-fall runoff forecasting to biological signal prediction. In this project I try to demonstrate implemention of a Recurrent Neural Network (RNN) based vanilla LSTM/GRU and encoder-decoder models for time series prediction task using Keras. I have used a case study of the Upper Awash basin Meteorological and Hydrological data which is recorded at various record stations. I have used 28 years of daily recorded data. In this repo I provide the data used and the detailed experimental setups for the different vanilla LSTM/GRU and Encoder-Decoder based approaches. You can download the zip file of this ripo and run it on Google Colab. Details of the experiments are found on the provided notebook files. ## Contents of this repository - `Vanilla LSTM/GRU Code` -- A folder containing Jupyter notebooks for vanilla LSTM/GRU experiments - `Encoder-Decoder Code` -- A folder contatining Jupyter notebooks for encoder decoder based experiments - `1981_final` -- Folder containing all the datasets used in this project ## Contact Fetulhak Abdurahman: `afetulhak (at) yahoo.com`