This repo uses Python and Keras frameworks to build, train and test a neural network based on the paper.

Nimal C. Jayasundara, et al. Artificial Neural Network for Sacramento–San JoaquinDelta Flow–Salinity Relationship for CalSim 3.0

To try it on the cloud (mybinder.org) simply use .

To setup a local enviornment, first download miniconda3 and then create an environment based on environment.yml file

conda env create -f environment.yml

Now you follow the instructions below

The repo contains jupyter notebooks and python code in two files. The starting point for input preprocessing are DSS files from one or mor runs of CALSIM based DSM2 studies.

• Preprocessing. The read_calsim_and_collate_inputs.ipynb takes the .dss files and creates input and output csv files
• Training and Testing. The ann_smscg_ff_calsim3_style.ipynb uses the csv files and builds, trains, saves and tests the neural network
• Optimization Sample. The sample_water_cost_with_ann.ipynb uses the trained ann to be used on a sample water cost optimization problem