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README.md

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-# CNN_KarstSpringModeling
+# CNN_KarstSpringModeling
+
+
+doi of this repo:  
+*will be added soon*
+
+doi of according publication:
+*will be added soon*
+
+This repository enables you to reproduce the results and apply the groundwater level forecasting mehtodology of:     
+*Wunsch, A., Liesch, T., Cinkus, G., Ravbar, N., Chen, Z., Mazzilli, N., Jourde, H., Golscheider, N., Karst spring discharge modeling based on deep learning using
+spatially distributed input data (submitted to HESS, 2021)*
+
+Contact: [andreas.wunsch@kit.edu](andreas.wunsch@kit.edu)
+
+ORCIDs of first author:   
+A. Wunsch:  [0000-0002-0585-9549](https://orcid.org/0000-0002-0585-9549)   
+
+
+For a detailed description please refer to the publication.
+Please adapt all absolute loading/saving and software paths within the scripts to make them running, you Python software for a successful application. Our models are implemented in Python 3.8 (van Rossum, 1995) and we use the following libraries and frameworks: [Numpy](https://numpy.org/) (van der Walt et al., 2011), [Pandas](https://pandas.pydata.org/) (McKinney, 2010; Reback et al., 2020), [Scikit-Learn](https://scikit-learn.org/stable/) (Pedregosa et al., 2011), [Unumpy](https://pythonhosted.org/uncertainties/numpy_guide.html) (Lebigot, 2010), Matplotlib (Hunter, 2007), [BayesOpt](https://github.com/fmfn/BayesianOptimization) (Nogueira, 2014), [TensorFlow](https://www.tensorflow.org/) and its [Keras](https://keras.io/) [API](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras) (Abadi et al., 2015; Chollet, 2015).
+
+### Content Overview:
+* /1
+
+* 
+
+*