Date: 23 April, 2024

Dataset Title: Mono Lake Water Levels Forecasting using Machine Learning

Dataset Creators: Shah, Bhavarth

Dataset Contact: Bhavarth Shah bhavarth@umich.edu

Key Points:
- As outlined in the main thesis, we have developed three approaches to forecasting water levels at Mono Lake, California using machine learning. Each approach utilizes a different dataset.
- These approaches include (Figure 1 in main thesis):
	I.	Using historical water levels dataset
	II.	Using historical precipitation dataset
	III.	Using historical Bayesian statistical dataset

Data Description:
- The three approaches used three distinct datasets named as follows: Historicalwater_levels.csv, Historical_Precipitation.csv, and Bayesian Statistical dataset.csv. These files are accessible using Microsoft Office or similar software. 

- The supplementary material includes Jupyter Notebooks (.ipynb) and Microsoft CSV and Excel (.csv and .xlsx) files. These notebooks can be accessed through Python, Project Jupyter, or Google Colab, and dependencies include libraries such as Pandas, NumPy, Matplotlib, Scikit-learn, Keras, and TensorFlow. The CSV and Excel files can be opened with Microsoft Excel, Google Sheets, or similar applications.

- The supplementary material also includes Excel files for stage-curve calculations and diversions, named Water_levels_Stage_Curve_Calculations1970-2018.xlsx and Diversions_calculation.xlsx, respectively.

Files contained here:
The files are divided based on the above three approaches. The corresponding CSV and ipynb files correspond to the respective dataset (.csv) used and the machine learning model (.ipynb) developed using that dataset. 

# I.	Using historical water levels dataset
- Historicalwater_levels.csv
- Water_Levels_Forecast_Support Vector Machine Model.ipynb
- Water_Levels_Forecast_Random Forest Model.ipynb
- Water_Levels_Forecast_LSTM Model.ipynb


# II.	Using historical water levels dataset
- Historical_Precipitation.csv
- Simple_Bayesian_Based_Precipitation_Forecasting.ipynb
- Advanced_Bayesian_Based_Precipitation_Forecasting.ipynb


# III.	Using historical Bayesian statistical dataset
- Bayesian Statistical dataset.csv
- Random Forest_Model.ipynb
- Random Forest_Model_Tuning.ipynb
- LSTM_Model_Base.ipynb
- Multivariate LSTM_Model_1.ipynb
- Multivariate LSTM_Model_2.ipynb
- Multivariate LSTM_Model_3.ipynb
- Multivariate LSTM_Model_4.ipynb
- Multivariate LSTM_Model_5.ipynb
- Multivariate LSTM_Model_6.ipynb
- Multivariate LSTM_Model_7.ipynb
- Multivariate LSTM_Model_8.ipynb
- Multivariate LSTM_Model_9.ipynb
- Multivariate LSTM_Model_10.ipynb
- Multivariate LSTM_Model_11.ipynb
- Multivariate LSTM_Model_12.ipynb


# Calculations and conversions:
- Diversions calculation.xlsx
- Water levels_Stage_Curve_Calculations1970-2018.xlsx

# Original Thesis:
- Shah_Bhavarth_Thesis.pdf

Please note that the CSV files referenced in the machine learning model (.ipynb) files may have names that differ from those listed above. However, the data within these CSV files remains the same.

Use and Access:
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
This license lets others modify and build upon your work non-commercially. Their new works must also acknowledge you and be non-commercial, but they don’t have to license their derivative works on the same terms.

To Cite Data:
Shah, Bhavarth. 2024. "Mono Lake Water Levels Forecasting Using Machine Learning." Master’s thesis, University of Michigan, School for Environment and Sustainability. ORCID iD: 0000-0002-2391-8610.