An open source library for environmental isotopic modelling using machine learning techniques

Stable isotopic composition modelling of water is an important part of resource management studies. We present a tool that estimates water stable isotope compositions using discontinuous inputs in time and space through machine learning algorithms. This tool has a multi-stage coupled algorithm that firstly calculates the parameters defined by the user that potentially affect the isotopic composition such as meteorological parameters, then, integrates the results of different parameters and generates the isotopic composition models for each time window. Isocompy time windows can be defined flexibly based on the amount of spatial-temporal properties of the available data. A variety of decision-making algorithms are implemented in this tool as an optional support to the user in different stages: from dataset preprocessing, outlier detection, statistical analysis, feature selection, model validation and calibration to postprocessing. Reports, figures, datasheets and maps could be generated in each step to clarify the underlying processes.

All in all, this tool aims (1) to offer an integrated, open-source Python library that is dedicated to the water isotopic composition statistical-regression modelling (2) to potentially improve our understanding of the precipitation stable isotopes by implementing novel machine-learning tools; and (3) to ensure reproducible research in environmental studies.