Isocompy

Isocompy models isotopic compositions using machine learning to estimate stable water isotope variations from user-defined environmental and meteorological variables.

Key Features:

• Machine Learning Integration: Employs machine learning algorithms to model isotopic data and generate estimations and predictions.
• Comprehensive Data Handling: Implements dataset preprocessing, outlier detection, statistical analysis, feature selection, model validation, calibration, and postprocessing.
• Discontinuous Input Handling: Operates with discontinuous inputs in both time and space to accommodate sparse environmental datasets.
• Automatic Decision-Making: Incorporates automatic decision-making procedures across algorithmic stages for model selection and processing steps.
• Output Generation: Produces detailed outputs including reports, figures, and spatial maps relevant to stable water isotope studies.

Scientific Applications:

• Stable water isotope analysis: Analysis and estimation of stable water isotope compositions in environmental datasets.
• Meteorological and precipitation isotope studies (Northern Chile): Modeling meteorological features and isotopic composition of precipitation demonstrated in a case study from Northern Chile with results comparable to prior studies.

Methodology:

Computational steps explicitly include dataset preprocessing, outlier detection and statistical analysis, feature selection, machine learning–based modeling, model validation and calibration, and postprocessing.