An open source Python library for environmental isotopic modelling

Isotopic composition modelling is a key aspect in many environmental studies. This work presents Isocompy, an open source Python library that estimates isotopic compositions through machine learning algorithms with user-defined variables. Isocompy includes dataset preprocessing, outlier detection, s...

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Published inScientific reports Vol. 13; no. 1; pp. 1895 - 19
Main Authors Hassanzadeh, Ashkan, Valdivielso, Sonia, Vázquez-Suñé, Enric, Criollo, Rotman, Corbella, Mercè
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 02.02.2023
Nature Publishing Group
Nature Portfolio
Subjects
704/106/242
704/2151/241
Algorithms
Decision making
Environmental research
Environmental studies
General circulation models
Humanities and Social Sciences
Humidity
Hydrology
Isotope studies
Isotopes
Libraries
Machine learning
Mathematical models
multidisciplinary
Phase transitions
Precipitation
Science
Science (multidisciplinary)
Statistical analysis
Online AccessGet full text
ISSN2045-2322
2045-2322
DOI10.1038/s41598-023-29073-2

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Abstract Isotopic composition modelling is a key aspect in many environmental studies. This work presents Isocompy, an open source Python library that estimates isotopic compositions through machine learning algorithms with user-defined variables. Isocompy includes dataset preprocessing, outlier detection, statistical analysis, feature selection, model validation and calibration and postprocessing. This tool has the flexibility to operate with discontinuous inputs in time and space. The automatic decision-making procedures are knitted in different stages of the algorithm, although it is possible to manually complete each step. The extensive output reports, figures and maps generated by Isocompy facilitate the comprehension of stable water isotope studies. The functionality of Isocompy is demonstrated with an application example involving the meteorological features and isotopic composition of precipitation in N Chile, which are compared with the results produced in previous studies. In essence, Isocompy offers an open source foundation for isotopic studies that ensures reproducible research in environmental fields.
AbstractList Isotopic composition modelling is a key aspect in many environmental studies. This work presents Isocompy, an open source Python library that estimates isotopic compositions through machine learning algorithms with user-defined variables. Isocompy includes dataset preprocessing, outlier detection, statistical analysis, feature selection, model validation and calibration and postprocessing. This tool has the flexibility to operate with discontinuous inputs in time and space. The automatic decision-making procedures are knitted in different stages of the algorithm, although it is possible to manually complete each step. The extensive output reports, figures and maps generated by Isocompy facilitate the comprehension of stable water isotope studies. The functionality of Isocompy is demonstrated with an application example involving the meteorological features and isotopic composition of precipitation in N Chile, which are compared with the results produced in previous studies. In essence, Isocompy offers an open source foundation for isotopic studies that ensures reproducible research in environmental fields.
Isotopic composition modelling is a key aspect in many environmental studies. This work presents Isocompy, an open source Python library that estimates isotopic compositions through machine learning algorithms with user-defined variables. Isocompy includes dataset preprocessing, outlier detection, statistical analysis, feature selection, model validation and calibration and postprocessing. This tool has the flexibility to operate with discontinuous inputs in time and space. The automatic decision-making procedures are knitted in different stages of the algorithm, although it is possible to manually complete each step. The extensive output reports, figures and maps generated by Isocompy facilitate the comprehension of stable water isotope studies. The functionality of Isocompy is demonstrated with an application example involving the meteorological features and isotopic composition of precipitation in N Chile, which are compared with the results produced in previous studies. In essence, Isocompy offers an open source foundation for isotopic studies that ensures reproducible research in environmental fields.Isotopic composition modelling is a key aspect in many environmental studies. This work presents Isocompy, an open source Python library that estimates isotopic compositions through machine learning algorithms with user-defined variables. Isocompy includes dataset preprocessing, outlier detection, statistical analysis, feature selection, model validation and calibration and postprocessing. This tool has the flexibility to operate with discontinuous inputs in time and space. The automatic decision-making procedures are knitted in different stages of the algorithm, although it is possible to manually complete each step. The extensive output reports, figures and maps generated by Isocompy facilitate the comprehension of stable water isotope studies. The functionality of Isocompy is demonstrated with an application example involving the meteorological features and isotopic composition of precipitation in N Chile, which are compared with the results produced in previous studies. In essence, Isocompy offers an open source foundation for isotopic studies that ensures reproducible research in environmental fields.
Abstract Isotopic composition modelling is a key aspect in many environmental studies. This work presents Isocompy, an open source Python library that estimates isotopic compositions through machine learning algorithms with user-defined variables. Isocompy includes dataset preprocessing, outlier detection, statistical analysis, feature selection, model validation and calibration and postprocessing. This tool has the flexibility to operate with discontinuous inputs in time and space. The automatic decision-making procedures are knitted in different stages of the algorithm, although it is possible to manually complete each step. The extensive output reports, figures and maps generated by Isocompy facilitate the comprehension of stable water isotope studies. The functionality of Isocompy is demonstrated with an application example involving the meteorological features and isotopic composition of precipitation in N Chile, which are compared with the results produced in previous studies. In essence, Isocompy offers an open source foundation for isotopic studies that ensures reproducible research in environmental fields.
ArticleNumber 1895
Author Hassanzadeh, Ashkan
Vázquez-Suñé, Enric
Corbella, Mercè
Criollo, Rotman
Valdivielso, Sonia
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  givenname: Mercè
  surname: Corbella
  fullname: Corbella, Mercè
  organization: Departament de Geologia, Universitat Autònoma de Barcelona (UAB)
BackLink https://www.ncbi.nlm.nih.gov/pubmed/36732615$$D View this record in MEDLINE/PubMed
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SSID ssj0000529419
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Snippet Isotopic composition modelling is a key aspect in many environmental studies. This work presents Isocompy, an open source Python library that estimates...
Abstract Isotopic composition modelling is a key aspect in many environmental studies. This work presents Isocompy, an open source Python library that...
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Publisher
StartPage 1895
SubjectTerms 704/106/242
704/2151/241
Algorithms
Decision making
Environmental research
Environmental studies
General circulation models
Humanities and Social Sciences
Humidity
Hydrology
Isotope studies
Isotopes
Libraries
Machine learning
Mathematical models
multidisciplinary
Phase transitions
Precipitation
Science
Science (multidisciplinary)
Statistical analysis
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Title An open source Python library for environmental isotopic modelling
URI https://link.springer.com/article/10.1038/s41598-023-29073-2
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