Isotope mixing scenarios and machine learning model in: To what extent are the source mixing models accurate: evaluation of the model accuracy and guidelines for the site-specific model selection
Data files
Oct 12, 2023 version files 41.97 KB
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mixing_scenarios.xlsx
41 KB
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README.md
966 B
Jul 18, 2024 version files 128.50 KB
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Case_studies.xlsx
34.55 KB
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Mixing_scenarios.xlsx
91.44 KB
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README.md
2.51 KB
Abstract
Source mixing models are applied broadly to ecological and hydrological studies, but their accuracy in calculating source contribution (pi) has never been evaluated due to the unknowable nature of the actual source mixing ratios. Moreover, the effect of the external influence originated from the characteristics of the user-provided data has never been quantified, hampering the establishment of the model selection framework suitable for diverse study backgrounds. Therefore, we (1) evaluated the model accuracy in estimating pi of an iterative model (IsoSource) and three Bayesian models (MixSIR, SIMMR, and MixSIAR) under 500 mixing scenarios with predefined mixing ratios, and (2) analyzed the influences of external factors on the model performance. We aimed to build a model-assessment framework and provided a guideline for model selection. Our results from the mixing scenarios of unprecedented size demonstrate that the Bayesian models, particularly SIMMR, exhibited significantly improved performance in estimating the actual pi. As the external influences, less uniform pi vectors and higher underdetermination of pi distributions results in decreased accuracy of model estimation. The effect of the number of sources is negligible when it was less than seven in a dual-tracer occasion. We identify the key parameters and predict the bias of the source mixing model through machine learning approach. Our study is the first evaluation of source mixing model accuracy in pi estimation, and thus crucial for ecohydrological research. We also propose a model selection guideline for various research environments and sampling regimes.
README: Isotope mixing scenarios and machine learning models
https://doi.org/10.5061/dryad.sbcc2frd2
This data includes the isotope mixing scenarios and the machine learning models used in the paper entitled To what extent are the source mixing models accurate: evaluation of the model accuracy and guidelines for the site-specific model selection which have been submitted to the journal Water Resource Research. These scenarios were presumptively created to evaluate the accuracy of the isotope source mixing models, i.e. IsoSource, MixSIR, SIMMR, and MixSIAR. The machine learning models are established for the prediction of model estimation bias in field studies.
Description of the data and file structure
The file Mixing scenarios.xlsx contains five sheets with the level of NOS as the sheet's name. These five sheets contain the mixing scenarios under different levels of NOS mentioned in the manuscript. The file Case studies.xlsx contains the external factors of the two field study datasets of the manuscript. The file machine_learning_script.R and machine_learning_models.Rdata contain the R script and data of the machine learning models, with instructions inside.
Mixing scenarios.xlsx
NOS: Number of source.
pi,a: The actual proportional source contribution of the i source to its mixture designed in the mixing scenarios.
δ2Hsource,i: The 2H isotope signature of the i source.
δ2Hmixture: The 2H isotope signature of the mixture.
δ18Osource,i: The 18O isotope signature of the i source.
Case studies.xlsx
Zhangbei and Lushan are the two different study areas.
NOS: Number of source.
DFC: The proportional isotopic signature deviation from the mixture to the center of the mixing polygon.
DFM: The proportional isotopic signature deviation of a source to its mixture.
lnCVe: The natural logarithmic form of the coefficient of variation of the estimated proportional source contribution in a mixing process.
lnIQR: The natural logarithmic form of the interquartile range of the model estimation distribution. NOS: The number of sources.
δ18Omixture: The 18O isotope signature of the mixture
Detailed descriptions of file machine_learning_script.R and machine_learning_models.Rdata is in the captions of machine_learning_script.R
Code/Software
R is required to load the machine_learning_models.Rdata and run the machine_learning_script.R. Annotations are provided throughout the script.