Dataset Information

Gradient boosting machine learning to improve satellite-derived column water vapor measurement error.

ABSTRACT: The atmospheric products of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm include column water vapor (CWV) at a 1 km resolution, derived from daily overpasses of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Aqua and Terra satellites. We have recently shown that machine learning using extreme gradient boosting (XGBoost) can improve the estimation of MAIAC aerosol optical depth (AOD). Although MAIAC CWV is generally well validated (Pearson's R >0.97 versus CWV from AERONET sun photometers), it has not yet been assessed whether machine-learning approaches can further improve CWV. Using a novel spatiotemporal cross-validation approach to avoid overfitting, our XGBoost model, with nine features derived from land use terms, date, and ancillary variables from the MAIAC retrieval, quantifies and can correct a substantial portion of measurement error relative to collocated measurements at AERONET sites (26.9% and 16.5% decrease in root mean square error (RMSE) for Terra and Aqua datasets, respectively) in the Northeastern USA, 2000-2015. We use machine-learning interpretation tools to illustrate complex patterns of measurement error and describe a positive bias in MAIAC Terra CWV worsening in recent summertime conditions. We validate our predictive model on MAIAC CWV estimates at independent stations from the SuomiNet GPS network where our corrections decrease the RMSE by 19.7% and 9.5% for Terra and Aqua MAIAC CWV. Empirically correcting for measurement error with machine-learning algorithms is a postprocessing opportunity to improve satellite-derived CWV data for Earth science and remote sensing applications.

SUBMITTER: Just AC

PROVIDER: S-EPMC7665162 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Gradient boosting machine learning to improve satellite-derived column water vapor measurement error.

Just Allan C AC Liu Yang Y Sorek-Hamer Meytar M Rush Johnathan J Dorman Michael M Chatfield Robert R Wang Yujie Y Lyapustin Alexei A Kloog Itai I

Atmospheric measurement techniques 20200902 9

The atmospheric products of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm include column water vapor (CWV) at a 1 km resolution, derived from daily overpasses of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Aqua and Terra satellites. We have recently shown that machine learning using extreme gradient boosting (XGBoost) can improve the estimation of MAIAC aerosol optical depth (AOD). Although MAIAC CWV is generally well validated ...[more]

PMID: 33193906

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Project description:BackgroundA receptive endometrium is a prerequisite for successful embryo implantation. Mounting evidence shows that nearly one-third of infertility and implantation failures are caused by defective endometrial receptivity. This study pooled 218 subjects from multiple datasets to investigate the association of the immune infiltration level with reproductive outcome. Additionally, macrophage-endometrium interaction modules were constructed to explore an accurate and cost-effective approach to endometrial receptivity assessment.MethodsImmune-infiltration levels in 4 GEO datasets (n=218) were analyzed and validated through meta-analysis. Macrophage-endometrium interaction modules were selected based on the weighted gene co-expression network in GSE58144 and differentially expressed genes dominated by GSE19834 dataset. Xgboost, random forests, and regression algorithms were applied to predictive models. Subsequently, the efficacy of the models was compared and validated in the GSE165004 dataset. Forty clinical samples (RT-PCR and western blot) were performed for expression and model validation, and the results were compared to those of endometrial thickness in clinical pregnancy assessment.ResultsAltered levels of Mϕs infiltration were shown to critically influence embryo implantation. The three selected modules, manifested as macrophage-endometrium interactions, were enrichment in the immunoreactivity, decidualization, and signaling functions and pathways. Moreover, hub genes within the modules exerted significant reproductive prognostic effects. The xgboost algorithm showed the best performance among the machine learning models, with AUCs of 0.998 (95% CI 0.994-1) and 0.993 (95% CI 0.979-1) in GSE58144 and GSE165004 datasets, respectively. These results were significantly superior to those of the other two models (random forest and regression). Similarly, the model was significantly superior to ultrasonography (endometrial thickness) with a better cost-benefit ratio in the population.ConclusionSuccessful embryo implantation is associated with infiltration levels of Mϕs, manifested in genetic modules involved in macrophage-endometrium interactions. Therefore, utilizing the hub genes in these modules can provide a platform for establishing excellent machine learning models to predict reproductive outcomes in patients with defective endometrial receptivity.

Dataset Information

Gradient boosting machine learning to improve satellite-derived column water vapor measurement error.

Publications

Gradient boosting machine learning to improve satellite-derived column water vapor measurement error.

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