Abstract
Prediction of wetland greenhouse gas (GHG) fluxes has been a challenging undertaking. Machine learning techniques such as the artificial neural network (ANN) has a strong potential to provide high quality predictions of the wetland GHG fluxes. We developed eight different ANN models and investigated their suitability to predict the major GHG fluxes (CO2 and CH4) in coastal salt marshes (dominated by Spartina alterniflora) of Waquoit Bay, Massachusetts, USA. Based on the dominant environmental drivers, the daytime net uptake fluxes of CO2 were predicted as a function of photosynthetically active radiation, soil temperature (ST), and porewater salinity (SS). The net emission fluxes of CH4 were predicted as a function of ST and SS. Our models with the radial basis function neural network (RBNN) provided the most accurate and least-biased predictions of the net CO2 uptake (Nash-Sutcliffe Efficiency, NSE = 0.98) and CH4 emission (NSE = 0.90-0.92). The linear layer neural network generated the least successful and most biased predictions of the GHG fluxes (NSE = 0.48-0.80). Other ANNs, including the commonly-used feed forward neural network (FFNN), provided less accurate and more biased predictions of the CO2 (NSE = 0.86-0.97) and CH4 (NSE = 0.73-0.89) fluxes than the RBNN. We, therefore, recommend using RBNN as the first choice and FFNN (or its variant) as the second choice for predicting the GHG fluxes in coastal salt marshes. Our findings and tools would help derive plausible scenarios and guidelines for restoration, monitoring, and maintenance of coastal salt marshes in the U.S. and beyond.
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Acknowledgements
This research was funded by a grant from the U.S. National Science Foundation (NSF) awarded to Abdul-Aziz (NSF CBET Environmental Sustainability Award No. 1705941). The datasets used in this study were collected through a project from the National Oceanic and Estuarine Administration (NOAA)’s National Estuarine Research Reserve Science Collaborative (NOAA Project No. NA09NOS4190153), awarded to Abdul-Aziz. All data were adequately described in the main text, figures, tables, and in the supporting information. The complete dataset is available in the figshare data repository at https://doi.org/10.6084/m9.figshare.15125148.v1.
Availability of data and material
The dataset was deposited in figshare under the following reference: Abdul-Aziz, Omar I.; Tang, Jianwu; Moseman-Valtierra, Serena (2021): GHG flux dataset of Waquoit Bay, MA, USA saltmarshes (May-October 2013). figshare. Dataset. https://doi.org/10.6084/m9.figshare.15125148.v1.
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Built-in functions of MATLAB2020a were used for all coding, analysis, and visualization related to the model developments and evaluations. The MATLAB functions used in this study are described in Text S1 in supplementary information.
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U.S. National Science Foundation (NSF) (NSF CBET Environmental Sustainability Award No. 1705941) and National Oceanic and Estuarine Administration (NOAA)’s National Estuarine Research Reserve Science Collaborative (NOAA Project No. NA09NOS4190153), awarded to Abdul-Aziz.
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Abdul-Aziz conceptualized the research idea. Abdul-Aziz and Zaki designed the methodology, conducted the analyses, and summarized the results. Both authors contributed to the writing. Abdul-Aziz administered the projects funding the research and supervised Zaki. All authors have read and agreed to the published version of the manuscript.
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Zaki, M.T., Abdul-Aziz, O.I. Predicting greenhouse gas fluxes in coastal salt marshes using artificial neural networks. Wetlands 42, 37 (2022). https://doi.org/10.1007/s13157-022-01558-2
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DOI: https://doi.org/10.1007/s13157-022-01558-2