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Learning the Indicative Patterns of Simulated Force Changes in Soil Moisture by BP Neural Networks and Finding Differences with SMAP Observations

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  • Xiaoning Li

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China
    College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China)

  • Hongwei Zhao

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Chong Sun

    (College of Information Media, Jilin Province Economic Management Cadre College, Changchun 130012, China)

  • Xiaofeng Li

    (College of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Xiaolin Li

    (School of Marxism, Changchun Normal University, Changchun 130032, China)

  • Yang Zhao

    (College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China)

  • Xuezhi Wang

    (College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China)

Abstract

Soil moisture is a vital land surface variable that can influence climate change. Many problems in soil moisture data require the identification of signals obscured by anthropogenic external forces (including greenhouse gases such as CO 2 and aerosol radiative force), natural forces (such as volcanic and solar activity), and internal variability (such as ENSO, NAO, and PDO). Although artificial neural networks (ANNs) have been widely studied in making accurate predictions, the studies of interpretation of ANNs in soil moisture are still rare. Hence, the proposed method aims to assist in the study of interpretating soil moisture data. Specifically, first, an ANN model is trained to predict the approximate year of the simulations by identifying the spatial patterns of qualitative changes in soil moisture. After accurately predicting the approximate year, the spatial patterns in the ANN model, acting as “reliable indicators” of the force changes, are the different natures of regional signals. Then, the simulated data and Soil Moisture Active and Passive (SMAP) observations are fed into the trained ANN separately, and the specific differences are observed by the Deep Taylor Decomposition (DTD) visualization tool. By comparing with the standard multiple linear regression method, the results of the ANN model can provide the reliable indicators of change for a specific year, thus providing meaningful information from the ANN model according to the common soil moisture data. The results show that a large correlation exists between eastern Asia and western North America during the 21st century, and the correlation increases with time in Australia. This also reflects the strong force signal due to a combination of anthropogenic and external forces that has played a role in soil moisture over the decades and can clearly discern the differences between model simulations and observed data. This study indicates that the proposed method using ANNs and visualization tools enables relatively accurate predictions and the discovery of unknown patterns within soil moisture data.

Suggested Citation

  • Xiaoning Li & Hongwei Zhao & Chong Sun & Xiaofeng Li & Xiaolin Li & Yang Zhao & Xuezhi Wang, 2022. "Learning the Indicative Patterns of Simulated Force Changes in Soil Moisture by BP Neural Networks and Finding Differences with SMAP Observations," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11310-:d:910727
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    References listed on IDEAS

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    1. Malte Meinshausen & S. Smith & K. Calvin & J. Daniel & M. Kainuma & J-F. Lamarque & K. Matsumoto & S. Montzka & S. Raper & K. Riahi & A. Thomson & G. Velders & D.P. Vuuren, 2011. "The RCP greenhouse gas concentrations and their extensions from 1765 to 2300," Climatic Change, Springer, vol. 109(1), pages 213-241, November.
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