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A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from SSMIS Data and Evaluation of its Reliability by Uncertainty Parameters

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Abstract

This study demonstrates the application of wavelet transform comprising discrete wavelet transform, maximum overlap discrete wavelet transform (MODWT), and multiresolution-based MODWT (MODWT-MRA), as well as wavelet packet transform (WP), coupled with artificial intelligence (AI)-based models including multi-layer perceptron, radial basis function, adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming to retrieve snow depth (SD) from special sensor microwave imager sounder obtained from the national snow and ice data center. Different mother wavelets were applied to the passive microwave (PM) frequencies; afterward, the dominant resultant decomposed subseries comprising low frequencies (approximations) and high frequencies (details) were detected and inserted into the AI-based models. The results indicated that the WP coupled with ANFIS (WP-ANFIS) outperformed the other studied models with the determination coefficient of 0.988, root mean square error of 3.458 cm, mean absolute error of 2.682 cm, and Nash–Sutcliffe efficiency of 0.987 during testing period. The final verification also confirmed that the WP is a promising pre-processing technique to improve the accuracy of the AI-based models in SD evaluation from PM data.

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All data, models, and code are available from the corresponding author by request.

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The authors did not receive support from any organization for the submitted work.

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Arash Adib, Arash Zaerpour, Ozgur Kisi and Morteza Lotfirad declare that they have contribution in the preparation of this manuscript.

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Correspondence to Arash Adib.

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The manuscript is an original work with its own merit, has not been previously published in whole or in part, and is not being considered for publication elsewhere.

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Arash Adib, Arash Zaerpour, Ozgur Kisi and Morteza Lotfirad have read the final manuscript, have approved the submission to the journal and have accepted full responsibilities pertaining to the manuscript’s delivery and contents.

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Arash Adib, Arash Zaerpour, Ozgur Kisi and Morteza Lotfirad agree to publish this manuscript upon acceptance.

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Adib, A., Zaerpour, A., Kisi, O. et al. A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from SSMIS Data and Evaluation of its Reliability by Uncertainty Parameters. Water Resour Manage 35, 2723–2740 (2021). https://doi.org/10.1007/s11269-021-02863-x

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  • DOI: https://doi.org/10.1007/s11269-021-02863-x

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