nach oben

Water Resources Management

Erschienen in:

13.06.2021

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

verfasst von: Arash Adib, Arash Zaerpour, Ozgur Kisi, Morteza Lotfirad

Erschienen in: Water Resources Management | Ausgabe 9/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel Cloud-Based Multidimensional Parallel Dynamic Programming Algorithm for a Cascade Hydropower System

Nächster Artikel Comparison of Representative Heuristic Algorithms for Multi-Objective Reservoir Optimal Operation

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Abbaspour KC, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J, Srinivasan R (2007) Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J Hydrol 333(2–4):413–430. https://doi.org/10.1016/j.jhydrol.2006.09.014CrossRef

Amiri GG, Shahjouei A, Saadat S, Ajallooeian M (2011) Hybrid evolutionary-neural network approach in generation of artificial accelerograms using principal component analysis and wavelet-packet transform. J Earthquake Eng 15(1):50–76. https://doi.org/10.1080/13632469.2010.517281CrossRef

Ansari H, Marofi S, Mohamadi M (2019) Topography and land cover effects on snow water equivalent estimation using AMSR-E and GLDAS data. Water Resour Manag 33:1699–1715. https://doi.org/10.1007/s11269-019-2200-0CrossRef

Armstrong R, Knowles K, Brodzik M, Hardman MA (1994) DMSP SSM/I-SSMIS pathfinder daily EASE-Grid brightness temperatures, version 2. NASA National Snow Ice Data Center Distributed Active Archive Center: Boulder, Colorado, USA. https://doi.org/10.5067/3EX2U1DV3434

Azamathulla HM, Ghani AA, Leow CS, Chang CK, Zakaria NA (2011) Gene-Expression Programming for the development of a stage-discharge curve of the Pahang River. Water Resour Manag 25(11):2901–2916. https://doi.org/10.1007/s11269-011-9845-7CrossRef

Barak S, Sadegh SS (2016) Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm. Int J Elec Power 82:92–104. https://doi.org/10.1016/j.ijepes.2016.03.012CrossRef

Chang ATC, Foster JL, Hall DK (1987) Nimbus-7 SMMR derived global snow cover parameters. Ann Glac 9:39–44. https://doi.org/10.3189/S0260305500200736CrossRef

Coifman RR, Wickerhauser MV (1992) Entropy-based algorithms for best basis selection. IEEE T Inform Theory 38(2):713–718. https://doi.org/10.1109/18.119732CrossRef

Farsani PA, Vafakhah M, Farsani HK, Hertig E (2020) Regional flood frequency analysis through some machine learning models in semi-arid regions. Water Resour Manag 34:2887–2909. https://doi.org/10.1007/s11269-020-02589-2CrossRef

Ferreira C (2001) Gene expression programming: A new adaptive algorithm for solving problems. Com Sys 13(2):87–129

Foster JL, Hall DK, Chang ATC, Rango A (1984) An overview of passive microwave snow research and results. Rev Geophys Space Phys 22(2):195–208. https://doi.org/10.1029/RG022i002p00195CrossRef

Graf R, Zhu S, Sivakumar B (2019) Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach. J Hydrol 578:124115. https://doi.org/10.1016/j.jhydrol.2019.124115CrossRef

Grody NC, Basist AN (1996) Global identification of snow cover using SSM/I measurements. IEEE T Geosci Remote 34(1):237–249. https://doi.org/10.1109/36.481908CrossRef

Hadad K, Pourahmadi M, Maraghi HM (2011) Fault diagnosis and classification based on wavelet transform and neural network. Prog Nucl Energy 53(1):41–47. https://doi.org/10.1016/j.pnucene.2010.09.006CrossRef

Hazarika BB, Gupta D, Berlin M (2020) Modeling suspended sediment load in river using extreme learning machine and tin support vector regression with wavelet conjunction. Environ Earth Sci 79:234. https://doi.org/10.1007/s12665-020-08949-wCrossRef

Hong H, Zhang Z, Guo A, Shen L, Sun H, Liang Y, Wu F, Lin H (2020) Radial basis function artificial neural network (RBF ANN) as well as the hybrid method of RBF ANN and grey relational analysis able to well predict trihalomethanes levels in tap water. J Hydrol 591:125574. https://doi.org/10.1016/j.jhydrol.2020.125574CrossRef

Jang JSR (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE T Syst Man Cy-S 23(3):665–685. https://doi.org/10.1109/21.256541CrossRef

Johnston J, Maggioni V, Houser P (2020) Comparing global passive microwave freeze/thaw records: Investigating differences between Ka- and L-band products. Remote Sens Environ 247:111936. https://doi.org/10.1016/j.rse.2020.111936CrossRef

Mohammadi B, Ahmadi F, Mehdizadeh S, Guan Y, Pham QB, Linh NTT, Tri DQ (2020) Developing novel roboust models to improve the accuracy of daily streamflow modeling. Water Resour Manag 34(10):3387–3409. https://doi.org/10.1007/s11269-020-02619-zCrossRef

Moody GB, Mark RG (2001) The impact of the MIT-BIH Arrhythmia Database. IEEE Eng Med Biol 20(3):45–50. https://doi.org/10.1109/51.932724CrossRef

Moosavi V, Malekinezhad H, Shirmohammadi B (2014) Fractional snow cover mapping from MODIS data using wavelet-artificial intelligence hybrid models. J Hydrol 511:160–170. https://doi.org/10.1016/j.jhydrol.2014.01.015CrossRef

Moreno-Rodenas AM, Tscheikner-Gratl F, Langeveld JG, Clemens FHLR (2019) Uncertainty analysis in a large-scale water quality integrated catchment modeling study. Water Res 158:46–60. https://doi.org/10.1016/j.watres.2019.04.016CrossRef

Percival DB, Walden AT (2000) Wavelet Methods for Time Series Analysis. Cambridge University Press, Online ISBN: 9780511841040, 594 https://doi.org/10.1017/CBO9780511841040

Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3): 379–423, 27(4): 623–656. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

Slater AG, Barrett AP, Clark MP, Lundquist JD, Raleigh MS (2013) Uncertainty in seasonal snow reconstruction: Relative impacts of model forcing and image availability. Adv Water Resour 55:165–177. https://doi.org/10.1016/j.advwatres.2012.07.006CrossRef

Sun Z, Chang CC (2002) Structural damage assessment based on wavelet packet transform. J Struct Eng 128(10):1354–1361. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:10(1354)CrossRef

Truong HK, Keylock CJ, Eckert N, Bellot H, Naaim M (2018) Refining the processing of paired time series data to improve velocity estimation in snow flows. Cold Reg Sci Technol 151:75–88. https://doi.org/10.1016/j.coldregions.2018.03.004CrossRef

Xiao X, Zhang T, Zhong X, Shao W, Li X (2018) Support vector regression snow-depth retrieval algorithm using passive microwave remote sensing data. Remote Sens Environ 210:48–64. https://doi.org/10.1016/j.rse.2018.03.008CrossRef

Yariyan P, Avand M, Abbaspour RA, Karami M, Tiefenbacher J (2020) GIS-based spatial modeling of snow avalanches using four novel ensemble models. Sci Total Environ 745:141008. https://doi.org/10.1016/j.scitotenv.2020.141008CrossRef

Titel: A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from SSMIS Data and Evaluation of its Reliability by Uncertainty Parameters
verfasst von: Arash Adib
Arash Zaerpour
Ozgur Kisi
Morteza Lotfirad
Publikationsdatum: 13.06.2021
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 9/2021
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-021-02863-x

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 9/2021

Assessment and Prediction of Groundwater using Geospatial and ANN Modeling

Fuzzy Representation of Environmental Flow in Multi-Objective Risk Analysis of Reservoir Operation

Oceanic-Atmospheric Variability Influences on Baseflows in the Continental United States

Economic Analysis of Recreation Access at a Lake Facing Water Crisis due to Municipal Water Demand

Development of a Combined Index to Evaluate Sustainability of Water Resources Systems

Modeling the Effectiveness of Rain Barrels, Cisterns, and Downspout Disconnections for Reducing Combined Sewer Overflows in a City-Scale Watershed