Fractional snow cover mapping from MODIS data using wavelet-artificial intelligence hybrid models

Vahid Moosavi, Hossein Malekinezhad, Bagher Shirmohammadi

Research output: Contribution to JournalArticleAcademic

Abstract

This study was carried out to evaluate the wavelet-artificial intelligence hybrid models to produce fractional snow cover maps. At first, cloud cover was removed from MODIS data and cloud free images were produced. SVM-based binary classified ETM+ imagery were then used as reference maps in order to obtain train and test data for sub-pixel classification models. ANN and ANFIS-based modeling were performed using raw data (without wavelet-based preprocessing). In the next step, several mother wavelets and levels were used in order to decompose the original data to obtain wavelet coefficients. Then, the decomposed data were used for further modeling processes. ANN, ANFIS, wavelet-ANN and wavelet-ANFIS models were compared to evaluate the effect of wavelet transformation on the ability of artificial intelligence models. It was demonstrated that wavelet transformation as a preprocessing approach can significantly enhance the performance of ANN and ANFIS models. This study indicated an overall accuracy of 92.45% for wavelet-ANFIS model, 86.13% for wavelet-ANN, 72.23% for ANFIS model and 66.78% for ANN model. In fact, hybrid wavelet-artificial intelligence models can extract the characteristics of the original signals (i.e. model inputs) accurately through decomposing the non-stationary and complex signals into several stationary and simpler signals. The positive effect of fuzzification as well as wavelet transformation in the wavelet-ANFIS model was also confirmed. © 2014 Elsevier B.V.
Original languageEnglish
Pages (from-to)160-170
Number of pages11
JournalJournal of Hydrology
Volume511
DOIs
Publication statusPublished - 16 Apr 2014
Externally publishedYes

Keywords

  • Artificial intelligence
  • ETM+
  • MODIS
  • Snow cover mapping
  • Sub-pixel classification
  • Wavelet transformation

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