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Variation Trend Analysis of Groundwater Depth with Wavelet Neural Network, and Detection of Relationship Between Climate Variability and Groundwater Variation Depth with Wavelet Analysis (Ghorveh-Dehgolan plain)

Memarian, Ali | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 43060 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Abrishamchi, Ahmad
  7. Abstract:
  8. Over time, human needs more groundwater to meet agricultural, industrial, and urban uses; so the study on factor affecting groundwater and groundwater level changes are important for water resource management. However, often forgotten is the fact that accurate and reliable predictions are based on a correct diagnose of the past. One of the questions here is how the climate has changed since the last. Such a question is largely related to detection. Purpose of detecting climate variability and climate change, identifying climate variability and trends in system and describe the factors causing these changes are. Without knowing and understanding these changes and fluctuations, we are not able to adopt appropriate policies for the long-term future water sources.
    In summary, Purpose of this study was to appraise the effect of parameters on the groundwater level plain Ghorveh_Dehgolan; Therefore, the groundwater level and rainfall data, was classified by the method of principal components analysis. Then, a representative of each cluster was determined. Consequently, the prediction was made only for epresentatives of each cluster, rather than buffering the entire region. Wavelet neural network was used for the prediction of the model and Then, using wavelet analysis to evaluate the effect on the regional climate phenomenon (PDO, ENSO, AMO). Detection studies show that the ENSO phenomenon is the most influential effect on Ghorveh plain water. In addition, wavelet neural network studies show that groundwater levels will drop up to 1 meter in 1391. Therefore, policies must be taken to reduce this effect
  9. Keywords:
  10. Artificial Neural Network ; Principal Component Analysis (PCA) ; Wavelet Transform ; Climate Phenomena Detection ; Varimax Method

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