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Geology 2022

Aida H Baghanam

Aida H Baghanam, Speaker at Geology Conference 2022
University of Tabriz, Iran (Islamic Republic of)
Title : Deep (CNN) or shallow (FFNN) learning for statistical downscaling of climate data?


Encountering with stochastic inherent climate parameters, get cumbersome in computational phase. Precipitation and temperature are also the most-known climate parameters that affects local climate formation. Simulating the variability of these two parameters from past era till present, allow managers to manage effectively and make policies accurately. The simulation of precipitation which formed based on bunch of interactions, led it “hard-to-gain” trustable results. Thus, studying the characteristics of such stochastic parameters has been the subject of high priority for researchers. Using the AI-based models led to gain accurate results. On the other hand, deep-based models have successfully applied in climate issues and the superiority of these methods are proofed. In this research, local precipitation and temperature are projected based on state-of-the-art method named Convolutional Neural Network (CNN) for the end of century using General Circulation Model (GCM) dataset obtained from coupled Model Inter-comparison Project 6 (CMIP6). Also shallow-layered Feed Forward Neural Network (FFNN) was used as evaluation of the performance of CNN model. Ultimately, the performance of models evaluated based on Root Mean Squared Error (RMSE) and Coefficient of Determination (DC) criteria and results denoted that the CNN standalone outperformed over shallow-layered FFNN coupled with pre-processing techniques.


Dr. Aida H. Baghanam started to serve as an assistant professor in the Civil and Environmental Engineering Department at the University of Tabriz, Iran, since August 2019. She gained invaluable experience in computer modeling, atmospheric science, soil physics, hydrology, and solute transport in the subsurface as well as various other numerical modeling techniques during her academic life. During recent years she focused on the impact of climate change on the water resources, where she delved into applying Artificial Intelligence models to improve the performance of climate change impact models. She went over on various bias correction and predictor screening models to enhance precipitation and temperature downscaling models’ precision. She published over 20 refereed journal papers since 2012, as well as 6 book chapters and several conference papers. Her citation indices in Google Scholar are H-index of 130 and i-index of 15 and total citations of 1181.

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