Just a creative person who love to learn and experience new things. I became interested in Geology, Math, Programming and Marketing since I was Kid. I found that I’m Interested in Data Science and Visualizing Data in Python. Also I have some management experiences. I enjoy music, videogames, movies and outdoor activities; also I love to communicate with people and know about their culture. Nowadays I'm trying to play the Kamancheh (an Iranian Instrument).
Description: FEZrs is the name of the Open-source remote sensing project that is developed with Python inspired by ENVI (software developed by L3Harris Geospatial)
Description: -Establish 10 Ground Control Points (and note their geographical coordinates using Base and Rover) -Capture over 250 Images by using DJI Phantom 4 Pro V2.0 (with an overlap of 80%) -Align Photos at Agisoft Metashape -Build Point Cloud -Build Digital Elevation Model -Build Orthomosaic map
Description: Since I had participated in the Entrepreneurship Olympiad, I wanted to create a MOOC centered on the Entrepreneurship Olympiad with the medalists of the Entrepreneurship Olympiad, so I created this course with the participation and contribution of more than 10 medalists of the Entrepreneurship Olympiad, Economics Olympiad and accounting undergraduate students at GreenOly.
Description: Contribution: Data Analysis
Description: In the hydrological cycle, runoff precipitation is one of the most significant and complex phenomena. In order to develop and improve predictive models, different perspectives have been presented in its modeling. Hydrological processes can be confidently modeled with the help of artificial intelligence techniques. In this study, the runoff of the Leilanchai watershed was simulated using artificial neural networks (ANNs) and M5 model tree methods and their hybrid with wavelet transform. Seventy percent of the data used in the train state and thirty percent in the test state were collected in this watershed from 2000 to 2021. In addition to daily and monthly scales, simulated and observed results were compared within each scale. Initially, the rainfall and runoff time series were divided into multiple sub-series using the wavelet transform to combat instability. The resultant subheadings were then utilized as input for an ANN and M5 model tree. The results demonstrated that hybrid models with wavelet improved the ANN model's daily accuracy by 4% and its monthly accuracy by 26%. It also improved the M5 model tree's daily and monthly accuracy by 4% and 41%. The wavelet-M5 model's accuracy does not diminish to the same degree as the wavelet-ANN (WANN) model as the forecast horizon lengthens. Consequently, the Leilanchai watershed has a relatively stable behavior pattern. Finally, hybrid models, in conjunction with the wavelet transform, improve forecast accuracy.