Open Webinar: Principal Component Analysis (PCA) of Water Chemistry data with Python

There are several ways to analyze the correlation among chemistry components, however the methods to cluster water samples to analyze its source, degree of contamination or chemical reactions are not well developed or not well standarized. We have developed an applied case of Principal Component Analysis (PCA) for water samples of dataset used for identifying causes of high uranium concentration in the San Joaquin Valley, California, US. This webinar will cover all steps involved on the PCA analysis with Python on a Jupyter notebook together with a agglomerative clustering analysis, the webinar will cover as well a comparison of related samples on the dendogram with their position on the PCA plot.

Instructor / Instructor:

Saul Montoya M.Sc

Hydrogeologist - Numerical Modeler

Mr. Montoya is a Civil Engineer graduated from the Catholic University in Lima with postgraduate studies in Management and Engineering of Water Resources (WAREM Program) from Stuttgart University – Germany with mention in Groundwater Engineering and Hydroinformatics. Mr Montoya has a strong analytical capacity for the interpretation, conceptualization and modeling of the surface and underground water cycle and their interaction. He is in charge of numerical modeling for contaminant transport and remediation systems of contaminated sites. Inside his hydrological and hydrogeological investigations Mr. Montoya has developed an holistic comprehension of the water cycle, understanding and quantifying the main hydrological dynamic process of precipitation, runoff, evaporation and recharge to the groundwater system.

Language / Lenguaje:

English

Event date / Fecha del evento:

Monday, February 28, 2022 6:00 p.m. Amsterdam Time

Hosted by / Organizado por:

Hatarilabs

Stream link / Enlace de transmisión:

https://meet.google.com/ksv-tkia-gho

Input data / Datos de entrada:

https://owncloud.hatarilabs.com/s/17guKP4OU6TnUCU

Additional instructions / Instrucciones adicionales:

Password to download input data: Hatarilabs. You need to have Anaconda installed on your computer.