Webinar: Fill Missing Precipitation Data with Machine Learning in Python and Scikit-Learn

Thursday, August 20, 2020
6:00 PM 8:00 PM 18:00 20:00

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Evaluation of hydrological processes as evapotranspiration, runoff, routing and infiltration require data as precipitation, flow, temperature and radiation on a daily basis. Required data for hydrological modeling need to be accurate and must be completed over the study period. It is common that historical data from hydrological stations are incomplete and has many gaps that can be filled by the use of machine learning algorithms like Scikit-Learn in Python3.

This webinar shows a applied procedure to run a complete script for the filling of missing precipitation in one station by the use of data from 2 nearby stations. The script will be run on Python 3 on a Anaconda Prompt environment.

Objectives

We expect that the participants will be able to:

Learn the different tools of Scikit-Learn for data processing and regression.
Apply matplotlib to plot precipitation data.
Know the process and purpose of data scaling.
Evaluate the regression performance.
Save results to a text file.

Instructor

Saul Montoya M.Sc.

Hydrogeologist and 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 a holistic comprehension of the water cycle, understanding and quantifying the main hydrological dynamic process of precipitation, runoff, evaporation and recharge to the groundwater system.