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

Опубликовано: 20 Октябрь 2021
на канале: Hatari Labs

3,406

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 tutorial 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 3.9 on a Anaconda Prompt environment.

Input data
You can download the input data from this link:
https://hatarilabs.com/ih-en/fill-mis...