Keras is a high level platform for neural networks in Python. This platform is focused on fast experimentation on input data. Keras supports convultional networks, recurrent networks and their combination. In addition, Keras runs seamlessly on CPUs, GPUs and andavanced multiprocessor computers.
The advantage of machine learning libraries as Keras in Python scripts is the practical way to manage the data, the configuration options, the processing capacity and the input/output representation from huge datasets.
This webinar shows the complete process to create a neural network in Keras with a Tensorflow backend done to fill missing precipitation data for a station based on the records from neighboring stations. The scripting is done with Python 3, Keras and Tensorflow on a Jupyter Notebook. The webinar describes Keras commands for the creation, trainning and prediction of a neural netowork, as well as tools in Python for data import and analysis.
Climate change impact assessment on basin water cycle response requires a dataset of precipitation and temperature on certain temporal and spatial scales. Predictive climate change data for hydrological models is available from some webservers, but most times this data is restricted to amount of records available, spatial extend, type of climate change scenario and type of global circulation model (GCM) used. Besides, there are some concepts needed to understand and choose a appropiate dataset for an hydrological model with climate change scenarios.
This webinar aims to teach the most important concepts about climate change models, available web servers, predictive and historical data, data set types for hydrological models and Python commands to obtain precipitation and temperature data in an appropriate format for hydrological modeling.
MODFLOW6 is the last major version of MODFLOW developed by the USGS. This version brings new tools, features, a different arrangement of the working files, many discretization options and a new perspective of MODFLOW as a tool for the simulation of the groundwater flow regimen and the main related physical process.
Flopy is the Python library for the creation, simulation and output analysis of MODFLOW models. The library can handle an variety of MODFLOW versions as MODFLOW 2005, MODFLOW USG, MODFLOW and SEAWAT as well as utilities for model output representation and water balance analysis.
There are limited options of pre- and post-processing software for MODFLOW6, therefore the adoption of MODFLOW6 in research and consulting is limited to highly skilled programmers/modelers. Flopy brings some interesting tools for the development and simulation of MODFLOW6 models.
This webminar will bring a overview of the main features of MODFLOW6 and Flopy with a practical part of groundwater modeling on steady state with MODFLOW6 / Flopy on a Jupyter notebook.