SkyLake's starred repositories
Awesome-Geospatial
Long list of geospatial tools and resources
Python-Practical-Application-on-Climate-Variability-Studies
This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python.
spatial_rf_python
Benchmarking of spatial regression methods with respect to spatial heterogeneity, and providing a Python implementation of spatial Random Forests
elnino_prediction
elnino_prediction
RNN_Pytorch
Using the Pytorch to build an image temporal prediction model of the encoder-forecaster structure, ConvGRU kernel & ConvLSTM kernel
ClimateHack-Model
ConvLSTM and Optical Flow for prediction of 24 geospatial images given 12
3D-storm-nowcasting
Three-dimensional gridded radar echo extrapolation for convective storm nowcasting based on a 3D-ConvLSTM model
Conv_LSTM_Time_Series_Prediction
Predicting Iot Sensors' values, using ConvLSTM Time Series model
Wind_Prediction-Seq2Seq-pred
Sequence to Sequence Prediction using CNN-LSTM, ConvLSTM, SBU-LSTM, BD-LSTM, etc.
M5ForecstingAccuracy
ConvLSTM based solution for sales forecasting problem.
Video-Frame-Prediction
In this repository, we focus on video frame prediction the task of predicting future frames given a set of past frames. We present an Adversarial Spatio-Temporal Convolutional LSTM architecture to predict the future frames of the Moving MNIST Dataset. We evaluate the model on long-term future frame prediction and its performance of the model on out-of-domain inputs by providing sequences on which the model was not trained.
Basic_PredRNN_Seq2seq
从Convlstm2D的基础上改编: https://github.com/ndrplz/ConvLSTM_pytorch 可用于雷达回波等短临预报领域,采用模仿Dr.SXJ的Seq2seq结构.
EnvironmentPrediction
Implementation of occupancy grid predictions via ConvLSTM video frame prediction architecture.
SFO-weather-prediction
Predict weather at San Francisco International Airport (SFO) using long short-term memory (LSTM).
ml_drought
Machine learning to better predict and understand drought. Moving github.com/ml-clim
downscale-sst
Downscaling Oceanographic Satellite Data with Convolutional Neural Networks
Cloud-Cover-Prediction
Next Sequence Prediction of Satellite Images using Neural Networks
Numpy-array-to-Images
A python script that generates the frame by frame images for a given Numpy pixel array.[Keras ConvLSTM data block]
Arctic_SIC_prediction
Convolutional Recurrent Neural Networks for Predicting Sea Ice Concentration in the Arctic
ConvLSTM-RAU-net
Spatial-temperal Prediction Model based on history observation and WRF numerical prediction
tensorflow-deeplab-v3-plus
DeepLabv3+ built in TensorFlow