yx577,github

2daycourse

My 2-day short course on spatial data analytics and geostatistics. I hope these resources are helpful, Prof. Michael Pyrcz

a-beginner-guide-to-carry-out-extreme-value-analysis-with-codes-in-python

A beginner's guide to carry out extreme value analysis, which consists of basic steps, multiple distribution fitting, confidential intervals, IDF/DDF, and a simple application of IDF information for roof drainage design. The guide mainly focuses on extreme rainfall analysis. However, the basic steps are also suitable for other climatic or hydrologic variables such as temperature, wind speed or runoff.

air-quality

Air quality - machine learning classification

an-introduction-to-statistical-learning

This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

antecedent-precipitation-tool

Created to automate the climatological analysis workflows USACE Regulatory Project Managers are required to perform to comply with long-standing agency guidance as well as the new Navigable Waters Protection Rule, effective June 22, 2020..

applegate_etal_2015_code_package

Materials for reproducing figures from Applegate et al. (2015, Climate Dynamics)

aqualord

Data for Water Resource Management

area-water-resources-optimization

This is a multi-objective optimization with constriant NSGA-2

artificial-intelligence

A machine-learning application for short term day trading with prediction governed by linear Support Vector Machine (SVM) in python using scikit learn

asfpm2020-demo

Probabilistic Flood Risk Analysis to Support Risk Informed Decision-Making: Examples and Materials

atlas

Datasets, code and virtual workspace for the Climate Change ATLAS

atmds

This repository is under construction. It will be used to store all necessary function for the downscaling of atmspheric forcing variables.

augmented-convlstm

A statistical downscaling approach using ConvLSTMs.

awesome-atmos

A curated list of awesome Python libraries, software and resources in Atmosphere, Environment and Machine Learning

bayesian-multivariate-time-series-causal-inference

R code for ''Bayesian method for causal inference in spatially-correlated multivariate time series''

bicycle-network-optimization

Dissertation for my MSc in Smart Cities and Urban Analytics (UCL CASA)

biwavelet

R package to conduct univariate and bivariate wavelet analyses

ca_electricty_price_prediction_neural_net

Time series regression models using ARIMA, SARIMAX, and Recursive Neural Network to predict day-ahead and hour-ahead California wholesale electricity prices. Features include demand forecasts, NOAA weather station data, and CA Dept. of Water Resources reservoir water level hourly observation.

calculate-precipitation-based-agricultural-drought-indices-with-python

Precipitation-based indices are generally considered as the simplest indices because they are calculated solely based on long-term rainfall records that are often available. The mostly used precipitation-based indices consist of Decile Index (DI) Hutchinson Drought Severity Index (HDSI) Percen of Normal Index (PNI) Z-Score Index (ZSI) China-Z Index (CZI) Modified China-Z Index (MCZI) Rainfall Anomaly Index (RAI) Effective Drought Index (EDI) Standardized Precipitation Index (SPI).

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