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gapfill

Fill the gap of precipitation and temperature data from station observations

glcm-svm

提取图像的灰度共生矩阵（GLCM），根据GLCM求解图像的概率特征，利用特征训练SVM分类器，对目标分类

gmd-code

gpm-bico

Bias Correction Tool for GPM precipitation data

gwr-oi

A two-step merging and downscaling method for satellite precipitation

hagelslag

Hagelslag is an object-based severe storm hazard forecasting system.

hur-detect

Deep Semi-Supervised Object Detection for Extreme Weather Events

icip2016

Anchored Neighborhood Regression based Single Image Super-Resolution from Self-Examples

inpainting

Image inpainting via dictionary learning and sparse representation.

kaggle-rainfall-prediction

This machine learning project learnt and predicted rainfall behavior based on 14 weather features. Applied KNN model, Clustering model and Random Forest model.

kpca

kpca,matlab

land-cover-land-use-project

The purpose off this project is o investigate the potential of using passive microwave brightness temperature and emissivity data sets from satellite observationsto monitor recent land cover/land use changes and droughts. Temperature and emissivity data at differentvarious frequencies will be formulated to calculate global land surface emissivityfor the last three dacades.The estimates are then compared against the independent drought indicators which are based on percipitation and soil moisture data records.

lle-locally-linear-embedding-

LLE (Locally Linear Embedding) , matlab

longleadextremeprecipitationforecasting

CS670 - Artificial Intelligence | UMass Boston

lpp

LPP(Locality Preserving Projections) , Matlab

lpt-matlab

Large Scale Precipitation Tracking (Kerns and Chen 2016, JGR) code in Matlab.

lpt-python-public

Public release version of Large Scale Precipitation Tracking (LPT). Kerns and Chen (2020, JGR Atmospheres).

machine-learning-models

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

machine_learning

机器学习算法的实现和应用

ml_classification

Analyzing SVM and KNN classification using sklearn module

ml_regression

Comparing SVM & KNN Regression

mljar-supervised

Automated Machine Learning for Supervised Tasks: Binary Classification, MultiClass Classification and Regression (work in progress)

mlp

MultiLayer perception for classification and regression

mlp-and-smv-classification-

simple comparative of SVM and MLP classifiers using Heart Disease UCI Database

mnist-classification

Pytorch、Scikit-learn实现多种分类方法，包括逻辑回归（Logistic Regression）、多层感知机（MLP）、支持向量机（SVM）、K近邻（KNN）、CNN、RNN，极简代码适合新手小白入门，附英文实验报告（ACM模板）

mondrianforest

Code for Mondrian Forests (for classification and regression)

mradar

处理雷达数据的matlab程序包

nanjing-university-of-information-science-technology-niust-

nearest-neighbour-algorithm

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.[1] In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression. In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.

neuralnetworks2s

Python code for MWR paper 'Using Artificial Neural Networks for Generating Probabilistic Subseasonal Precipitation Forecasts over California'