Introduction to Principal Component Analysis

This is the Part 35 of the Lecture. For the complete lecture on PCA click here.

Principal Component Analysis (PCA) is a statistical technique commonly used in data analysis to simplify the complexity of large datasets by transforming the original variables into a smaller set of uncorrelated variables known as principal components. These components capture the maximum amount of variance in the data, thereby allowing for easier interpretation and visualization of the underlying patterns and relationships. PCA is widely used in fields such as machine learning, image processing, and genetics, providing researchers with valuable insights and actionable information for decision-making. By identifying the key dimensions driving variation in the data, PCA enables researchers to better understand and extract meaningful information from complex datasets.

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Five Most Extreme Hydrologic Events that changed the World

Typhoon Tip, Philipines(Collected from : CSMonitor ) Devastating storms, severe flood, acute famine conditions, etc. hydrological events of extreme nature has changed human history. Any event which is not normal is known as an abnormal event. In the case of hydrology, an event that has a return period of more than 100 years is considered Extreme. According to Herring(2020) of Climate.gov, "An extreme event is a time and place in which weather, climate, or environmental conditions—such as temperature, precipitation, drought, or flooding—rank above a threshold value near the upper or lower ends of the range of historical measurements." Though the threshold is not objective, few researchers have defined "extreme events as those that occur in the highest or lowest 5% or 10% of historical measurements". Some have described events by their deviation from the mean, or by their occurrence interval. Here the most severe five extreme hydrologic events were discussed which ...

Autocorrelation in Water Resource Development

A new article was posted in HydroGeek : Five Most Recent Research Works on Autocorrelation in Water Resource Management Autocorrelation is the correlation between two part of a single data series and is useful when the trendability of a parameter is approximated with the help of data. Most used in water research study. This article highlights the most recent research works on the application of autocorrelation on water resource development studies. Click here to read it in HydroGeek @Mrinmoy's Page @data_hydrology , @Merchandise or @ @products_sustainability Add to Listy /

First Edition of HydroGeek Newsletter for the year 2023 Launched

First Edition of HydroGeek Launched You will be happy to know that the first edition of the HydroGeek Newsletter of the year 2023 is launched The content of the first edition is as given below : Cover Feature: The Free Software for Water Resource Management Feature 1: A case study of the ELECTRE Decision-Making Method in Water Resource: How to use the technique in the selection of the best solution among the available many. Feature 2: Project Idea on Climate Change Impact Studies Feature 3: Instrument Recommendation: An instrument that can monitor more than seven water quality parameters in real-time Regular Features: News and Views, Recommended New and Old Books, More Project Ideas, etc. Click here to access it. #hydrology #hydroinformatics #newsletter @data_hydrology , @Merchandise or @ @products_sustainability Add to Listy /

Hydrology Newsletter

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