Five Most Used Machine Learning Techniques in Hydroinformatics
Hydroinformatics is the engineering of how AI,ML and data science can be utilized in water resources.
The article with more detail can be found herehere.
You may also like :
HydroGeek: The newsletter for researchers of water resources
https://hydrogeek.substack.com/
Baipatra VSC: Enroll for online courses for Free
http://baipatra.ws
Energy in Style: Participate in Online Internships for Free
http://energyinstyle.website
Innovate S: Online Shop for Water Researchers
https://baipatra.stores.instamojo.com/
Call for Paper: International Journal of HydroClimatic Engineering
http://energyinstyle.website/journals/
Hydro Geek Newsletter Edition 2023.1
https://notionpress.com/read/hydro-geek-newsletter-edition-2023-1
Introduction to Model Development for Prediction, Simulation, and Optimization.
https://imojo.in/1DJDUzm
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 ...
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 /
The principal objective of hydrologic models is to forecast the runoff of a surface water body, especially dendritic systems like rivers, streams, etc. The inputs to these models are generally Rainfall/Precipitation, Soil Characteristics, and other Climatic parameters like evapotranspiration, humidity, etc. LULC and geo-morphology are also used as the required input parameters of the hydrologic models. Both input and output of these models are temporally as well as spatially variable. Now the resolution varies with different models. Some models consider all the sub-basins to be a single watershed and determine the output based on the characteristics of this single watershed(lumped).In contrast, some other models will consider the {impact|effect} of each of the sub-basins on the central outflow of the watershed(distributed).In a few models, the entire watershed is divided into grids or units of uniform dimension. However, the accuracy is highest for the models, which considers the {impa...