Skip to main content

Seven New Runoff Prediction Models

Seven Most Recent Runoff Prediction Models

Runoff Prediction Models

Runoff Prediction Models(RPM) are those models which predict the runoff or flood peak of a watershed or input runoff to a dam etc. Generally, such models require climatic parameters, geomorphology, soil characteristics, and land cover as input against which a runoff model can predict the monthly, weekly, daily, or even hourly runoff. These models can be spatially distributed or lumped, temporally long or short, data-driven or conceptual. 

In recent years due to the massive development in data-driven and smart concepts like artificial neural networks(ANN), decision trees, and evolutionary algorithms, application of such techniques are now common to develop RPMs. Among data-driven techniques, ANN is the most popular, followed by evolutional algorithms. But compared to the standalone application of neural networks, hybrid models where ANN with conceptual models like HyMOD or HEC is found to be more successful.

Hydrologic models like RPMs need to be calibrated and validated with ground-level primary data and also outputs are compared with the same output from other models. AutoRegressive Integrated Moving Average(ARIMA) was found to be the most used model for comparison.

In the case of selection of RPMs, various statistical indices are used like Root Mean Square Error(RMSE), Mean Absolute Error (MAE), Mean Relative Error(MRE), Nash Sutcliffe Coefficient(NS), Nash Sutcliffe Efficiency(NSE), BIAS, etc. among which NSE and MAR were found to be the most widely used error functions.

Seven Most Recent RPM


The seven most recent Runoff Prediction Models(RPM) are selected based on their accuracy, reliability, ease of use, and recentness. 

If you had found some interesting RPMs published within the last year(2021-22) share them by posting a comment to this post.

Thanks for reading,
@Merchandises or @Shop
Become my friend in Listy/Pearltrees/Twitter

Popular posts from this blog

Five most significant findings of the week related to water resources

"Sediment cores taken from the Southern Ocean dating back 23 million years are providing insight into how ancient methane escaping from the seafloor could have led to regional or global climate and environmental changes, according to a new study." Click here "Scientists analyzing one of the largest genomic datasets of plants have discovered how the first plants on Earth evolved the mechanisms used to control water and 'breathe' on land hundreds of millions of years ago. The study has important implications in understanding how to plant water transport systems have evolved and how these might adapt in the future in response to climate change." Click here "A new analysis of the River Ganges in West Bengal, India, highlights how wastewater flowing into the river impacts its water quality, and how that influences shifts with seasons and tides." Click here "MIT researchers have developed a solar-powered desalination system that is more efficient an

Five free statistics software that you can use in Water Resource Research

Statistics is an essential part of water resource research. But presently all the popular statistical software is expensive and for a student or individual researcher, it is nearly impossible to procure such software. For example, the cost of the most popular statistical software is as given below : Statista ($59 per month,billed annually) Sigma XL : $299.00 USD(Single Licence) IBM SPSS Statistics ($99 per user per month) JMP : $1200 Minitab : $1610.00 USD(Single User Annual Subscription) There are many other paid software programs that offer various statistical analyses, such as Origin Pro and Stata, but their prices vary depending on location, number of users, and other factors. That is why they ask you to request a quote, and upon receipt of your request, they will provide you with a quotation based on your specific requirement. An individual researcher, on the other hand, cannot afford such a high price. As a result, we must rely on grants or institutes to obtain this software.

Online Innternship Opportunity : Optimal Energy Allocation in Paper Industry by Nature Based Optimization Techniques

Summary • The paper industry is a profitable and essential sector for sustainable livelihood. • Unplanned power allocation among industry units is a major cause of industry losses. • Bio-inspired optimization techniques, such as the Moth Flame Optimization Technique (MFT), Water Cycle Optimization (WCO), and Fish Foraging Algorithm (FFA), are being used to identify optimal solutions. • These population-based algorithms consider every part of the search domain to find the optimal solution. • These techniques can help allocate power optimally and sustainably, ensuring no compromise in the quality of the output and expenditures are allocated for essential needs only. 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://b