A programming tool for nonparametric system prediction using Partial Informational Correlation and Partial Weights

Sharma, Ashish; Mehrotra, R.; Li, Jingwan; Jha, Sanjeev

doi:10.1016/j.envsoft.2016.05.021

Cited by 41 publications

(26 citation statements)

References 22 publications

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“…For instance, the precipitation might be replaced by the QPF or local LCRA rain gauge data. In the future, other data preprocessing approaches such as partial information approach (Sharma and Mehrotra, ; Sharma et al ., ) could also be implemented to automatically choose the best input parameters for data‐driven models to improve reservoir inflow forecast.…”

Section: Discussionmentioning

confidence: 99%

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Zhao

Minsker

Salas

et al. 2017

J American Water Resour Assoc

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Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real-time decisions given that flood conditions change rapidly. This study's objective is to build real-time data-driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics-based model that estimates errors in the flow predictions from a physics-based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short-term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics-based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser-based Web application, enabling ease of use by both technical and nontechnical personnel.(KEY TERMS: flooding; data-driven model services; AzureML; reservoir inflow.)

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Section: Discussionmentioning

confidence: 99%

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Zhao

Minsker

Salas

et al. 2017

J American Water Resour Assoc

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“…The NPRED model was applied to predict the maximum storm burst fractions at the same duration as the downscaled future daily rainfall to be disaggregated using daily rainfall and temperature predictors. The experiments to predict storm burst fractions were based on open-source NPRED software for R [34], which uses partial information correlation (PIC) logic to detect predictors and partial weights (PW) to predict the response [34,35]. The NPRED tool was used without separating the data into seasonal segments for more precise predictions, as suggested by Fadhel et al [15].…”

Section: Methodsmentioning

confidence: 99%

An Uncertainty Investigation of RCM Downscaling Ratios in Nonstationary Extreme Rainfall IDF Curves

et al. 2018

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Designed for rainstorms and flooding, hydrosystems are largely based on local rainfall Intensity-Duration-Frequency (IDF) curves which include nonstationary components accounting for climate variability. IDF curves are commonly calculated using downscaling outputs from General Circulation Models (GCMs) or Regional Circulation Models (RCMs). However, the downscaling procedures used in most studies are based on one specific time scale (e.g., 1 h) and generally ignore scale-driven uncertainty. This study analyzes the uncertainties in IDF curves stemming from RCM downscaling ratios for four representative weather stations in the United Kingdom. We constructed a series of IDF curves using distribution-based scaling bias-correction technology and a statistical downscaling method to explore the scale-driven uncertainty of IDF curves. The results revealed considerable scale-induced uncertainty of IDF curves for short durations and long return periods; however, there was no clear correlation with the mean storm intensity of the IDF curves of different RCM ensemble members for each duration and return period. The scale-driven uncertainty of IDF curves, which may be propagated or enhanced through hydrometeorological applications, is critical and cannot be ignored in the hydrosystem design process; therefore, a multi-scale method to derive IDF curves must be developed.

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“…Readers are referred to (Mehrotra and Sharma, 2006; Sharma and Mehrotra, 2014;Sharma et al, 2016) for further details on the nonparametric modelling framework used in this work.…”

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confidence: 99%

“…The sample 15 standard deviations are used to standardise the predictor variables to make them independent of their measurement scale, while the partial weights allow elimination of a predictor variable if not relevant to the prediction being made. Readers are referred to Sharma and Mehrotra (2014) for the informational theory rationale that allows for the estimation of these partial weights, and the NPRED, R package ( (Sharma et al, 2016), downloadable from http://www.hydrology.unsw.edu.au/download/software/npred) that enables their estimation for any sample data set. …”

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confidence: 99%

Estimating Radar Precipitation in Cold Climates: The role of Air Temperature within a Nonparametric Framework

Sivasubramaniam¹,

Sharma²,

Alfredsen³

2018

Preprint

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Abstract. In cold climates, the form of precipitation (snow or rain or a mixture of snow and rain) results in uncertainty in radar precipitation estimation. Estimation often proceeds without distinguishing the state of precipitation which is known to impact the radar reflectivity -precipitation relationship. In the present study, we investigate the use of air temperature within a nonparametric predictive framework to improve radar precipitation estimation for cold climates. Compared to radar reflectivity -gauge relationships, this approach uses gauge precipitation and air temperature observations to estimate radar precipitation. A 5 nonparametric predictive model is constructed with radar precipitation rate and air temperature as predictor variables, and gauge precipitation as an observed response using a k-nearest neighbour (k-nn) regression estimator. The relative importance of the two predictors is ascertained using an information theory-based rationale. Four years (2011-2015) of hourly radar precipitation rate from the Norwegian national radar network over the Oslo region, hourly gauged precipitation from 68 gauges, and gridded observational air temperature were used to formulate the predictive model and hence make our investigation possible. Gauged 10 precipitation data were corrected for wind induced catch error before using them as true observed response. The predictive model with air temperature as an added covariate reduces root mean squared error (RMSE) by up to 15 % compared to the model that uses radar precipitation rate as the sole predictor. More than 80 % of gauge locations in the study area showed improvement with the new method. Further, the associated impact of air temperature became insignificant at more than 85 % of gauge locations when the temperature was above 10• C, which indicates that the partial dependence of precipitation on air 15 temperature is most important for colder climates alone.

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A programming tool for nonparametric system prediction using Partial Informational Correlation and Partial Weights

Cited by 41 publications

References 22 publications

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

An Uncertainty Investigation of RCM Downscaling Ratios in Nonstationary Extreme Rainfall IDF Curves

Estimating Radar Precipitation in Cold Climates: The role of Air Temperature within a Nonparametric Framework

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