Predicting Ice Jams With Discriminant Function Analysis

2002 ◽  
Author(s):  
Kathleen D. White ◽  
Steven F. Daly
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Prediction Models ◽  
Function Analysis ◽  
Quantitative Information ◽  
Prediction Methods ◽  
Deterministic Models ◽  
Ice Jams ◽  
Variable Threshold ◽  
Ice Jam

Breakup ice jam prediction methods are desirable to provide early warning and allow rapid, effective ice jam mitigation due to the suddenness with which breakup jams and related flooding occur. However, prediction models are limited to empirical or stochastic models rather than deterministic models because of the difficulties in using deterministic models to forecast the formation of breakup ice jams. Existing ice jam prediction methods range from empirical single-variable threshold-type analyses to statistical methods such as logistic regression and discriminant function analysis. Empirical methods are highly site-specific and tend to over predict jam occurrence. In addition, existing models do not provide quantitative information regarding the risk of errors in prediction, which limits their usefulness in emergency situations. In this paper, existing methods are reviewed and a three-step process to predict breakup ice jams is proposed.

Predicting Ice Jams With Neural Networks

2002 ◽  
Author(s):  
Darrell D. Massie ◽  
Kathleen D. White ◽  
Steven F. Daly ◽  
Regan McDonald
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Function Analysis ◽  
Analytical Description ◽  
Ice Jams ◽  
Variable Threshold ◽  
Ice Jam ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Neural Network Input

One of the most difficult problems facing hydraulicians is the development of a method that predicts the formation of breakup ice jams. Because of the suddenness with which breakup jams and related flooding occur, prediction methods are desirable to provide early warning and allow rapid, effective ice jam mitigation. Breakup ice jam prediction models are presently limited due to the lack of an analytical description of the complex physical processes, and range from empirical single-variable threshold-type analyses to statistical methods such as logistic regression and discriminant function analysis. In this study, a neural network method is used to predict breakup ice jams at Oil City, PA. Discussion of how the neural network input vector was determined and the methods used to appropriately account for the relatively low occurrence of jams are addressed. The neural network prediction proved to be more accurate than other methods attempted at this site.

Predicting Public Procurement Irregularity: An Application of Neural Networks

2018 ◽  
Vol 15 (1) ◽  
pp. 141-154 ◽  
Author(s):  
Ting Sun ◽  
Leonardo J. Sales
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Logistic Regression ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Prediction Models ◽  
Function Analysis ◽  
Public Procurement ◽  
Predictive Performance ◽  
Z Scores

ABSTRACT Using the data describing the characteristics of contractors provided by the Comptroller General of the Union, Brazil (CGU), this paper mainly implements two artificial neural networks, traditional neural network (TNN) and deep neural network (DNN), to develop prediction models of public procurement irregularities designed for the initial screening of contractors. This is the first application of DNN in the context of government auditing. To examine the effectiveness of DNN, the authors compare its predictive performance to TNN and two other algorithms (logistic regression and discriminant function analysis) and find that DNN significantly outperforms TNN and other algorithms in terms of accuracy, precision, F-scores, AUC, and other metrics, as suggested by the high Z-scores of the Z-tests. Although TNN has a higher recall than DNN, the difference of recall between TNN and DNN is insignificant. Logistic regression and discriminant function analysis achieve the highest recall scores, but their Z-scores are much lower than those of other metrics. Therefore, DNN generally performs more accurately than other approaches and meets the requirement of the CGU for an early alarm system.

Review of prediction methods for breakup ice jams

2003 ◽  
Vol 30 (1) ◽  
pp. 89-100 ◽  
Author(s):  
Kathleen D White
Keyword(s):  
Open Water ◽  
Flood Damage ◽  
Water Levels ◽  
Water Flooding ◽  
Prediction Methods ◽  
Deterministic Models ◽  
Flood Prediction ◽  
Ice Jams ◽  
Ice Jam ◽  
Flood Warning

Breakup ice jams often occur suddenly, with little warning. Severe flooding or ice-related damage can result from rapid rises in upstream water levels associated with breakup ice jams. Breakup jam prediction methods that can be used to increase response time are desirable to minimize flood damage, including potential loss of life. A variety of hydrologic and hydraulic models exist to predict open-water flooding, whether resulting from rainfall, snowmelt, or catastrophic events such as dam breaches. However, breakup ice jams result from a complex series of physical processes that cannot currently be described with analytical or deterministic models, hindering the development of prediction methods. Those which do exist are highly site specific and range from simple empirical models to an artificial intelligence formulation. To date, no one model exhibits a clear advantage over the others. This paper provides examples of existing breakup ice jam prediction methods and discusses their potential advantages and disadvantages.Key words: ice jam, breakup ice jam, flood prediction, flood warning, ice jam mitigation.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

scholarly journals Description of Echolocation Call Parameters for Urban Bats in Vietnam as a Step Towards a More Integrated Acoustic Monitoring of Urban Wildlife in Southeast Asia

Diversity ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Long Kim Pham ◽  
Bang Van Tran ◽  
Quy Tan Le ◽  
Trung Thanh Nguyen ◽  
Christian C. Voigt
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Quantitative Description ◽  
Southeast Asian ◽  
Asian Countries ◽  
Echolocation Call ◽  
Echolocation Calls ◽  
Pilot Work ◽  
Southeast Asian Countries

This study is the first step towards more systematic monitoring of urban bat fauna in Vietnam and other Southeast Asian countries by collecting bat echolocation call parameters in Ho Chi Minh and Tra Vinh cities. We captured urban bats and then recorded echolocation calls after releasing in a tent. Additional bat’s echolocation calls from the free-flying bats were recorded at the site where we captured bat. We used the obtained echolocation call parameters for a discriminant function analysis to test the accuracy of classifying these species based on their echolocation call parameters. Data from this pilot work revealed a low level of diversity for the studied bat assemblages. Additionally, the discriminant function analysis successfully classified bats to four bat species with an accuracy of >87.4%. On average, species assignments were correct for all calls from Taphozous melanopogon (100% success rate), for 70% of calls from Pipistrellus javanicus, for 80.8% of calls from Myotis hasseltii and 67.3% of calls from Scotophilus kuhlii. Our study comprises the first quantitative description of echolocation call parameters for urban bats of Vietnam. The success in classifying urban bats based on their echolocation call parameters provides a promising baseline for monitoring the effect of urbanization on bat assemblages in Vietnam and potentially also other Southeast Asian countries.

Mineralogy of Egyptian Bentonitic Clays I: Discriminant Function Analysis

2012 ◽  
Vol 60 (4) ◽  
pp. 387-404 ◽  
Author(s):  
Mohamed Agha ◽  
Ray E. Ferrell ◽  
George F. Hart
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis

Using discriminant function analysis to identify Holocene tephras based on magnetite composition: a case study from the Sunwapta Pass area, Jasper National Park

1986 ◽  
Vol 23 (6) ◽  
pp. 804-812 ◽  
Author(s):  
A. B. Beaudoin ◽  
R. H. King
Keyword(s):  
National Parks ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
National Park ◽  
Function Analysis ◽  
Classification Schemes ◽  
Jasper National Park ◽  
Multivariate Method ◽  
Reference Samples

The magnetite composition from three sets of samples of Mazama, St. Helens set Y, and Bridge River tephras from Jasper and Banff national parks are used to test whether discriminant function analysis can unambiguously distinguish these tephras. The multivariate method is found to be very sensitive to the change in reference samples. St. Helens set Y tephra is clearly distinguished. However, discrimination between Mazama and Bridge River tephras is less distinct. A set of unknown tephras from the Sunwapta Pass area was used to test the classification schemes. Unknown tephras are assigned to different tephra types depending on which reference tephra set is used in the discriminant function analysis.

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