A Comparative Assessment of Variable Selection Methods in Urban Water Demand Forecasting

Haque, Mahmudul; Rahman, Ataur; Hagare, Dharmappa; Chowdhury, R. K.

doi:10.3390/w10040419

Cited by 40 publications

(24 citation statements)

References 39 publications

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“…Several methods are available to identify appropriate combinations of predictor variables (indicators) for the regression models, such as forward selection, stepwise selection, backward elimination, and principle component analysis (Haque et al., 2018). This study uses a simplified forward selection process, iteratively increasing the number of predictor variables in the model.…”

Section: Methodsmentioning

confidence: 99%

Selecting Indicators and Optimizing Decision Rules for Long‐Term Water Resources Planning

Murgatroyd

2021

Water Resources Research

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Water planners must address these issues through improved water conservation, resources coordination, and capacity expansion projects. However, uncertainties arising from climate change, population growth, changing patterns of water demand and consumption, technological advancements, and regulatory reform continue to complicate water resources planning problems (Brown et al., 2015;Wilby & Dessai, 2010). Previous research has sought to overcome these issues by identifying long-term and short-term water system investment portfolios (Bor-

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Section: Methodsmentioning

confidence: 99%

Selecting Indicators and Optimizing Decision Rules for Long‐Term Water Resources Planning

Murgatroyd

2021

Water Resources Research

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“…The research progressed in three main stages: 1. determining explanatory variables, 2. building the models, 3. forecasting, and evaluating the models. A set of explanatory variables was determined using Principal Component Analysis (PCA) based on the procedure presented by M. M. Haque et al [7]. To determine the explanatory variables, data from 2011-2016 were used.…”

Section: Fig 1 Location Of Research Objects 3 Materials and Methodsmentioning

confidence: 99%

“…According to the procedure presented by M. M. Haque et al [7], the set of explanatory variables was determined based on the following parameters: -selecting the variables with the highest correlation coefficients for a given component (variable loadings).…”

Section: Fig 1 Location Of Research Objects 3 Materials and Methodsmentioning

confidence: 99%

Forecasting water consumption in towns of different sizes

2019

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The aim of the work was to compare water consumption forecasting in two towns of different sizes. The objects of research were the town of Toruń and the town of Żnin in central Poland. Two models were built for each. The models were constructed using the multiple regression method. In constructing the models, explanatory variables determined by Principal Component Analysis (PCA) were used. The set of explanatory variables identified to construct each individual model differed. The models for Toruń obtained better forecast quality assessment criteria values. This was mainly due to the water supply system in the small town (Żnin) being less resilient to sudden, short-term changes in consumers’ water use. At the same time, the importance of the location of the meteorological stations from which data was taken to build the model was emphasised.

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“…where p is the probability of an event (Y=1), βi is the estimated coefficient and Xi is the explanatory variables ( i=1,2,…,k ). According to Haque et al (2018), there are four types of model selection in logistic regression which are known as Enter, Stepwise, Forward and Backward.…”

Section: Logistic Regressionmentioning

confidence: 99%

Prediction of Diabetic Retinopathy Among Type Ii Diabetic Patients Using Data Mining Techniques

Khairudin¹,

Razak²,

Rahman³

et al. 2020

MJoC

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Diabetic retinopathy is one of the leading causes of visual disability and blindness worldwide. It is estimated that 4.8% out of 37 million cases of blindness were due to diabetic retinopathy, globally. It affects patients suffering from prolonged diabetes, which probably results in permanent blindness. The earliest symptoms surfaced when the patients have vision problems. Therefore, regular eyes examination and early intervention normally controls this disease. Many studies for early intervention and prevention of diabetic retinopathy uses various predictive models. The booming of database and digital storage technology creates an abundance of health records. Thus, data mining techniques helps uncover meaningful patterns while attending to sensitivity health record issues. Hence, this study took the data mining approach in predicting the presence of diabetic retinopathy narrowing to only Type II diabetic patients as well as to determine the risk factors that contribute to the presence of diabetic retinopathy. The data mining models selected for this study is the Logistic Regression, Decision Tree and Artificial Neural Network. The dataset of 361 Type II diabetic patients from Ophthalmology Clinic, UiTM Medical Specialist Centre were selected between January 2014 to December 2018, consists of 17 variables. The result shows that the Logistic Regression using Forward selection method model is the best model it had the highest sensitivity (Sen=50.0%), specificity (Spe=79.03%) and accuracy rate (Acc=66.36%) on the validation dataset compared to other Logistic Regression selection options. Meanwhile among the Decision Tree models, DT using Gini is the best model. Logistic Regression (Forward) and Decision Tree (Gini) were then compared with Artificial Neural Network model (Sen=56.25%, Spe=70.97%, Acc=64.55%). The results demonstrated that Logistic Regression using Forward selection method was the best model to predict the presence of diabetic retinopathy among the Type II diabetic patients compared to other models. The significant risk factors associated with the presence of the diabetic retinopathy obtained are duration of diabetes, HbA1C level, diabetic foot ulcer, nephropathy, and neuropathy.

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A Comparative Assessment of Variable Selection Methods in Urban Water Demand Forecasting

Cited by 40 publications

References 39 publications

Selecting Indicators and Optimizing Decision Rules for Long‐Term Water Resources Planning

Selecting Indicators and Optimizing Decision Rules for Long‐Term Water Resources Planning

Forecasting water consumption in towns of different sizes

Prediction of Diabetic Retinopathy Among Type Ii Diabetic Patients Using Data Mining Techniques

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