K-Nearest Neighbours Method as a Tool for Failure Rate Prediction

Kutyłowska, Małgorzata

doi:10.3311/ppci.10045

Cited by 12 publications

(11 citation statements)

References 15 publications

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“…The developed daily electricity consumption prediction model is validated through performance comparisons against a set of four widelyrecognized machine learning models namely, radial basis neural network ( ), support vector machines ( ), decision tree ( ) and Gaussian process ( ). More information about , , , and can be adopted from Gholami et al [21], He et al [22], El-Zahab et al [23] and Kutyłowska [24]. The performance comparisons are carried out using split validation based on mean absolute error ( ), mean absolute percentage error ( ) and rootmean squared error (…”

Section: Model Developmentmentioning

confidence: 99%

On the Implementation of Machine Learning Models for Emulating Daily Electricity Consumption in Hotel Facilities

Abdelkader¹,

Al-Sakkaf²,

Elshaboury³

et al. 2021

World Congress on Civil, Structural, and Environmental Engineering

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The increase in electricity consumption has negative effects on the environment, including greenhouse gas emissions, global warming, and rapid climate change. In an attempt to address this global problem, this research aims at developing machine learning models for the prediction of electricity consumption in hotels. The models are implemented using several techniques, including K-nearest neighbors, radial basis neural network, support vector machines, decision tree, and Gaussian process. The performance of the aforementioned models is evaluated by measuring the mean absolute error, mean absolute percentage error and root-mean squared error using split validation. Finally, two-tailed Student's t-tests are performed to evaluate the statistical significance level of prediction models. The results demonstrate that the K-nearest neighbors model accomplished the highest prediction performance yielding mean absolute error, mean absolute percentage error and root-mean squared error of 130.77, 276.45 and 4.79%, respectively. Support vector machines provided the lowest prediction accuracy accomplishing mean absolute error, mean absolute percentage error and root-mean squared error of 303.39, 382.78 and 11.98%, respectively.

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Section: Model Developmentmentioning

confidence: 99%

On the Implementation of Machine Learning Models for Emulating Daily Electricity Consumption in Hotel Facilities

Abdelkader¹,

Al-Sakkaf²,

Elshaboury³

et al. 2021

World Congress on Civil, Structural, and Environmental Engineering

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“…The set of data points will be measure and converted into a graph to see which training points it is moving towards. The set of data points which is called the estimation of k will be classified as the same category as the particular training point [19]. There are few benefits of using this algorithm.…”

Section: K-nearest Neighbour (Knn)mentioning

confidence: 99%

Data Mining and Personalization for Smart Travel Management

2020

jcr

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The technological advancement demands the tourism experience to be acquainted with the new concept entitled Smart Tourism. Consequently, the improvement of travel management is essential by introducing smarter technologies. In this paper, application of data mining and integration of personalization are proposed to enhance online travel management. The study investigates the problems existing in the current travel E-commerce websites and how these could be resolved through personalization. This research further enlightens the details of data mining which is applied to achieve personalization. Interviews and questionnaires are conducted for this research and their results are displayed and analyzed in this study. The results are discussed in detail and relating their responses to the idea of personalization in travel e-commerce website. Finally, a conclusion containing all the research findings gathered throughout the entire research including the authors' opinions and the limitation of the current study are presented to consolidate the research.

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“…water pipes failures or evaluating leakage potential [18][19][20][21][22]. These activities are very important in the aspect of water distribution quality and reliability, but they are not the only way to limit problems connected with pipes failures.…”

Section: A R C H I T E C T U R E C I V I L E N G I N E E R I N G E N V I R O N M E N Tmentioning

confidence: 99%

Analysis of the Width of Protection Zone Near a Water Supply Network

Iwanek

Suchorab

Sidorowicz

2019

Architecture, Civil Engineering, Environment

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A protection zone along water distribution network is one of the proposals to limit the negative effects of potential breakages of buried water pipes, connected with the creation of swallow holes, hollows or depressions [1, 2]. Water flowing out from a leaking pipe can wash out fine soil particles from a solid matrix and move them through soil pores causing suffosion. The phenomena of this kind are dangerous especially in urban areas with developed infrastructure. Empty spaces created incrementally under the soil surface can be hazardous for human health or property-accidents of cars falling in holes created after a water pipe failure are reported several times a year all over the world. The problem still exists because of two reasons. The first is that water supply pipes are usually located along roads, which are used by vehicles at all times, and also where the density of infrastructure is often the highest. The second reason is connected with the fact that water pipes failures and breakages occur randomly during the whole maintenance period of every water supply system in the world [3, 4]. Plenty of methods for leakage detection, evaluating the technical conditions of operating water pipes and risk management in water distribution systems have been reported in the literature in recent years [e.g. 5-11]. Modelling and computer simulations have become common tools for the analysis of pipe failures and leakages [12-17]. Moreover, mathematical approaches, such as fuzzy sets, artificial neural networks or k-nearest neighbours algorithm have been developed lately for predicting

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K-Nearest Neighbours Method as a Tool for Failure Rate Prediction

Cited by 12 publications

References 15 publications

On the Implementation of Machine Learning Models for Emulating Daily Electricity Consumption in Hotel Facilities

On the Implementation of Machine Learning Models for Emulating Daily Electricity Consumption in Hotel Facilities

Data Mining and Personalization for Smart Travel Management

Analysis of the Width of Protection Zone Near a Water Supply Network

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