Comparative analysis of machine learning techniques for predicting water main failures in the City of Kitchener

Omar, Abdelhady; Delnaz, Atefeh; Nik-Bakht, Mazdak

doi:10.1016/j.iintel.2023.100044

Cited by 3 publications

(6 citation statements)

References 27 publications

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“…The proposed analysis and the utilization of a custom Python solution with opensource technologies, including Flask as the web framework, produce a commitment to tailoring the technology to the specific needs of the city. The literature review provides a foundation for interpreting the digital twin results [6][7][8][9][10][11][12][13][14]. These studies underline the potential of citizen feedback data to inform urban planning and decision-making.…”

Section: Discussionmentioning

confidence: 99%

“…The paper on Flask-based digital twin applications contributes to understanding the technical architecture of the digital twin in Patras. The proposed frameworks in these papers highlight the versatility of Flask for developing smart city digital twins [6][7][8][9][10][11][12][13][14][15][16][17]. The integration of machine learning models in these frameworks mirrors the approach taken in the Patras digital twin, emphasizing the role of Flask in building scalable and intelligent urban management platforms.…”

Section: Discussionmentioning

confidence: 99%

“…This resonates with the digital twin's use of a random forest classifier for predictive modeling. The literature showcases a combination of digital twin technology and machine learning for enhancing public services [5,8,[12][13][14]22]. In a similar vein, the digital twin in Patras leverages machine learning within a Flask-based framework to optimize urban planning.…”

Section: Discussionmentioning

confidence: 99%

“…In our exploration of the intricate urban fabric, we focus on the neighborhood level, recognizing the pivotal roles of citizen report analytics, prediction, and digital twin technologies. This study integrates extract, transform, load/extract, load, transform (ETL/ELT) processes, artificial intelligence (AI) techniques, and a digital twin methodology [6][7][8][9][10][11][12][13][26][27][28] to interpret urban data streams emanating from citizen interactions with the city's coordinatebased problem mapping platform. The synergy of an interactive GeoDataFrame within the digital twin methodology creates dynamic entities, facilitating simulations across diverse scenarios.…”

Section: Methodsmentioning

confidence: 99%

“…In connection to this, the main goal of Ref. [13] is to develop a data-driven prediction model for water main failures in the City of Kitchener, Ontario, Canada, with the goal of reducing field inspections through proactive interventions. By limiting field inspections to pipes more likely to fail in the future, the study contributes to preventive maintenance practices and cost-effective updates of asset management plans.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level

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Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city’s coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of water–main failures and management of the associated risks using integrated predictive analytics approach

Delnaz,

Nasiri,

2024

Safety and Reliability

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The Use of Artificial Intelligence in the Assessment of User Routes in Shared Mobility Systems in Smart Cities

Kubik

2023

Smart Cities

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The use of artificial intelligence in solutions used in smart cities is becoming more and more popular. An example of the use of machine learning is the improvement of the management of shared mobility systems in terms of assessing the accuracy of user journeys. Due to the fact that vehicle-sharing systems are appearing in increasing numbers in city centers and outskirts, and the way vehicles are used is not controlled by operators in real mode, there is a need to fill this research gap. The article presents a built machine learning model, which is a supplement to existing research and is updated with new data from the existing system. The developed model is used to determine and assess the accuracy of trips made by users of shared mobility systems. In addition, an application was also created showing an example of using the model in practice. The aim of the article is therefore to indicate the possibility of correct identification of journeys with vehicles from shared mobility systems. Studies have shown that the prediction efficiency of the data generated by the model reached the level of 95% agreement. In addition, the research results indicate that it is possible to automate the process of evaluating journeys made in shared mobility systems. The application of the model in practice will facilitate management and, above all, it is open to further updates. The use of many machine learning models will allow solving many problems that will occur in an increasing number of smart cities.

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Comparative analysis of machine learning techniques for predicting water main failures in the City of Kitchener

Cited by 3 publications

References 27 publications

Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level

Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level

Prediction of water–main failures and management of the associated risks using integrated predictive analytics approach

The Use of Artificial Intelligence in the Assessment of User Routes in Shared Mobility Systems in Smart Cities

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