Smart City Application and Analysis: Real-time Urban Drainage Monitoring by IoT Sensors: A Case Study of Hong Kong

Keung, K. L.; Lee, C. K. M.; Ng, Ka Man; Yeung, Ching-Chi

doi:10.1109/ieem.2018.8607303

Cited by 45 publications

(15 citation statements)

References 3 publications

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“…e plot of the values associated with the pressure at normal condition is shown in Figure 8. e input dataset values of pressure at the normal condition are fed to the R studio; as the forecasting process requires statistical data, the R studio converts the input data into a time-series data with the time-series function and the plot of the time-series dataset values associated with the pressure at the normal condition with respect to the time [42].…”

Section: Forecasting Process-normal Conditionmentioning

confidence: 99%

Prediction of Fault Occurrences in Smart City Water Distribution System Using Time-Series Forecasting Algorithm

Chenniappan

Gnanavel

Gunasekaran

et al. 2022

Mathematical Problems in Engineering

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The proposed research work is focused on forecasting the future requirements of water supply based on the current requirement of water and also identifying the possibility of occurrences of cracks and leaks using the ARIMA (autoregressive integrated moving average) model. The experiments were conducted using real-time experimental hardware. The pressure data obtained and their p -value is less than 0.05, which represents the stability of the data in the ARIMA model. The forecasted pressure data range between 0.451379 N/m2 and 2.022273 N/m2. The frequency of the forecasted pressure ranges between 1.706869 N/m2 and 3.065836 N/m2 (maximum peak) and −0.81046 N/m2 and 1.042164 N/m2 (minimum peak). Forecasted data of pressure at damaged condition lie between 2.880788 N/m2 and 3.29797 N/m2 and frequency ranges between 4.866227 N/m2 and 5.664348 N/m2. Similarly, future forecasted data of water requirement for the next 1 year range between 614.6292 (liters/week) and 620.0099 (liters/week), the frequency of the forecast value with maximum ranging from 617.0086 (liters/week) to 628.5465 (liters/week), and the minimum peaks ranging from 611.0967 (liters/week) to 612.2914 (liters/week). The above data are for a single water distribution pipeline.

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Section: Forecasting Process-normal Conditionmentioning

confidence: 99%

Prediction of Fault Occurrences in Smart City Water Distribution System Using Time-Series Forecasting Algorithm

Chenniappan

Gnanavel

Gunasekaran

et al. 2022

Mathematical Problems in Engineering

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“…Since the last decade, digital advertising has become much more critical than before as it is more affordable and can reach larger audiences. 5G networking is being adopted worldwide increasingly so the access speed of digital content can be instantaneous with the aid of the Internet of Things ( Keung et al, 2018 , 2020 , 2021 , 2022a , b ; Lee et al, 2018b ; Liu et al, 2019 ; Li et al, 2021b , c , d ; Xia et al, 2021 ; Zheng et al, 2021 ; Fan et al, 2022 ; Zhang et al, 2022 ). DCM aims to build a close connection with customers through continuous conversation to convince their leads over time.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Effectiveness of Digital Content Marketing Under Mixed Reality Training Platform on the Online Purchase Intention

Li¹,

Chan

Chow

et al. 2022

Front. Psychol.

Self Cite

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The purpose of this research is to investigate the effectiveness of Digital Content Marketing (DCM) on a Mixed Reality (MR) training platform environment with the consideration of online purchase intention (OPI) through social media. E-commerce today encounters several common issues that cause customers to have reservations to purchase online. With the absence of physical contact points, customers often perceive more risks when making purchase decisions. Furthermore, online retailers often find it hard to engage customers and develop long-term relationships. In this research, a Structural Equation Model (SEM) is proposed to examine the efficacy of DCM from both immediate and long-term OPI. The results examine whether adopting DCM on an MR training platform environment through social media brings positive results in OPI. Empirical research was carried out through online questionnaires collected in 2021 and 2022. A total of 374 questionnaires were qualified for data analysis in this study, conducted with IBM SPSS and AMOS. The results imply that DCM is critical to stimulating both immediate and long-term OPI. The immediate OPI is positively affected by increasing perceived value through MR in DCM. Regarding the long-term OPI, increased customer engagement with DCM under MR environment can cultivate brand trust and significantly affect the long-term OPI.

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“…Existing solutions to this problem usually rely on a single data source such as river levels (Le et al 2019;Mioc et al 2011) and drainage system sensors (Keung et al 2018). Such systems predict sensor values in the future and rarely use these predictions as an input to a physics-based model, or indirectly identify which areas are most badly affected.…”

Section: Introductionmentioning

confidence: 99%

Using Multimodal Data and AI to Dynamically Map Flood Risk

Bryan-Smith

2022

AAAI

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Classical measurements and modelling that underpin present flood warning and alert systems are based on fixed and spatially restricted static sensor networks. Computationally expensive physics-based simulations are often used that can't react in real-time to changes in environmental conditions. We want to explore contemporary artificial intelligence (AI) for predicting flood risk in real time by using a diverse range of data sources. By combining heterogeneous data sources, we aim to nowcast rapidly changing flood conditions and gain a grater understanding of urgent humanitarian needs.

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Smart City Application and Analysis: Real-time Urban Drainage Monitoring by IoT Sensors: A Case Study of Hong Kong

Cited by 45 publications

References 3 publications

Prediction of Fault Occurrences in Smart City Water Distribution System Using Time-Series Forecasting Algorithm

Prediction of Fault Occurrences in Smart City Water Distribution System Using Time-Series Forecasting Algorithm

Evaluating the Effectiveness of Digital Content Marketing Under Mixed Reality Training Platform on the Online Purchase Intention

Using Multimodal Data and AI to Dynamically Map Flood Risk

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