Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

Smolak, Kamil; Kasieczka, Barbara; Fiałkiewicz, Wiesław; Rohm, Witold; Siła-Nowicka, Katarzyna; Kopańczyk, Katarzyna

doi:10.1080/1573062x.2020.1734947

Cited by 41 publications

(19 citation statements)

References 33 publications

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“…Regarding works related to water consumption data and human mobility, Smolak et al [18], [19] compares several ML and statistical models to predict the water usage based on human mobility data. The main aim of this work is the water demand forecasting of a particular area, and they actually concluded that the use of this mobility data is correlated to the water demand and this it benefits its prediction.…”

Section: Related Workmentioning

confidence: 99%

“…The human displacements are defined at a region level in an aggregated form whereas the water consumption data are defined at a much finer granularity as it is extracted directly from smart meters. Although water consumption and human-flow data have already been explored together in several prediction problems [18], [19], it is worth mentioning that their goal was the prediction of the water consumption of a population instead of its movement activity as in our proposal.…”

Section: Introductionmentioning

confidence: 99%

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Human Mobility Prediction With Region-Based Flows and Water Consumption

et al. 2021

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We are witnessing an increasing need to accurately measure people's mobility as it has become an instrumental factor for the development of innovative services in multiple domains. In this context, several ICT solutions have relied on location-based technologies such as GPS, WiFi or Bluetooth to track individuals movements. However, these technologies are limited by the privacy restrictions of data providers. In this paper we propose a methodology to robustly predict citizens' mobility patterns based on heterogeneous data from different sources. Particularly, our methodology focuses on a human mobility predictor based on a low-resolution mobility dataset and the use of water consumption data as a facilitator of this prediction task. As a result, this work explores whether the water consumption within a geographical region can reveal human activity patterns relevant from the point of view of the mobility mining discipline. This approach has been tested in a residential area near Madrid (Spain) obtaining quite promising results.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Mobility Prediction With Region-Based Flows and Water Consumption

et al. 2021

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“…Reliable monitoring of human mobility, referring to the movement of human beings (individuals as well as groups) in space and time, plays a fundamental role in a variety of fields, such as tourist management [ 1 , 2 ], migration [ 3 , 4 ], urban planning [ 5 – 7 ], demand forecasting [ 8 , 9 ], disaster management [ 10 , 11 ], and epidemic modelling [ 12 – 14 ], to name a few. The ongoing COVID-19 pandemic (as of the time of writing) uniquely highlights the necessity of human mobility monitoring in a rapid and comprehensive manner.…”

Section: Introductionmentioning

confidence: 99%

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

Huang

et al. 2021

PLoS ONE

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In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics.

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“…The proliferation of mobile devices has a significant impact on studying human mobility in areas such as disease spread modelling 1 , utility management 2 , and urban planning 3 . The majority of these applications is based on the regularity and predictability of the movement of individuals 4 , helping to understand the human behaviour underlying mobility.…”

Section: Introductionmentioning

confidence: 99%

The impact of human mobility data scales and processing on movement predictability

Smolak

Siła-Nowicka

Delvenne

et al. 2021

Sci Rep

Self Cite

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Predictability of human movement is a theoretical upper bound for the accuracy of movement prediction models, which serves as a reference value showing how regular a dataset is and to what extent mobility can be predicted. Over the years, the predictability of various human mobility datasets was found to vary when estimated for differently processed datasets. Although attempts at the explanation of this variability have been made, the extent of these experiments was limited. In this study, we use high-precision movement trajectories of individuals to analyse how the way we represent the movement impacts its predictability and thus, the outcomes of analyses made on these data. We adopt a number of methods used in the last 11 years of research on human mobility and apply them to a wide range of spatio-temporal data scales, thoroughly analysing changes in predictability and produced data. We find that spatio-temporal resolution and data processing methods have a large impact on the predictability as well as geometrical and numerical properties of human mobility data, and we present their nonlinear dependencies.

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Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

Abstract: (2020) Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models,

Cited by 41 publications

References 33 publications

Human Mobility Prediction With Region-Based Flows and Water Consumption

Human Mobility Prediction With Region-Based Flows and Water Consumption

ODT FLOW: Extracting, analyzing, and sharing multi-source multi-scale human mobility

The impact of human mobility data scales and processing on movement predictability

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