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Journals
Smart Cities
Special Issues
Machine Learning and Big Data in Geosciences
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Machine Learning and Big Data in Geosciences

A special issue of Smart Cities (ISSN 2624-6511). This special issue belongs to the section "Smart Data".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 5938

Special Issue Editors

Prof. Dr. Isam Shahrour

E-Mail Website
Guest Editor
Civil and Geo-Environmental Laboratory, Lille University, 59650 Villeneuve d'Ascq, France
Interests: smart city; smart energy; smart water; smart infrastructures; civil engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongqiang Liu

E-Mail Website
Guest Editor
Norwegian Geotechnical Institute, PO Box 3930, Ullevaal Stadion, NO-0806 Oslo, Norway
Interests: geotechnical uncertainty quantification; inherent spatial variability of soils; bayesian updating of geotechnical systems; geohazard risk assessment; machine learning in geotechnics
Special Issues, Collections and Topics in MDPI journals
Dr. Hanbing Bian

E-Mail Website
Guest Editor
Civil and Geo-Environmental Laboratory, Lille University, 42 Rue Paul Duez, 59000 Lille, France
Interests: geotechnical engineering; numerical modelling; geo-data; risque assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The geosciences have been largely driven by the use of data. Recent scientific and professional developments in data acquisition in the geosciences and data analysis using big data and machine learning open new opportunities for an intensive use of data in geosciences. This Special Issue aims to share the latest research on the development and application of big data and machine learning methods in geosciences and related fields. We are pleased to invite you to submit a paper for this Special Issue titled “Big Data and Machine Learning in Geosciences”, which aims to share the latest scientific and professional developments and applications in the use of massive data in the geosciences and related fields, with a focus on the following issues:

  • Role of data science in solving traditional and emergent problems in geosciences.
  • Progresses in data collection in geoscience (remote sensing, smart sensors, open data, social media, and mobile applications).
  • Specificities and patterns of data in geosciences, data cleaning.
  • Combination of geoscience-based design methods with artificial intelligence methods (machine learning and deep learning).
  • Role of visualization and visual analytics in geosciences
  • Needs and perspectives for the use of data in geosciences.

Prof. Dr. Isam Shahrour
Dr. Zhongqiang Liu
Dr. Hanbing Bian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Smart Cities is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • machine learning
  • massive data
  • geosciences
  • monitoring
  • visualization

Published Papers (1 paper)

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Research

23 pages, 33864 KiB  
Article
Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems
by Neda Mashhadi, Isam Shahrour, Nivine Attoue, Jamal El Khattabi and Ammar Aljer
Smart Cities 2021, 4(4), 1293-1315; https://doi.org/10.3390/smartcities4040069 - 01 Oct 2021
Cited by 28 | Viewed by 5065
Abstract
This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring [...] Read more.
This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software; then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters. Full article
(This article belongs to the Special Issue Machine Learning and Big Data in Geosciences)
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