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Environments
Special Issues
Data-Modelling Applications in Water System Management
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Data-Modelling Applications in Water System Management

A special issue of Environments (ISSN 2076-3298).

Deadline for manuscript submissions: closed (30 November 2016) | Viewed by 32007

Special Issue Editors

Prof. Dr. Luigi Berardi

E-Mail Website
Guest Editor
Department of Engineering and Geology, University “G. D’Annunzio” of Chieti Pescara, viale Pindaro 42, 65127 Pescara, Italy
Interests: data-driven modeling of environmental phenomena; evolutionary computing and hybrid evolutionary computing; multi-objective decision support tools; water distribution and sewer system analysis; optimization applied to management of water systems
Special Issues, Collections and Topics in MDPI journals
Dr. Daniele Laucelli

E-Mail Website
Guest Editor
Civil Engineering and Architecture Department Technical University of Bari v. E. Orabona 4, 70125 Bari, Italy
Interests: data modeling and soft-computing for environmental systems; management and planning of water distribution systems; analysis of leakage in water systems; hydroinformatics and decision support in the management of water systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water system management has a direct impact on natural and urban environments, covering a wide spectrum of field applications, ranging from watershed and groundwater management to natural and antropic water systems for water supply and wastwater harvesting. The complexity of many phenomena undelying such systems has motivated many researchers, in the last few decades, in exploiting data-driven modeling, including techniques such as artificial neural networks, rule-based models, or population-based strategies. On the one hand, such techniques permit the determination of the relationships between input and output field data using representative trainig sets. On the other hand, data-driven modeling is used to mine knowledge from data, thus, unveiling new relationships among the observed variables, which would be difficult to discover using physically-based aproaches.

This Special Issue aims at collecting different contributions in the area of water system management, where various data modeling techniques are applied for system analysis and decision support purposes. The main aim is to promote the interdisciplinary exchange of experiences and to provide stimuli for future research in data-driven modeling for water system management. This might include, though not be limited to, development and application of novel data-driven modeling for ad hoc water system managemnt purposes; comparisons among different data-modeling techniques applied to water system analysis and management; and application of data-driven modeling to support water system regulation.

Dr. Luigi Berardi
Dr. Daniele Laucelli
Guest Editor

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. Environments is an international peer-reviewed open access monthly 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 1800 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

  • Data-driven modeling for water supply and wastewater systems analysis and management
  • Data-driven modeling for groundwater and watershed analysis and management
  • Artificial intelligence, Machine learning, Data Mining
  • Decision support system for natural and anthropic water systems

Published Papers (5 papers)

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Editorial

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173 KiB  
Editorial
Data-Modelling Applications in Water System Management
by Daniele Laucelli and Luigi Berardi
Environments 2017, 4(3), 55; https://0-doi-org.brum.beds.ac.uk/10.3390/environments4030055 - 08 Aug 2017
Viewed by 3944
Abstract
Water system management has a direct impact on natural and urban environments, covering a wide spectrum of field applications, ranging from watershed and groundwater management to natural and anthropic systems for water supply and wastewater harvesting. [...] Full article
(This article belongs to the Special Issue Data-Modelling Applications in Water System Management)

Research

Jump to: Editorial

3017 KiB  
Article
Uncertainty Impact on Water Management Analysis of Open Water Reservoir
by Daniel Marton and Stanislav Paseka
Environments 2017, 4(1), 10; https://0-doi-org.brum.beds.ac.uk/10.3390/environments4010010 - 04 Feb 2017
Cited by 8 | Viewed by 6376
Abstract
The aim of this paper is to use a methodology to introduce uncertainty of hydrological and operational input data into mathematical models needed for the design and operation of reservoirs. The application of uncertainty to input data is calculated, with the reservoir volume [...] Read more.
The aim of this paper is to use a methodology to introduce uncertainty of hydrological and operational input data into mathematical models needed for the design and operation of reservoirs. The application of uncertainty to input data is calculated, with the reservoir volume being affected by these uncertainties. The values of outflows from the reservoir and hydrological reliability are equally affected. The simulation model of the reservoir behavior was used, which allows to evaluate the results of solutions and helps to reduce, for example, the cost of dam construction, the risk of poor design of reservoir volumes, future operational risk of failures and reduce water shortages during the operation of water reservoirs. The practical application is carried out on the water management analysis of a reservoir in the Czech Republic. It was found that uncertainty of storage volume with 100% reliability achieved ±4% to ±6% values and the subsequent reliability uncertainty is in the value interval of ±0.2% to ±0.3%. Full article
(This article belongs to the Special Issue Data-Modelling Applications in Water System Management)
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5041 KiB  
Article
Analysis and Modelling of Taste and Odour Events in a Shallow Subtropical Reservoir
by Edoardo Bertone and Kelvin O’Halloran
Environments 2016, 3(3), 22; https://0-doi-org.brum.beds.ac.uk/10.3390/environments3030022 - 19 Aug 2016
Cited by 13 | Viewed by 6038
Abstract
Understanding and predicting Taste and Odour events is as difficult as critical for drinking water treatment plants. Following a number of events in recent years, a comprehensive statistical analysis of data from Lake Tingalpa (Queensland, Australia) was conducted. Historical manual sampling data, as [...] Read more.
Understanding and predicting Taste and Odour events is as difficult as critical for drinking water treatment plants. Following a number of events in recent years, a comprehensive statistical analysis of data from Lake Tingalpa (Queensland, Australia) was conducted. Historical manual sampling data, as well as data remotely collected by a vertical profiler, were collected; regression analysis and self-organising maps were the used to determine correlations between Taste and Odour compounds and potential input variables. Results showed that the predominant Taste and Odour compound was geosmin. Although one of the main predictors was the occurrence of cyanobacteria blooms, it was noticed that the cyanobacteria species was also critical. Additionally, water temperature, reservoir volume and oxidised nitrogen availability, were key inputs determining the occurrence and magnitude of the geosmin peak events. Based on the results of the statistical analysis, a predictive regression model was developed to provide indications on the potential occurrence, and magnitude, of peaks in geosmin concentration. Additionally, it was found that the blue green algae probe of the lake’s vertical profiler has the potential to be used as one of the inputs for an automated geosmin early warning system. Full article
(This article belongs to the Special Issue Data-Modelling Applications in Water System Management)
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3909 KiB  
Article
Relating Water Quality and Age in Drinking Water Distribution Systems Using Self-Organising Maps
by E.J. Mirjam Blokker, William R. Furnass, John Machell, Stephen R. Mounce, Peter G. Schaap and Joby B. Boxall
Environments 2016, 3(2), 10; https://0-doi-org.brum.beds.ac.uk/10.3390/environments3020010 - 20 Apr 2016
Cited by 27 | Viewed by 7192
Abstract
Understanding and managing water quality in drinking water distribution system is essential for public health and wellbeing, but is challenging due to the number and complexity of interacting physical, chemical and biological processes occurring within vast, deteriorating pipe networks. In this paper we [...] Read more.
Understanding and managing water quality in drinking water distribution system is essential for public health and wellbeing, but is challenging due to the number and complexity of interacting physical, chemical and biological processes occurring within vast, deteriorating pipe networks. In this paper we explore the application of Self Organising Map techniques to derive such understanding from international data sets, demonstrating how multivariate, non-linear techniques can be used to identify relationships that are not discernible using univariate and/or linear analysis methods for drinking water quality. The paper reports on how various microbial parameters correlated with modelled water ages and were influenced by water temperatures in three drinking water distribution systems. Full article
(This article belongs to the Special Issue Data-Modelling Applications in Water System Management)
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4455 KiB  
Article
Estimating the Probability of Vegetation to Be Groundwater Dependent Based on the Evaluation of Tree Models
by Isabel C. Pérez Hoyos, Nir Y. Krakauer and Reza Khanbilvardi
Environments 2016, 3(2), 9; https://0-doi-org.brum.beds.ac.uk/10.3390/environments3020009 - 02 Apr 2016
Cited by 12 | Viewed by 7847
Abstract
Groundwater Dependent Ecosystems (GDEs) are increasingly threatened by humans’ rising demand for water resources. Consequently, it is imperative to identify the location of GDEs to protect them. This paper develops a methodology to identify the probability of an ecosystem to be groundwater dependent. [...] Read more.
Groundwater Dependent Ecosystems (GDEs) are increasingly threatened by humans’ rising demand for water resources. Consequently, it is imperative to identify the location of GDEs to protect them. This paper develops a methodology to identify the probability of an ecosystem to be groundwater dependent. Probabilities are obtained by modeling the relationship between the known locations of GDEs and factors influencing groundwater dependence, namely water table depth and climatic aridity index. Probabilities are derived for the state of Nevada, USA, using modeled water table depth and aridity index values obtained from the Global Aridity database. The model selected results from the performance comparison of classification trees (CT) and random forests (RF). Based on a threshold-independent accuracy measure, RF has a better ability to generate probability estimates. Considering a threshold that minimizes the misclassification rate for each model, RF also proves to be more accurate. Regarding training accuracy, performance measures such as accuracy, sensitivity, and specificity are higher for RF. For the test set, higher values of accuracy and kappa for CT highlight the fact that these measures are greatly affected by low prevalence. As shown for RF, the choice of the cutoff probability value has important consequences on model accuracy and the overall proportion of locations where GDEs are found. Full article
(This article belongs to the Special Issue Data-Modelling Applications in Water System Management)
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