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Water
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Soft Computing for Water and Aquatic Resource Management
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Soft Computing for Water and Aquatic Resource Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 10092

Special Issue Editors

Prof. Dr. Inmaculada Pulido-Calvo

E-Mail Website
Guest Editor
Dpto. de Ciencias Agroforestales, Escuela Técnica Superior de Ingeniería, Universidad de Huelva, 21007 Huelva, Spain
Interests: fluid mechanics; hydraulic engineering; water resources management; water distribution network; heuristic model
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Juan Carlos Gutiérrez-Estrada

E-Mail Website
Guest Editor
Dpto. de Ciencias Agroforestales, Escuela Técnica Superior de Ingeniería, Universidad de Huelva, 21007 Huelva, Spain
Interests: environmental modeling; aquatic ecology; freshwater fishes; fuzzy systems; artificial neural networks

Special Issue Information

Dear Colleagues,

An integrated water resource management requires the establishment of coordinated governance guidelines that guarantee the compatibility of different consumptive and non-consumptive water uses, the sustainability of aquatic ecosystems, and economic and social welfare. This holistic approach implies basins management strategies that must include and implement a broad knowledge of ecosystem components involving climatology, geomorphology, hydrological and hydraulic engineering, water quantity and quality, and aquatic vegetation and fauna.

To achieve this objective, the consideration of emerging technologies that allow spatial and temporal integration of a high quantity of data of different nature to achieve effective and dynamic solutions according to environmental conditions is essential. In this context, soft computing techniques, which allows models and control complex systems characterized by high levels of uncertainty, can support and encourage adaptative strategies for water and/or aquatic resource management.

In this Special Issue, original research and review contributions related with advanced applications of soft computing techniques for water and/or aquatic resources management are highly welcome.

Prof. Dr. Inmaculada Pulido-Calvo
Prof. Dr. Juan Carlos Gutiérrez-Estrada
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. Water 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 2600 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

  • Hydraulic and hydrologic engineering
  • Irrigation engineering
  • Aquaculture engineering
  • Environmental modeling
  • Aquatic ecology
  • Freshwater fauna
  • Evolutionary algorithms and genetic programming
  • Neural net systems
  • Fuzzy systems
  • Expert systems

Published Papers (3 papers)

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Research

14 pages, 2066 KiB  
Article
Developing Pseudo Continuous Pedotransfer Functions for International Soils Measured with the Evaporation Method and the HYPROP System: II. The Soil Hydraulic Conductivity Curve
by Amninder Singh, Amir Haghverdi, Hasan Sabri Öztürk and Wolfgang Durner
Water 2021, 13(6), 878; https://doi.org/10.3390/w13060878 - 23 Mar 2021
Cited by 7 | Viewed by 3232
Abstract
Direct measurement of unsaturated hydraulic parameters is costly and time-consuming. Pedotransfer functions (PTFs) are typically developed to estimate soil hydraulic properties from readily available soil attributes. For the first time, in this study, we developed PTFs to estimate the soil hydraulic conductivity (log( [...] Read more.
Direct measurement of unsaturated hydraulic parameters is costly and time-consuming. Pedotransfer functions (PTFs) are typically developed to estimate soil hydraulic properties from readily available soil attributes. For the first time, in this study, we developed PTFs to estimate the soil hydraulic conductivity (log(K)) directly from measured data. We adopted the pseudo continuous neural network PTF (PCNN-PTF) approach and assessed its accuracy and reliability using two independent data sets with hydraulic conductivity measured via the evaporation method. The primary data set contained 150 international soils (6963 measured data pairs), and the second dataset consisted of 79 repacked Turkish soil samples (1340 measured data pairs). Four models with different combinations of the input attributes, including soil texture (sand, silt, clay), bulk density (BD), and organic matter content (SOM), were developed. The best performing international (root mean square error, RMSE = 0.520) and local (RMSE = 0.317) PTFs only had soil texture information as inputs when developed and tested using the same data set to estimate log(K). However, adding BD and SOM as input parameters increased the reliability of the international PCNN-PTFs when the Turkish data set was used as the test data set. We observed an overall improvement in the performance of PTFs with the increasing number of data points per soil textural class. The PCNN-PTFs consistently performed high across tension ranges when developed and tested using the international data set. Incorporating the Turkish data set into PTF development substantially improved the accuracy of the PTFs (on average close to 60% reduction in RMSE). Consequently, we recommend integrating local HYPROPTM (Hydraulic Property Analyzer, Meter Group Inc., USA) data sets into the international data set used in this study and retraining the PCNN-PTFs to enhance their performance for that specific region. Full article
(This article belongs to the Special Issue Soft Computing for Water and Aquatic Resource Management)
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11 pages, 2100 KiB  
Article
A Computer Program to Support the Selection of Turbines to Recover Unused Energy at Hydraulic Networks
by Ángel Mariano Rodríguez-Pérez, Inmaculada Pulido-Calvo and Pablo Cáceres-Ramos
Water 2021, 13(4), 467; https://doi.org/10.3390/w13040467 - 11 Feb 2021
Cited by 6 | Viewed by 2812
Abstract
For this paper, a computer program was designed and developed to calculate which turbines could be placed in a water distribution system considering the hydraulic constraints. The aforementioned turbines are placed in locations where we have unused hydraulic energy, i.e., when this energy [...] Read more.
For this paper, a computer program was designed and developed to calculate which turbines could be placed in a water distribution system considering the hydraulic constraints. The aforementioned turbines are placed in locations where we have unused hydraulic energy, i.e., when this energy is dissipated by a regulating valve. In our case, what we do is place a turbine to make use of that excess energy. Once the data has been entered into the program, it provides the type or types of turbines that can be placed in each location, what power these turbines would be, and how much they would generate annually. The program offers us two calculation options. In the first, and simpler, one, it would be done using the net head at the location where the turbine is to be placed. For this option, it would only be necessary to introduce the flow rate, the net head, and the hours that the turbine will be in operation to perform the calculation. The second option would be in the case where we did not have the net head, and, instead, we had the gross head. In this case, we have to calculate the head losses. Normally, this would be the most used option because there are usually no pressure drops. To perform the calculation, in this case, it is necessary to know, apart from what is mentioned in the first option, the characteristics of the pipe (diameter, length, and material). Full article
(This article belongs to the Special Issue Soft Computing for Water and Aquatic Resource Management)
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17 pages, 2819 KiB  
Article
Developing Pseudo Continuous Pedotransfer Functions for International Soils Measured with the Evaporation Method and the HYPROP System: I. The Soil Water Retention Curve
by Amninder Singh, Amir Haghverdi, Hasan Sabri Öztürk and Wolfgang Durner
Water 2020, 12(12), 3425; https://doi.org/10.3390/w12123425 - 06 Dec 2020
Cited by 10 | Viewed by 3305
Abstract
Direct measurements of soil hydraulic properties are time-consuming, challenging, and often expensive. Therefore, their indirect estimation via pedotransfer functions (PTFs) based on easily collected properties like soil texture, bulk density, and organic matter content is desirable. This study was carried out to assess [...] Read more.
Direct measurements of soil hydraulic properties are time-consuming, challenging, and often expensive. Therefore, their indirect estimation via pedotransfer functions (PTFs) based on easily collected properties like soil texture, bulk density, and organic matter content is desirable. This study was carried out to assess the accuracy of the pseudo continuous neural network PTF (PCNN-PTF) approach for estimating the soil water retention curve of 153 international soils (a total of 12,654 measured water retention pairs) measured via the evaporation method. In addition, an independent data set from Turkey (79 soil samples with 7729 measured data pairs) was used to evaluate the reliability of the PCNN-PTF. The best PCNN-PTF showed high accuracy (root mean square error (RMSE) = 0.043 cm3 cm−3) and reliability (RMSE = 0.061 cm3 cm−3). When Turkish soil samples were incorporated into the training data set, the performance of the PCNN-PTF was enhanced by 33%. Therefore, to further improve the performance of the PCNN-PTF for new regions, we recommend the incorporation of local soils, when available, into the international data sets and developing new sets of PCNN-PTFs. Full article
(This article belongs to the Special Issue Soft Computing for Water and Aquatic Resource Management)
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