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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Resources and Sustainable Utilization".
Deadline for manuscript submissions: 31 December 2021.
Special Issue Editors
2. Discipline of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Selangor 47500, Malaysia
Interests: hydrological and water quality modeling; artificial neural network; sustainable stormwater management
Interests: artificial intelligence techniques on hydrological process; environmental and water resources; dam and reservoir operation and multi-sensor system integration
Special Issue Information
Dear Colleagues,
Both the rapid urbanization process and climate change have significantly posed greater engineering challenges in the 21st century. The real scenarios in this context are associated with uncertainty and vagueness. As the water and environmental systems become more complex, new approaches and techniques are required to enable the decision-makers and stakeholders to develop sustainable solutions and make decisions based on a wide range of uncertainty and limited information. Application of soft computing techniques to solve complex problems in hydrology and water resources fields have gained increasing attention due to their robustness and inherent tolerance of uncertainty compared to the traditional methods. Soft computing allows spatial and temporal integration of various data of different nature in order to acquire better empirical insights into complex multiparameter problems and, hence, encourage adaptive strategies for holistic water management. Some of the soft computing techniques, such as fuzzy logic, expert systems, artificial neural networks, fuzzy neural networks, and genetic algorithms, have been widely employed either as single or hybrid systems in solving various real-life water and environmental problems. Thus, the aim of this Special Issue is to show recent and novel applications of soft computing techniques in the field of water engineering, especially in various hydrological processes (e.g., rainfall, runoff, etc.) and water quality forecasting. All original research and review contributions within the scope of this Special Issue are highly welcome.
Dr. Chow Ming Fai
Prof. Dr. Ahmed Hussein Kamel Ahmed Elshafie
Dr. Al Mahfoodh Ali Najah Ahmed
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 papers will be 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. Sustainability 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 1900 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
- water resource management
- environmental modeling
- genetic algorithms
- machine learning
- multi-purpose reservoir
- hybrid expert systems
- adaptive neuro fuzzy inference systems (ANFIS)
- sustainable development
- pattern recognition
- optimization algorithm
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A Neuro-Fuzzy Classifier for Evaluating Spatial and Temporal Variations in Water Quality
Authors: Ho Huu Loc
Affiliation: Water Engineering and Management (WEM), Department of Civil and Infrastructure Engineering (CIE), School of Engineering and Technology (SET), Asian Institute of Technology (AIT), P. O. Box 4, 58 Moo 9, Km.42, Paholyothin Highway, Klong Luang, Pathumthani 12120, Thailand
Abstract: Adaptive neuro-fuzzy inference system (ANFIS) is a kind of adaptive network that takes the advantage of both neural networks and fuzzy logic. However, the ANFIS outputs are not an integer, it is not suitable for classifying problems. This study presents the development of a novel neuro-fuzzy classifier (NFC) for evaluating spatial and temporal variations in water quality. The illustration case is concerned with an application in the Song Quao-Ca Giang (SQ-CG) water system, a primary domestic water supply source of the city of Phan Thiet in Binh Thuan province, Vietnam. The developed classifier may overcome the limitation of using ANFIS as a classifier. The results indicated that the proposed approach achieved high accuracy. In order to evaluate the efficiency of the proposed neuro-fuzzy classifier, the results were also compared with those obtained from other well-known classification methods, including support vector machine (SVM), Naive Bayes, artificial neural network (ANN), and decision tree (DT) approach. The comparative analysis showed that the neuro-fuzzy approach performed better than the others. It is expected that this study may be used as an assistance tool for managers and monitors of water quality.